Web image retrieval using an effective topic and content-based technique

NASA Astrophysics Data System (ADS)

Lee, Ching-Cheng; Prabhakara, Rashmi

2005-03-01

There has been an exponential growth in the amount of image data that is available on the World Wide Web since the early development of Internet. With such a large amount of information and image available and its usefulness, an effective image retrieval system is thus greatly needed. In this paper, we present an effective approach with both image matching and indexing techniques that improvise on existing integrated image retrieval methods. This technique follows a two-phase approach, integrating query by topic and query by example specification methods. In the first phase, The topic-based image retrieval is performed by using an improved text information retrieval (IR) technique that makes use of the structured format of HTML documents. This technique consists of a focused crawler that not only provides for the user to enter the keyword for the topic-based search but also, the scope in which the user wants to find the images. In the second phase, we use query by example specification to perform a low-level content-based image match in order to retrieve smaller and relatively closer results of the example image. From this, information related to the image feature is automatically extracted from the query image. The main objective of our approach is to develop a functional image search and indexing technique and to demonstrate that better retrieval results can be achieved.

Image/text automatic indexing and retrieval system using context vector approach

NASA Astrophysics Data System (ADS)

Qing, Kent P.; Caid, William R.; Ren, Clara Z.; McCabe, Patrick

1995-11-01

Thousands of documents and images are generated daily both on and off line on the information superhighway and other media. Storage technology has improved rapidly to handle these data but indexing this information is becoming very costly. HNC Software Inc. has developed a technology for automatic indexing and retrieval of free text and images. This technique is demonstrated and is based on the concept of `context vectors' which encode a succinct representation of the associated text and features of sub-image. In this paper, we will describe the Automated Librarian System which was designed for free text indexing and the Image Content Addressable Retrieval System (ICARS) which extends the technique from the text domain into the image domain. Both systems have the ability to automatically assign indices for a new document and/or image based on the content similarities in the database. ICARS also has the capability to retrieve images based on similarity of content using index terms, text description, and user-generated images as a query without performing segmentation or object recognition.

Content-based video retrieval by example video clip

NASA Astrophysics Data System (ADS)

Dimitrova, Nevenka; Abdel-Mottaleb, Mohamed

1997-01-01

This paper presents a novel approach for video retrieval from a large archive of MPEG or Motion JPEG compressed video clips. We introduce a retrieval algorithm that takes a video clip as a query and searches the database for clips with similar contents. Video clips are characterized by a sequence of representative frame signatures, which are constructed from DC coefficients and motion information (`DC+M' signatures). The similarity between two video clips is determined by using their respective signatures. This method facilitates retrieval of clips for the purpose of video editing, broadcast news retrieval, or copyright violation detection.

Automatic Content Analysis; Part I of Scientific Report No. ISR-18, Information Storage and Retrieval...

ERIC Educational Resources Information Center

Cornell Univ., Ithaca, NY. Dept. of Computer Science.

Four papers are included in Part One of the eighteenth report on Salton's Magical Automatic Retriever of Texts (SMART) project. The first paper: "Content Analysis in Information Retrieval" by S. F. Weiss presents the results of experiments aimed at determining the conditions under which content analysis improves retrieval results as well…

Words, concepts, or both: optimal indexing units for automated information retrieval.

PubMed Central

Hersh, W. R.; Hickam, D. H.; Leone, T. J.

1992-01-01

What is the best way to represent the content of documents in an information retrieval system? This study compares the retrieval effectiveness of five different methods for automated (machine-assigned) indexing using three test collections. The consistently best methods are those that use indexing based on the words that occur in the available text of each document. Methods used to map text into concepts from a controlled vocabulary showed no advantage over the word-based methods. This study also looked at an approach to relevance feedback which showed benefit for both word-based and concept-based methods. PMID:1482951

Comparing the quality of accessing medical literature using content-based visual and textual information retrieval

NASA Astrophysics Data System (ADS)

Müller, Henning; Kalpathy-Cramer, Jayashree; Kahn, Charles E., Jr.; Hersh, William

2009-02-01

Content-based visual information (or image) retrieval (CBIR) has been an extremely active research domain within medical imaging over the past ten years, with the goal of improving the management of visual medical information. Many technical solutions have been proposed, and application scenarios for image retrieval as well as image classification have been set up. However, in contrast to medical information retrieval using textual methods, visual retrieval has only rarely been applied in clinical practice. This is despite the large amount and variety of visual information produced in hospitals every day. This information overload imposes a significant burden upon clinicians, and CBIR technologies have the potential to help the situation. However, in order for CBIR to become an accepted clinical tool, it must demonstrate a higher level of technical maturity than it has to date. Since 2004, the ImageCLEF benchmark has included a task for the comparison of visual information retrieval algorithms for medical applications. In 2005, a task for medical image classification was introduced and both tasks have been run successfully for the past four years. These benchmarks allow an annual comparison of visual retrieval techniques based on the same data sets and the same query tasks, enabling the meaningful comparison of various retrieval techniques. The datasets used from 2004-2007 contained images and annotations from medical teaching files. In 2008, however, the dataset used was made up of 67,000 images (along with their associated figure captions and the full text of their corresponding articles) from two Radiological Society of North America (RSNA) scientific journals. This article describes the results of the medical image retrieval task of the ImageCLEF 2008 evaluation campaign. We compare the retrieval results of both visual and textual information retrieval systems from 15 research groups on the aforementioned data set. The results show clearly that, currently, visual retrieval alone does not achieve the performance necessary for real-world clinical applications. Most of the common visual retrieval techniques have a MAP (Mean Average Precision) of around 2-3%, which is much lower than that achieved using textual retrieval (MAP=29%). Advanced machine learning techniques, together with good training data, have been shown to improve the performance of visual retrieval systems in the past. Multimodal retrieval (basing retrieval on both visual and textual information) can achieve better results than purely visual, but only when carefully applied. In many cases, multimodal retrieval systems performed even worse than purely textual retrieval systems. On the other hand, some multimodal retrieval systems demonstrated significantly increased early precision, which has been shown to be a desirable behavior in real-world systems.

Content-based image retrieval by matching hierarchical attributed region adjacency graphs

NASA Astrophysics Data System (ADS)

Fischer, Benedikt; Thies, Christian J.; Guld, Mark O.; Lehmann, Thomas M.

2004-05-01

Content-based image retrieval requires a formal description of visual information. In medical applications, all relevant biological objects have to be represented by this description. Although color as the primary feature has proven successful in publicly available retrieval systems of general purpose, this description is not applicable to most medical images. Additionally, it has been shown that global features characterizing the whole image do not lead to acceptable results in the medical context or that they are only suitable for specific applications. For a general purpose content-based comparison of medical images, local, i.e. regional features that are collected on multiple scales must be used. A hierarchical attributed region adjacency graph (HARAG) provides such a representation and transfers image comparison to graph matching. However, building a HARAG from an image requires a restriction in size to be computationally feasible while at the same time all visually plausible information must be preserved. For this purpose, mechanisms for the reduction of the graph size are presented. Even with a reduced graph, the problem of graph matching remains NP-complete. In this paper, the Similarity Flooding approach and Hopfield-style neural networks are adapted from the graph matching community to the needs of HARAG comparison. Based on synthetic image material build from simple geometric objects, all visually similar regions were matched accordingly showing the framework's general applicability to content-based image retrieval of medical images.

Integrated approach to multimodal media content analysis

NASA Astrophysics Data System (ADS)

Zhang, Tong; Kuo, C.-C. Jay

1999-12-01

In this work, we present a system for the automatic segmentation, indexing and retrieval of audiovisual data based on the combination of audio, visual and textural content analysis. The video stream is demultiplexed into audio, image and caption components. Then, a semantic segmentation of the audio signal based on audio content analysis is conducted, and each segment is indexed as one of the basic audio types. The image sequence is segmented into shots based on visual information analysis, and keyframes are extracted from each shot. Meanwhile, keywords are detected from the closed caption. Index tables are designed for both linear and non-linear access to the video. It is shown by experiments that the proposed methods for multimodal media content analysis are effective. And that the integrated framework achieves satisfactory results for video information filtering and retrieval.

Retrieval of ice cloud properties using an optimal estimation algorithm and MODIS infrared observations: 1. Forward model, error analysis, and information content

NASA Astrophysics Data System (ADS)

Wang, Chenxi; Platnick, Steven; Zhang, Zhibo; Meyer, Kerry; Yang, Ping

2016-05-01

An optimal estimation (OE) retrieval method is developed to infer three ice cloud properties simultaneously: optical thickness (τ), effective radius (reff), and cloud top height (h). This method is based on a fast radiative transfer (RT) model and infrared (IR) observations from the MODerate resolution Imaging Spectroradiometer (MODIS). This study conducts thorough error and information content analyses to understand the error propagation and performance of retrievals from various MODIS band combinations under different cloud/atmosphere states. Specifically, the algorithm takes into account four error sources: measurement uncertainty, fast RT model uncertainty, uncertainties in ancillary data sets (e.g., atmospheric state), and assumed ice crystal habit uncertainties. It is found that the ancillary and ice crystal habit error sources dominate the MODIS IR retrieval uncertainty and cannot be ignored. The information content analysis shows that for a given ice cloud, the use of four MODIS IR observations is sufficient to retrieve the three cloud properties. However, the selection of MODIS IR bands that provide the most information and their order of importance varies with both the ice cloud properties and the ambient atmospheric and the surface states. As a result, this study suggests the inclusion of all MODIS IR bands in practice since little a priori information is available.

Retrieval of ice cloud properties using an optimal estimation algorithm and MODIS infrared observations. Part I: Forward model, error analysis, and information content.

PubMed

Wang, Chenxi; Platnick, Steven; Zhang, Zhibo; Meyer, Kerry; Yang, Ping

2016-05-27

An optimal estimation (OE) retrieval method is developed to infer three ice cloud properties simultaneously: optical thickness ( τ ), effective radius ( r eff ), and cloud-top height ( h ). This method is based on a fast radiative transfer (RT) model and infrared (IR) observations from the MODerate resolution Imaging Spectroradiometer (MODIS). This study conducts thorough error and information content analyses to understand the error propagation and performance of retrievals from various MODIS band combinations under different cloud/atmosphere states. Specifically, the algorithm takes into account four error sources: measurement uncertainty, fast RT model uncertainty, uncertainties in ancillary datasets (e.g., atmospheric state), and assumed ice crystal habit uncertainties. It is found that the ancillary and ice crystal habit error sources dominate the MODIS IR retrieval uncertainty and cannot be ignored. The information content analysis shows that, for a given ice cloud, the use of four MODIS IR observations is sufficient to retrieve the three cloud properties. However, the selection of MODIS IR bands that provide the most information and their order of importance varies with both the ice cloud properties and the ambient atmospheric and the surface states. As a result, this study suggests the inclusion of all MODIS IR bands in practice since little a priori information is available.

Enabling search over encrypted multimedia databases

NASA Astrophysics Data System (ADS)

Lu, Wenjun; Swaminathan, Ashwin; Varna, Avinash L.; Wu, Min

2009-02-01

Performing information retrieval tasks while preserving data confidentiality is a desirable capability when a database is stored on a server maintained by a third-party service provider. This paper addresses the problem of enabling content-based retrieval over encrypted multimedia databases. Search indexes, along with multimedia documents, are first encrypted by the content owner and then stored onto the server. Through jointly applying cryptographic techniques, such as order preserving encryption and randomized hash functions, with image processing and information retrieval techniques, secure indexing schemes are designed to provide both privacy protection and rank-ordered search capability. Retrieval results on an encrypted color image database and security analysis of the secure indexing schemes under different attack models show that data confidentiality can be preserved while retaining very good retrieval performance. This work has promising applications in secure multimedia management.

Information content of thermal infrared a microwave bands for simultaneous retrieval of cirrus ice water path and particle effective diameter

NASA Astrophysics Data System (ADS)

Bell, A.; Tang, G.; Yang, P.; Wu, D.

2017-12-01

Due to their high spatial and temporal coverage, cirrus clouds have a profound role in regulating the Earth's energy budget. Variability of their radiative, geometric, and microphysical properties can pose significant uncertainties in global climate model simulations if not adequately constrained. Thus, the development of retrieval methodologies able to accurately retrieve ice cloud properties and present associated uncertainties is essential. The effectiveness of cirrus cloud retrievals relies on accurate a priori understanding of ice radiative properties, as well as the current state of the atmosphere. Current studies have implemented information content theory analyses prior to retrievals to quantify the amount of information that should be expected on parameters to be retrieved, as well as the relative contribution of information provided by certain measurement channels. Through this analysis, retrieval algorithms can be designed in a way to maximize the information in measurements, and therefore ensure enough information is present to retrieve ice cloud properties. In this study, we present such an information content analysis to quantify the amount of information to be expected in retrievals of cirrus ice water path and particle effective diameter using sub-millimeter and thermal infrared radiometry. Preliminary results show these bands to be sensitive to changes in ice water path and effective diameter, and thus lend confidence their ability to simultaneously retrieve these parameters. Further quantification of sensitivity and the information provided from these bands can then be used to design and optimal retrieval scheme. While this information content analysis is employed on a theoretical retrieval combining simulated radiance measurements, the methodology could in general be applicable to any instrument or retrieval approach.

Information content and sensitivity of the 3β + 2α lidar measurement system for aerosol microphysical retrievals

NASA Astrophysics Data System (ADS)

Burton, Sharon P.; Chemyakin, Eduard; Liu, Xu; Knobelspiesse, Kirk; Stamnes, Snorre; Sawamura, Patricia; Moore, Richard H.; Hostetler, Chris A.; Ferrare, Richard A.

2016-11-01

There is considerable interest in retrieving profiles of aerosol effective radius, total number concentration, and complex refractive index from lidar measurements of extinction and backscatter at several wavelengths. The combination of three backscatter channels plus two extinction channels (3β + 2α) is particularly important since it is believed to be the minimum configuration necessary for the retrieval of aerosol microphysical properties and because the technological readiness of lidar systems permits this configuration on both an airborne and future spaceborne instrument. The second-generation NASA Langley airborne High Spectral Resolution Lidar (HSRL-2) has been making 3β + 2α measurements since 2012. The planned NASA Aerosol/Clouds/Ecosystems (ACE) satellite mission also recommends the 3β + 2α combination.Here we develop a deeper understanding of the information content and sensitivities of the 3β + 2α system in terms of aerosol microphysical parameters of interest. We use a retrieval-free methodology to determine the basic sensitivities of the measurements independent of retrieval assumptions and constraints. We calculate information content and uncertainty metrics using tools borrowed from the optimal estimation methodology based on Bayes' theorem, using a simplified forward model look-up table, with no explicit inversion. The forward model is simplified to represent spherical particles, monomodal log-normal size distributions, and wavelength-independent refractive indices. Since we only use the forward model with no retrieval, the given simplified aerosol scenario is applicable as a best case for all existing retrievals in the absence of additional constraints. Retrieval-dependent errors due to mismatch between retrieval assumptions and true atmospheric aerosols are not included in this sensitivity study, and neither are retrieval errors that may be introduced in the inversion process. The choice of a simplified model adds clarity to the understanding of the uncertainties in such retrievals, since it allows for separately assessing the sensitivities and uncertainties of the measurements alone that cannot be corrected by any potential or theoretical improvements to retrieval methodology but must instead be addressed by adding information content.The sensitivity metrics allow for identifying (1) information content of the measurements vs. a priori information; (2) error bars on the retrieved parameters; and (3) potential sources of cross-talk or "compensating" errors wherein different retrieval parameters are not independently captured by the measurements. The results suggest that the 3β + 2α measurement system is underdetermined with respect to the full suite of microphysical parameters considered in this study and that additional information is required, in the form of additional coincident measurements (e.g., sun-photometer or polarimeter) or a priori retrieval constraints. A specific recommendation is given for addressing cross-talk between effective radius and total number concentration.

A novel methodology for querying web images

NASA Astrophysics Data System (ADS)

Prabhakara, Rashmi; Lee, Ching Cheng

2005-01-01

Ever since the advent of Internet, there has been an immense growth in the amount of image data that is available on the World Wide Web. With such a magnitude of image availability, an efficient and effective image retrieval system is required to make use of this information. This research presents an effective image matching and indexing technique that improvises on existing integrated image retrieval methods. The proposed technique follows a two-phase approach, integrating query by topic and query by example specification methods. The first phase consists of topic-based image retrieval using an improved text information retrieval (IR) technique that makes use of the structured format of HTML documents. It consists of a focused crawler that not only provides for the user to enter the keyword for the topic-based search but also, the scope in which the user wants to find the images. The second phase uses the query by example specification to perform a low-level content-based image match for the retrieval of smaller and relatively closer results of the example image. Information related to the image feature is automatically extracted from the query image by the image processing system. A technique that is not computationally intensive based on color feature is used to perform content-based matching of images. The main goal is to develop a functional image search and indexing system and to demonstrate that better retrieval results can be achieved with this proposed hybrid search technique.

A novel methodology for querying web images

NASA Astrophysics Data System (ADS)

Prabhakara, Rashmi; Lee, Ching Cheng

2004-12-01

Ever since the advent of Internet, there has been an immense growth in the amount of image data that is available on the World Wide Web. With such a magnitude of image availability, an efficient and effective image retrieval system is required to make use of this information. This research presents an effective image matching and indexing technique that improvises on existing integrated image retrieval methods. The proposed technique follows a two-phase approach, integrating query by topic and query by example specification methods. The first phase consists of topic-based image retrieval using an improved text information retrieval (IR) technique that makes use of the structured format of HTML documents. It consists of a focused crawler that not only provides for the user to enter the keyword for the topic-based search but also, the scope in which the user wants to find the images. The second phase uses the query by example specification to perform a low-level content-based image match for the retrieval of smaller and relatively closer results of the example image. Information related to the image feature is automatically extracted from the query image by the image processing system. A technique that is not computationally intensive based on color feature is used to perform content-based matching of images. The main goal is to develop a functional image search and indexing system and to demonstrate that better retrieval results can be achieved with this proposed hybrid search technique.

Information content of OCO-2 oxygen A-band channels for retrieving marine liquid cloud properties

NASA Astrophysics Data System (ADS)

Richardson, Mark; Stephens, Graeme L.

2018-03-01

Information content analysis is used to select channels for a marine liquid cloud retrieval using the high-spectral-resolution oxygen A-band instrument on NASA's Orbiting Carbon Observatory-2 (OCO-2). Desired retrieval properties are cloud optical depth, cloud-top pressure and cloud pressure thickness, which is the geometric thickness expressed in hectopascals. Based on information content criteria we select a micro-window of 75 of the 853 functioning OCO-2 channels spanning 763.5-764.6 nm and perform a series of synthetic retrievals with perturbed initial conditions. We estimate posterior errors from the sample standard deviations and obtain ±0.75 in optical depth and ±12.9 hPa in both cloud-top pressure and cloud pressure thickness, although removing the 10 % of samples with the highest χ2 reduces posterior error in cloud-top pressure to ±2.9 hPa and cloud pressure thickness to ±2.5 hPa. The application of this retrieval to real OCO-2 measurements is briefly discussed, along with limitations and the greatest caution is urged regarding the assumption of a single homogeneous cloud layer, which is often, but not always, a reasonable approximation for marine boundary layer clouds.

Interactions among emotional attention, encoding, and retrieval of ambiguous information: An eye-tracking study.

PubMed

Everaert, Jonas; Koster, Ernst H W

2015-10-01

Emotional biases in attention modulate encoding of emotional material into long-term memory, but little is known about the role of such attentional biases during emotional memory retrieval. The present study investigated how emotional biases in memory are related to attentional allocation during retrieval. Forty-nine individuals encoded emotionally positive and negative meanings derived from ambiguous information and then searched their memory for encoded meanings in response to a set of retrieval cues. The remember/know/new procedure was used to classify memories as recollection-based or familiarity-based, and gaze behavior was monitored throughout the task to measure attentional allocation. We found that a bias in sustained attention during recollection-based, but not familiarity-based, retrieval predicted subsequent memory bias toward positive versus negative material following encoding. Thus, during emotional memory retrieval, attention affects controlled forms of retrieval (i.e., recollection) but does not modulate relatively automatic, familiarity-based retrieval. These findings enhance understanding of how distinct components of attention regulate the emotional content of memories. Implications for theoretical models and emotion regulation are discussed. (c) 2015 APA, all rights reserved).

Retrieval of ice cloud properties using an optimal estimation algorithm and MODIS infrared observations. Part I: Forward model, error analysis, and information content

PubMed Central

Wang, Chenxi; Platnick, Steven; Zhang, Zhibo; Meyer, Kerry; Yang, Ping

2018-01-01

An optimal estimation (OE) retrieval method is developed to infer three ice cloud properties simultaneously: optical thickness (τ), effective radius (reff), and cloud-top height (h). This method is based on a fast radiative transfer (RT) model and infrared (IR) observations from the MODerate resolution Imaging Spectroradiometer (MODIS). This study conducts thorough error and information content analyses to understand the error propagation and performance of retrievals from various MODIS band combinations under different cloud/atmosphere states. Specifically, the algorithm takes into account four error sources: measurement uncertainty, fast RT model uncertainty, uncertainties in ancillary datasets (e.g., atmospheric state), and assumed ice crystal habit uncertainties. It is found that the ancillary and ice crystal habit error sources dominate the MODIS IR retrieval uncertainty and cannot be ignored. The information content analysis shows that, for a given ice cloud, the use of four MODIS IR observations is sufficient to retrieve the three cloud properties. However, the selection of MODIS IR bands that provide the most information and their order of importance varies with both the ice cloud properties and the ambient atmospheric and the surface states. As a result, this study suggests the inclusion of all MODIS IR bands in practice since little a priori information is available. PMID:29707470

Retrieval of Ice Cloud Properties Using an Optimal Estimation Algorithm and MODIS Infrared Observations. Part I: Forward Model, Error Analysis, and Information Content

NASA Technical Reports Server (NTRS)

Wang, Chenxi; Platnick, Steven; Zhang, Zhibo; Meyer, Kerry; Yang, Ping

2016-01-01

An optimal estimation (OE) retrieval method is developed to infer three ice cloud properties simultaneously: optical thickness (tau), effective radius (r(sub eff)), and cloud-top height (h). This method is based on a fast radiative transfer (RT) model and infrared (IR) observations from the MODerate resolution Imaging Spectroradiometer (MODIS). This study conducts thorough error and information content analyses to understand the error propagation and performance of retrievals from various MODIS band combinations under different cloud/atmosphere states. Specifically, the algorithm takes into account four error sources: measurement uncertainty, fast RT model uncertainty, uncertainties in ancillary datasets (e.g., atmospheric state), and assumed ice crystal habit uncertainties. It is found that the ancillary and ice crystal habit error sources dominate the MODIS IR retrieval uncertainty and cannot be ignored. The information content analysis shows that, for a given ice cloud, the use of four MODIS IR observations is sufficient to retrieve the three cloud properties. However, the selection of MODIS IR bands that provide the most information and their order of importance varies with both the ice cloud properties and the ambient atmospheric and the surface states. As a result, this study suggests the inclusion of all MODIS IR bands in practice since little a priori information is available.

Retrieval of Ice Cloud Properties Using an Optimal Estimation Algorithm and MODIS Infrared Observations. Part I: Forward Model, Error Analysis, and Information Content

NASA Technical Reports Server (NTRS)

Wang, Chenxi; Platnick, Steven; Zhang, Zhibo; Meyer, Kerry; Yang, Ping

2016-01-01

An optimal estimation (OE) retrieval method is developed to infer three ice cloud properties simultaneously: optical thickness (tau), effective radius (r(sub eff)), and cloud top height (h). This method is based on a fast radiative transfer (RT) model and infrared (IR) observations from the MODerate resolution Imaging Spectroradiometer (MODIS). This study conducts thorough error and information content analyses to understand the error propagation and performance of retrievals from various MODIS band combinations under different cloud/atmosphere states. Specifically, the algorithm takes into account four error sources: measurement uncertainty, fast RT model uncertainty, uncertainties in ancillary data sets (e.g., atmospheric state), and assumed ice crystal habit uncertainties. It is found that the ancillary and ice crystal habit error sources dominate the MODIS IR retrieval uncertainty and cannot be ignored. The information content analysis shows that for a given ice cloud, the use of four MODIS IR observations is sufficient to retrieve the three cloud properties. However, the selection of MODIS IR bands that provide the most information and their order of importance varies with both the ice cloud properties and the ambient atmospheric and the surface states. As a result, this study suggests the inclusion of all MODIS IR bands in practice since little a priori information is available.

Content based image retrieval for matching images of improvised explosive devices in which snake initialization is viewed as an inverse problem

NASA Astrophysics Data System (ADS)

Acton, Scott T.; Gilliam, Andrew D.; Li, Bing; Rossi, Adam

2008-02-01

Improvised explosive devices (IEDs) are common and lethal instruments of terrorism, and linking a terrorist entity to a specific device remains a difficult task. In the effort to identify persons associated with a given IED, we have implemented a specialized content based image retrieval system to search and classify IED imagery. The system makes two contributions to the art. First, we introduce a shape-based matching technique exploiting shape, color, and texture (wavelet) information, based on novel vector field convolution active contours and a novel active contour initialization method which treats coarse segmentation as an inverse problem. Second, we introduce a unique graph theoretic approach to match annotated printed circuit board images for which no schematic or connectivity information is available. The shape-based image retrieval method, in conjunction with the graph theoretic tool, provides an efficacious system for matching IED images. For circuit imagery, the basic retrieval mechanism has a precision of 82.1% and the graph based method has a precision of 98.1%. As of the fall of 2007, the working system has processed over 400,000 case images.

Content-based management service for medical videos.

PubMed

Mendi, Engin; Bayrak, Coskun; Cecen, Songul; Ermisoglu, Emre

2013-01-01

Development of health information technology has had a dramatic impact to improve the efficiency and quality of medical care. Developing interoperable health information systems for healthcare providers has the potential to improve the quality and equitability of patient-centered healthcare. In this article, we describe an automated content-based medical video analysis and management service that provides convenience and ease in accessing the relevant medical video content without sequential scanning. The system facilitates effective temporal video segmentation and content-based visual information retrieval that enable a more reliable understanding of medical video content. The system is implemented as a Web- and mobile-based service and has the potential to offer a knowledge-sharing platform for the purpose of efficient medical video content access.

Exploring context and content links in social media: a latent space method.

PubMed

Qi, Guo-Jun; Aggarwal, Charu; Tian, Qi; Ji, Heng; Huang, Thomas S

2012-05-01

Social media networks contain both content and context-specific information. Most existing methods work with either of the two for the purpose of multimedia mining and retrieval. In reality, both content and context information are rich sources of information for mining, and the full power of mining and processing algorithms can be realized only with the use of a combination of the two. This paper proposes a new algorithm which mines both context and content links in social media networks to discover the underlying latent semantic space. This mapping of the multimedia objects into latent feature vectors enables the use of any off-the-shelf multimedia retrieval algorithms. Compared to the state-of-the-art latent methods in multimedia analysis, this algorithm effectively solves the problem of sparse context links by mining the geometric structure underlying the content links between multimedia objects. Specifically for multimedia annotation, we show that an effective algorithm can be developed to directly construct annotation models by simultaneously leveraging both context and content information based on latent structure between correlated semantic concepts. We conduct experiments on the Flickr data set, which contains user tags linked with images. We illustrate the advantages of our approach over the state-of-the-art multimedia retrieval techniques.

Evolving discriminators for querying video sequences

NASA Astrophysics Data System (ADS)

Iyengar, Giridharan; Lippman, Andrew B.

1997-01-01

In this paper we present a framework for content based query and retrieval of information from large video databases. This framework enables content based retrieval of video sequences by characterizing the sequences using motion, texture and colorimetry cues. This characterization is biologically inspired and results in a compact parameter space where every segment of video is represented by an 8 dimensional vector. Searching and retrieval is done in real- time with accuracy in this parameter space. Using this characterization, we then evolve a set of discriminators using Genetic Programming Experiments indicate that these discriminators are capable of analyzing and characterizing video. The VideoBook is able to search and retrieve video sequences with 92% accuracy in real-time. Experiments thus demonstrate that the characterization is capable of extracting higher level structure from raw pixel values.

Mobile object retrieval in server-based image databases

NASA Astrophysics Data System (ADS)

Manger, D.; Pagel, F.; Widak, H.

2013-05-01

The increasing number of mobile phones equipped with powerful cameras leads to huge collections of user-generated images. To utilize the information of the images on site, image retrieval systems are becoming more and more popular to search for similar objects in an own image database. As the computational performance and the memory capacity of mobile devices are constantly increasing, this search can often be performed on the device itself. This is feasible, for example, if the images are represented with global image features or if the search is done using EXIF or textual metadata. However, for larger image databases, if multiple users are meant to contribute to a growing image database or if powerful content-based image retrieval methods with local features are required, a server-based image retrieval backend is needed. In this work, we present a content-based image retrieval system with a client server architecture working with local features. On the server side, the scalability to large image databases is addressed with the popular bag-of-word model with state-of-the-art extensions. The client end of the system focuses on a lightweight user interface presenting the most similar images of the database highlighting the visual information which is common with the query image. Additionally, new images can be added to the database making it a powerful and interactive tool for mobile contentbased image retrieval.

Medical Image Retrieval: A Multimodal Approach

PubMed Central

Cao, Yu; Steffey, Shawn; He, Jianbiao; Xiao, Degui; Tao, Cui; Chen, Ping; Müller, Henning

2014-01-01

Medical imaging is becoming a vital component of war on cancer. Tremendous amounts of medical image data are captured and recorded in a digital format during cancer care and cancer research. Facing such an unprecedented volume of image data with heterogeneous image modalities, it is necessary to develop effective and efficient content-based medical image retrieval systems for cancer clinical practice and research. While substantial progress has been made in different areas of content-based image retrieval (CBIR) research, direct applications of existing CBIR techniques to the medical images produced unsatisfactory results, because of the unique characteristics of medical images. In this paper, we develop a new multimodal medical image retrieval approach based on the recent advances in the statistical graphic model and deep learning. Specifically, we first investigate a new extended probabilistic Latent Semantic Analysis model to integrate the visual and textual information from medical images to bridge the semantic gap. We then develop a new deep Boltzmann machine-based multimodal learning model to learn the joint density model from multimodal information in order to derive the missing modality. Experimental results with large volume of real-world medical images have shown that our new approach is a promising solution for the next-generation medical imaging indexing and retrieval system. PMID:26309389

HealthTrust: a social network approach for retrieving online health videos.

PubMed

Fernandez-Luque, Luis; Karlsen, Randi; Melton, Genevieve B

2012-01-31

Social media are becoming mainstream in the health domain. Despite the large volume of accurate and trustworthy health information available on social media platforms, finding good-quality health information can be difficult. Misleading health information can often be popular (eg, antivaccination videos) and therefore highly rated by general search engines. We believe that community wisdom about the quality of health information can be harnessed to help create tools for retrieving good-quality social media content. To explore approaches for extracting metrics about authoritativeness in online health communities and how these metrics positively correlate with the quality of the content. We designed a metric, called HealthTrust, that estimates the trustworthiness of social media content (eg, blog posts or videos) in a health community. The HealthTrust metric calculates reputation in an online health community based on link analysis. We used the metric to retrieve YouTube videos and channels about diabetes. In two different experiments, health consumers provided 427 ratings of 17 videos and professionals gave 162 ratings of 23 videos. In addition, two professionals reviewed 30 diabetes channels. HealthTrust may be used for retrieving online videos on diabetes, since it performed better than YouTube Search in most cases. Overall, of 20 potential channels, HealthTrust's filtering allowed only 3 bad channels (15%) versus 8 (40%) on the YouTube list. Misleading and graphic videos (eg, featuring amputations) were more commonly found by YouTube Search than by searches based on HealthTrust. However, some videos from trusted sources had low HealthTrust scores, mostly from general health content providers, and therefore not highly connected in the diabetes community. When comparing video ratings from our reviewers, we found that HealthTrust achieved a positive and statistically significant correlation with professionals (Pearson r₁₀ = .65, P = .02) and a trend toward significance with health consumers (r₇ = .65, P = .06) with videos on hemoglobinA(1c), but it did not perform as well with diabetic foot videos. The trust-based metric HealthTrust showed promising results when used to retrieve diabetes content from YouTube. Our research indicates that social network analysis may be used to identify trustworthy social media in health communities.

Efficient Caption-Based Retrieval of Multimedia Information

DTIC Science & Technology

1993-10-09

in the design of transportable natural language interfaces. Artifcial Intelligence , 32 (1987), 173-243. - 13- (101 Jones, M. and Eisner, J. A...systems for multimedia data . They exploit captions on the data and perform natural-language processing of them and English retrieval requests. Some...content analysis of the data is also performed to obtain additional descriptive information. The key to getting this approach to work is sufficiently

Value-Based Caching in Information-Centric Wireless Body Area Networks

PubMed Central

Al-Turjman, Fadi M.; Imran, Muhammad; Vasilakos, Athanasios V.

2017-01-01

We propose a resilient cache replacement approach based on a Value of sensed Information (VoI) policy. To resolve and fetch content when the origin is not available due to isolated in-network nodes (fragmentation) and harsh operational conditions, we exploit a content caching approach. Our approach depends on four functional parameters in sensory Wireless Body Area Networks (WBANs). These four parameters are: age of data based on periodic request, popularity of on-demand requests, communication interference cost, and the duration for which the sensor node is required to operate in active mode to capture the sensed readings. These parameters are considered together to assign a value to the cached data to retain the most valuable information in the cache for prolonged time periods. The higher the value, the longer the duration for which the data will be retained in the cache. This caching strategy provides significant availability for most valuable and difficult to retrieve data in the WBANs. Extensive simulations are performed to compare the proposed scheme against other significant caching schemes in the literature while varying critical aspects in WBANs (e.g., data popularity, cache size, publisher load, connectivity-degree, and severe probabilities of node failures). These simulation results indicate that the proposed VoI-based approach is a valid tool for the retrieval of cached content in disruptive and challenging scenarios, such as the one experienced in WBANs, since it allows the retrieval of content for a long period even while experiencing severe in-network node failures. PMID:28106817

Creating and indexing teaching files from free-text patient reports.

PubMed Central

Johnson, D. B.; Chu, W. W.; Dionisio, J. D.; Taira, R. K.; Kangarloo, H.

1999-01-01

Teaching files based on real patient data can enhance the education of students, staff and other colleagues. Although information retrieval system can index free-text documents using keywords, these systems do not work well where content bearing terms (e.g., anatomy descriptions) frequently appears. This paper describes a system that uses multi-word indexing terms to provide access to free-text patient reports. The utilization of multi-word indexing allows better modeling of the content of medical reports, thus improving retrieval performance. The method used to select indexing terms as well as early evaluation of retrieval performance is discussed. PMID:10566473

The Comprehensive Microbial Resource.

PubMed

Peterson, J D; Umayam, L A; Dickinson, T; Hickey, E K; White, O

2001-01-01

One challenge presented by large-scale genome sequencing efforts is effective display of uniform information to the scientific community. The Comprehensive Microbial Resource (CMR) contains robust annotation of all complete microbial genomes and allows for a wide variety of data retrievals. The bacterial information has been placed on the Web at http://www.tigr.org/CMR for retrieval using standard web browsing technology. Retrievals can be based on protein properties such as molecular weight or hydrophobicity, GC-content, functional role assignments and taxonomy. The CMR also has special web-based tools to allow data mining using pre-run homology searches, whole genome dot-plots, batch downloading and traversal across genomes using a variety of datatypes.

Beyond procedure's content: the role of accessibility experiences and personal uncertainty in procedural justice judgements.

PubMed

Greifeneder, Rainer; Müller, Patrick; Stahlberg, Dagmar; Van den Bos, Kees; Bless, Herbert

2011-01-01

Procedural justice concerns play a critical role in economic settings, politics, and other domains of human life. Despite the vast evidence corroborating their relevance, considerably less is known about how procedural justice judgments are formed. Whereas earlier theorizing focused on the systematic integration of content information, the present contribution provides a new perspective on the formation of justice judgments by examining the influence of accessibility experiences. Specifically, we hypothesize that procedural justice judgments may be formed based on the ease or difficulty with which justice-relevant information comes to mind. Three experiments corroborate this prediction in that procedures were evaluated less positively when the retrieval of associated unfair aspects was easy compared to difficult. Presumably this is because when it feels easy (difficult) to retrieve unfair aspects, these are perceived as frequent (infrequent), and hence the procedure as unjust (just). In addition to demonstrating that ease-of-retrieval may influence justice judgments, the studies further revealed that reliance on accessibility experiences is high in conditions of personal certainty. We suggest that this is because personal uncertainty fosters systematic processing of content information, whereas personal certainty may invite less taxing judgmental strategies such as reliance on ease-of-retrieval.

Developing a comprehensive system for content-based retrieval of image and text data from a national survey

NASA Astrophysics Data System (ADS)

Antani, Sameer K.; Natarajan, Mukil; Long, Jonathan L.; Long, L. Rodney; Thoma, George R.

2005-04-01

The article describes the status of our ongoing R&D at the U.S. National Library of Medicine (NLM) towards the development of an advanced multimedia database biomedical information system that supports content-based image retrieval (CBIR). NLM maintains a collection of 17,000 digitized spinal X-rays along with text survey data from the Second National Health and Nutritional Examination Survey (NHANES II). These data serve as a rich data source for epidemiologists and researchers of osteoarthritis and musculoskeletal diseases. It is currently possible to access these through text keyword queries using our Web-based Medical Information Retrieval System (WebMIRS). CBIR methods developed specifically for biomedical images could offer direct visual searching of these images by means of example image or user sketch. We are building a system which supports hybrid queries that have text and image-content components. R&D goals include developing algorithms for robust image segmentation for localizing and identifying relevant anatomy, labeling the segmented anatomy based on its pathology, developing suitable indexing and similarity matching methods for images and image features, and associating the survey text information for query and retrieval along with the image data. Some highlights of the system developed in MATLAB and Java are: use of a networked or local centralized database for text and image data; flexibility to incorporate new research work; provides a means to control access to system components under development; and use of XML for structured reporting. The article details the design, features, and algorithms in this third revision of this prototype system, CBIR3.

Combining textual and visual information for image retrieval in the medical domain.

PubMed

Gkoufas, Yiannis; Morou, Anna; Kalamboukis, Theodore

2011-01-01

In this article we have assembled the experience obtained from our participation in the imageCLEF evaluation task over the past two years. Exploitation on the use of linear combinations for image retrieval has been attempted by combining visual and textual sources of images. From our experiments we conclude that a mixed retrieval technique that applies both textual and visual retrieval in an interchangeably repeated manner improves the performance while overcoming the scalability limitations of visual retrieval. In particular, the mean average precision (MAP) has increased from 0.01 to 0.15 and 0.087 for 2009 and 2010 data, respectively, when content-based image retrieval (CBIR) is performed on the top 1000 results from textual retrieval based on natural language processing (NLP).

Content-based retrieval using MPEG-7 visual descriptor and hippocampal neural network

NASA Astrophysics Data System (ADS)

Kim, Young Ho; Joung, Lyang-Jae; Kang, Dae-Seong

2005-12-01

As development of digital technology, many kinds of multimedia data are used variously and requirements for effective use by user are increasing. In order to transfer information fast and precisely what user wants, effective retrieval method is required. As existing multimedia data are impossible to apply the MPEG-1, MPEG-2 and MPEG-4 technologies which are aimed at compression, store and transmission. So MPEG-7 is introduced as a new technology for effective management and retrieval for multimedia data. In this paper, we extract content-based features using color descriptor among the MPEG-7 standardization visual descriptor, and reduce feature data applying PCA(Principal Components Analysis) technique. We remodel the cerebral cortex and hippocampal neural networks as a principle of a human's brain and it can label the features of the image-data which are inputted according to the order of hippocampal neuron structure to reaction-pattern according to the adjustment of a good impression in Dentate gyrus region and remove the noise through the auto-associate- memory step in the CA3 region. In the CA1 region receiving the information of the CA3, it can make long-term or short-term memory learned by neuron. Hippocampal neural network makes neuron of the neural network separate and combine dynamically, expand the neuron attaching additional information using the synapse and add new features according to the situation by user's demand. When user is querying, it compares feature value stored in long-term memory first and it learns feature vector fast and construct optimized feature. So the speed of index and retrieval is fast. Also, it uses MPEG-7 standard visual descriptors as content-based feature value, it improves retrieval efficiency.

The semantic representation of event information depends on the cue modality: an instance of meaning-based retrieval.

PubMed

Karlsson, Kristina; Sikström, Sverker; Willander, Johan

2013-01-01

The semantic content, or the meaning, is the essence of autobiographical memories. In comparison to previous research, which has mainly focused on the phenomenological experience and the age distribution of retrieved events, the present study provides a novel view on the retrieval of event information by quantifying the information as semantic representations. We investigated the semantic representation of sensory cued autobiographical events and studied the modality hierarchy within the multimodal retrieval cues. The experiment comprised a cued recall task, where the participants were presented with visual, auditory, olfactory or multimodal retrieval cues and asked to recall autobiographical events. The results indicated that the three different unimodal retrieval cues generate significantly different semantic representations. Further, the auditory and the visual modalities contributed the most to the semantic representation of the multimodally retrieved events. Finally, the semantic representation of the multimodal condition could be described as a combination of the three unimodal conditions. In conclusion, these results suggest that the meaning of the retrieved event information depends on the modality of the retrieval cues.

The Semantic Representation of Event Information Depends on the Cue Modality: An Instance of Meaning-Based Retrieval

PubMed Central

Karlsson, Kristina; Sikström, Sverker; Willander, Johan

2013-01-01

The semantic content, or the meaning, is the essence of autobiographical memories. In comparison to previous research, which has mainly focused on the phenomenological experience and the age distribution of retrieved events, the present study provides a novel view on the retrieval of event information by quantifying the information as semantic representations. We investigated the semantic representation of sensory cued autobiographical events and studied the modality hierarchy within the multimodal retrieval cues. The experiment comprised a cued recall task, where the participants were presented with visual, auditory, olfactory or multimodal retrieval cues and asked to recall autobiographical events. The results indicated that the three different unimodal retrieval cues generate significantly different semantic representations. Further, the auditory and the visual modalities contributed the most to the semantic representation of the multimodally retrieved events. Finally, the semantic representation of the multimodal condition could be described as a combination of the three unimodal conditions. In conclusion, these results suggest that the meaning of the retrieved event information depends on the modality of the retrieval cues. PMID:24204561

Understanding human quality judgment in assessing online forum contents for thread retrieval purpose

NASA Astrophysics Data System (ADS)

Ismail, Zuriati; Salim, Naomie; Huspi, Sharin Hazlin

2017-10-01

Compared to traditional materials or journals, user-generated contents are not peer-reviewed. Lack of quality control and the explosive growth of web contents make the task of finding quality information on the web especially critical. The existence of new facilities for producing web contents such as forum makes this issue more significant. This study focuses on online forums threads or discussion, where the forums contain valuable human-generated information in a form of discussions. Due to the unique structure of the online forum pages, special techniques are required to organize and search for information in these forums. Quality biased retrieval is a retrieval approach that search for relevant document and prioritized higher quality documents. Despite major concern of quality content and recent development of quality biased retrieval, there is an urgent need to understand how quality content is being judged, for retrieval and performance evaluation purposes. Furthermore, even though there are various studies on the quality of information, there is no standard framework that has been established. The primary aim of this paper is to contribute to the understanding of human quality judgment in assessing online forum contents. The foundation of this study is to compare and evaluate different frameworks (for quality biased retrieval and information quality). This led to the finding that many quality dimensions are redundant and some dimensions are understood differently between different studies. We conducted a survey on crowdsourcing community to measure the importance of each quality dimensions found in various frameworks. Accuracy and ease of understanding are among top important dimensions while threads popularity and contents manipulability are among least important dimensions. This finding is beneficial in evaluating contents of online forum.

Informative Top-k Retrieval for Advanced Skill Management

NASA Astrophysics Data System (ADS)

Colucci, Simona; di Noia, Tommaso; Ragone, Azzurra; Ruta, Michele; Straccia, Umberto; Tinelli, Eufemia

The paper presents a knowledge-based framework for skills and talent management based on an advanced matchmaking between profiles of candidates and available job positions. Interestingly, informative content of top-k retrieval is enriched through semantic capabilities. The proposed approach allows to: (1) express a requested profile in terms of both hard constraints and soft ones; (2) provide a ranking function based also on qualitative attributes of a profile; (3) explain the resulting outcomes (given a job request, a motivation for the obtained score of each selected profile is provided). Top-k retrieval allows to select most promising candidates according to an ontology formalizing the domain knowledge. Such a knowledge is further exploited to provide a semantic-based explanation of missing or conflicting features in retrieved profiles. They also indicate additional profile characteristics emerging by the retrieval procedure for a further request refinement. A concrete case study followed by an exhaustive experimental campaign is reported to prove the approach effectiveness.

Image Location Estimation by Salient Region Matching.

PubMed

Qian, Xueming; Zhao, Yisi; Han, Junwei

2015-11-01

Nowadays, locations of images have been widely used in many application scenarios for large geo-tagged image corpora. As to images which are not geographically tagged, we estimate their locations with the help of the large geo-tagged image set by content-based image retrieval. In this paper, we exploit spatial information of useful visual words to improve image location estimation (or content-based image retrieval performances). We proposed to generate visual word groups by mean-shift clustering. To improve the retrieval performance, spatial constraint is utilized to code the relative position of visual words. We proposed to generate a position descriptor for each visual word and build fast indexing structure for visual word groups. Experiments show the effectiveness of our proposed approach.

Content-based cell pathology image retrieval by combining different features

NASA Astrophysics Data System (ADS)

Zhou, Guangquan; Jiang, Lu; Luo, Limin; Bao, Xudong; Shu, Huazhong

2004-04-01

Content Based Color Cell Pathology Image Retrieval is one of the newest computer image processing applications in medicine. Recently, some algorithms have been developed to achieve this goal. Because of the particularity of cell pathology images, the result of the image retrieval based on single characteristic is not satisfactory. A new method for pathology image retrieval by combining color, texture and morphologic features to search cell images is proposed. Firstly, nucleus regions of leukocytes in images are automatically segmented by K-mean clustering method. Then single leukocyte region is detected by utilizing thresholding algorithm segmentation and mathematics morphology. The features that include color, texture and morphologic features are extracted from single leukocyte to represent main attribute in the search query. The features are then normalized because the numerical value range and physical meaning of extracted features are different. Finally, the relevance feedback system is introduced. So that the system can automatically adjust the weights of different features and improve the results of retrieval system according to the feedback information. Retrieval results using the proposed method fit closely with human perception and are better than those obtained with the methods based on single feature.

Color Based Bags-of-Emotions

NASA Astrophysics Data System (ADS)

Solli, Martin; Lenz, Reiner

In this paper we describe how to include high level semantic information, such as aesthetics and emotions, into Content Based Image Retrieval. We present a color-based emotion-related image descriptor that can be used for describing the emotional content of images. The color emotion metric used is derived from psychophysical experiments and based on three variables: activity, weight and heat. It was originally designed for single-colors, but recent research has shown that the same emotion estimates can be applied in the retrieval of multi-colored images. Here we describe a new approach, based on the assumption that perceived color emotions in images are mainly affected by homogenous regions, defined by the emotion metric, and transitions between regions. RGB coordinates are converted to emotion coordinates, and for each emotion channel, statistical measurements of gradient magnitudes within a stack of low-pass filtered images are used for finding interest points corresponding to homogeneous regions and transitions between regions. Emotion characteristics are derived for patches surrounding each interest point, and saved in a bag-of-emotions, that, for instance, can be used for retrieving images based on emotional content.

MPEG-7 based video annotation and browsing

NASA Astrophysics Data System (ADS)

Hoeynck, Michael; Auweiler, Thorsten; Wellhausen, Jens

2003-11-01

The huge amount of multimedia data produced worldwide requires annotation in order to enable universal content access and to provide content-based search-and-retrieval functionalities. Since manual video annotation can be time consuming, automatic annotation systems are required. We review recent approaches to content-based indexing and annotation of videos for different kind of sports and describe our approach to automatic annotation of equestrian sports videos. We especially concentrate on MPEG-7 based feature extraction and content description, where we apply different visual descriptors for cut detection. Further, we extract the temporal positions of single obstacles on the course by analyzing MPEG-7 edge information. Having determined single shot positions as well as the visual highlights, the information is jointly stored with meta-textual information in an MPEG-7 description scheme. Based on this information, we generate content summaries which can be utilized in a user-interface in order to provide content-based access to the video stream, but further for media browsing on a streaming server.

The Comprehensive Microbial Resource

PubMed Central

Peterson, Jeremy D.; Umayam, Lowell A.; Dickinson, Tanja; Hickey, Erin K.; White, Owen

2001-01-01

One challenge presented by large-scale genome sequencing efforts is effective display of uniform information to the scientific community. The Comprehensive Microbial Resource (CMR) contains robust annotation of all complete microbial genomes and allows for a wide variety of data retrievals. The bacterial information has been placed on the Web at http://www.tigr.org/CMR for retrieval using standard web browsing technology. Retrievals can be based on protein properties such as molecular weight or hydrophobicity, GC-content, functional role assignments and taxonomy. The CMR also has special web-based tools to allow data mining using pre-run homology searches, whole genome dot-plots, batch downloading and traversal across genomes using a variety of datatypes. PMID:11125067

Information Content and Sensitivity of the 3β+2α Lidar Measurement System for Microphysical Retrievals

NASA Astrophysics Data System (ADS)

Burton, S. P.; Liu, X.; Chemyakin, E.; Hostetler, C. A.; Stamnes, S.; Moore, R.; Sawamura, P.; Ferrare, R. A.; Knobelspiesse, K. D.

2015-12-01

There is considerable interest in retrieving aerosol effective radius, number concentration and refractive index from lidar measurements of extinction and backscatter at several wavelengths. The 3 backscatter + 2 extinction (3β+2α) combination is particularly important since the planned NASA Aerosol-Clouds-Ecosystem (ACE) mission recommends this combination of measurements. The 2nd-generation NASA Langley airborne High Spectral Resolution Lidar (HSRL-2) has been making 3β+2α measurements since 2012. Here we develop a deeper understanding of the information content and sensitivities of the 3β+2α system in terms of aerosol microphysical parameters of interest. We determine best case results using a retrieval-free methodology. We calculate information content and uncertainty metrics from Optimal Estimation techniques using only a simplified forward model look-up table, with no explicit inversion. Simplifications include spherical particles, mono-modal log-normal size distributions, and wavelength-independent refractive indices. Since we only use the forward model with no retrieval, our results are applicable as a best case for all existing retrievals. Retrieval-dependent errors due to mismatch between the assumptions and true atmospheric aerosols are not included. The sensitivity metrics allow for identifying (1) information content of the measurements versus a priori information; (2) best-case error bars on the retrieved parameters; and (3) potential sources of cross-talk or "compensating" errors wherein different retrieval parameters are not independently captured by the measurements. These results suggest that even in the best case, this retrieval system is underdetermined. Recommendations are given for addressing cross-talk between effective radius and number concentration. A potential solution to the under-determination problem is a combined active (lidar) and passive (polarimeter) retrieval, which is the subject of a new funded NASA project by our team.

Potential of Higher Moments of the Radar Doppler Spectrum for Studying Ice Clouds

NASA Astrophysics Data System (ADS)

Loehnert, U.; Maahn, M.

2015-12-01

More observations of ice clouds are required to fill gaps in understanding of microphysical properties and processes. However, in situ observations by aircraft are costly and cannot provide long term observations which are required for a deeper understanding of the processes. Ground based remote sensing observations have the potential to fill this gap, but their observations do not contain sufficient information to unambiguously constrain ice cloud properties which leads to high uncertainties. For vertically pointing cloud radars, usually only reflectivity and mean Doppler velocity are used for retrievals; some studies proposed also the use of Doppler spectrum width.In this study, it is investigated whether additional information can be obtained by exploiting also higher moments of the Doppler spectrum such as skewness and kurtosis together with the slope of the Doppler peak. For this, observations of pure ice clouds from the Indirect and Semi-Direct Aerosol Campaign (ISDAC) in Alaska 2008 are analyzed. Using the ISDAC data set, an Optimal Estimation based retrieval is set up based on synthetic and real radar observations. The passive and active microwave radiative transfer model (PAMTRA) is used as a forward model together with the Self-Similar Rayleigh-Gans approximation for estimation of the scattering properties. The state vector of the retrieval consists of the parameters required to simulate the radar Doppler spectrum and describes particle mass, cross section area, particle size distribution, and kinematic conditions such as turbulence and vertical air motion. Using the retrieval, the information content (degrees of freedom for signal) is quantified that higher moments and slopes can contribute to an ice cloud retrieval. The impact of multiple frequencies, radar sensitivity and radar calibration is studied. For example, it is found that a single-frequency measurement using all moments and slopes contains already more information content than a dual-frequency measurement using only reflectivity and mean Doppler velocity. Eventually, the errors and uncertainties of the retrieved ice cloud parameters are investigated for the various retrieval configurations.

Potential of Higher Moments of the Radar Doppler Spectrum for Studying Ice Clouds

NASA Astrophysics Data System (ADS)

Lunt, M. F.; Rigby, M. L.; Ganesan, A.; Manning, A.; O'Doherty, S.; Prinn, R. G.; Saito, T.; Harth, C. M.; Muhle, J.; Weiss, R. F.; Salameh, P.; Arnold, T.; Yokouchi, Y.; Krummel, P. B.; Steele, P.; Fraser, P. J.; Li, S.; Park, S.; Kim, J.; Reimann, S.; Vollmer, M. K.; Lunder, C. R.; Hermansen, O.; Schmidbauer, N.; Young, D.; Simmonds, P. G.

2014-12-01

More observations of ice clouds are required to fill gaps in understanding of microphysical properties and processes. However, in situ observations by aircraft are costly and cannot provide long term observations which are required for a deeper understanding of the processes. Ground based remote sensing observations have the potential to fill this gap, but their observations do not contain sufficient information to unambiguously constrain ice cloud properties which leads to high uncertainties. For vertically pointing cloud radars, usually only reflectivity and mean Doppler velocity are used for retrievals; some studies proposed also the use of Doppler spectrum width.In this study, it is investigated whether additional information can be obtained by exploiting also higher moments of the Doppler spectrum such as skewness and kurtosis together with the slope of the Doppler peak. For this, observations of pure ice clouds from the Indirect and Semi-Direct Aerosol Campaign (ISDAC) in Alaska 2008 are analyzed. Using the ISDAC data set, an Optimal Estimation based retrieval is set up based on synthetic and real radar observations. The passive and active microwave radiative transfer model (PAMTRA) is used as a forward model together with the Self-Similar Rayleigh-Gans approximation for estimation of the scattering properties. The state vector of the retrieval consists of the parameters required to simulate the radar Doppler spectrum and describes particle mass, cross section area, particle size distribution, and kinematic conditions such as turbulence and vertical air motion. Using the retrieval, the information content (degrees of freedom for signal) is quantified that higher moments and slopes can contribute to an ice cloud retrieval. The impact of multiple frequencies, radar sensitivity and radar calibration is studied. For example, it is found that a single-frequency measurement using all moments and slopes contains already more information content than a dual-frequency measurement using only reflectivity and mean Doppler velocity. Eventually, the errors and uncertainties of the retrieved ice cloud parameters are investigated for the various retrieval configurations.

An Experimental Study on the Iso-Content-Based Angle Similarity Measure.

ERIC Educational Resources Information Center

Zhang, Jin; Rasmussen, Edie M.

2002-01-01

Retrieval performance of the iso-content-based angle similarity measure within the angle, distance, conjunction, disjunction, and ellipse retrieval models is compared with retrieval performance of the distance similarity measure and the angle similarity measure. Results show the iso-content-based angle similarity measure achieves satisfactory…

Hippocampal activation during retrieval of spatial context from episodic and semantic memory.

PubMed

Hoscheidt, Siobhan M; Nadel, Lynn; Payne, Jessica; Ryan, Lee

2010-10-15

The hippocampus, a region implicated in the processing of spatial information and episodic memory, is central to the debate concerning the relationship between episodic and semantic memory. Studies of medial temporal lobe amnesic patients provide evidence that the hippocampus is critical for the retrieval of episodic but not semantic memory. On the other hand, recent neuroimaging studies of intact individuals report hippocampal activation during retrieval of both autobiographical memories and semantic information that includes historical facts, famous faces, and categorical information, suggesting that episodic and semantic memory may engage the hippocampus during memory retrieval in similar ways. Few studies have matched episodic and semantic tasks for the degree to which they include spatial content, even though spatial content may be what drives hippocampal activation during semantic retrieval. To examine this issue, we conducted a functional magnetic resonance imaging (fMRI) study in which retrieval of spatial and nonspatial information was compared during an episodic and semantic recognition task. Results show that the hippocampus (1) participates preferentially in the retrieval of episodic memories; (2) is also engaged by retrieval of semantic memories, particularly those that include spatial information. These data suggest that sharp dissociations between episodic and semantic memory may be overly simplistic and that the hippocampus plays a role in the retrieval of spatial content whether drawn from a memory of one's own life experiences or real-world semantic knowledge. Published by Elsevier B.V.

HealthTrust: A Social Network Approach for Retrieving Online Health Videos

PubMed Central

Karlsen, Randi; Melton, Genevieve B

2012-01-01

Background Social media are becoming mainstream in the health domain. Despite the large volume of accurate and trustworthy health information available on social media platforms, finding good-quality health information can be difficult. Misleading health information can often be popular (eg, antivaccination videos) and therefore highly rated by general search engines. We believe that community wisdom about the quality of health information can be harnessed to help create tools for retrieving good-quality social media content. Objectives To explore approaches for extracting metrics about authoritativeness in online health communities and how these metrics positively correlate with the quality of the content. Methods We designed a metric, called HealthTrust, that estimates the trustworthiness of social media content (eg, blog posts or videos) in a health community. The HealthTrust metric calculates reputation in an online health community based on link analysis. We used the metric to retrieve YouTube videos and channels about diabetes. In two different experiments, health consumers provided 427 ratings of 17 videos and professionals gave 162 ratings of 23 videos. In addition, two professionals reviewed 30 diabetes channels. Results HealthTrust may be used for retrieving online videos on diabetes, since it performed better than YouTube Search in most cases. Overall, of 20 potential channels, HealthTrust’s filtering allowed only 3 bad channels (15%) versus 8 (40%) on the YouTube list. Misleading and graphic videos (eg, featuring amputations) were more commonly found by YouTube Search than by searches based on HealthTrust. However, some videos from trusted sources had low HealthTrust scores, mostly from general health content providers, and therefore not highly connected in the diabetes community. When comparing video ratings from our reviewers, we found that HealthTrust achieved a positive and statistically significant correlation with professionals (Pearson r 10 = .65, P = .02) and a trend toward significance with health consumers (r 7 = .65, P = .06) with videos on hemoglobinA1 c, but it did not perform as well with diabetic foot videos. Conclusions The trust-based metric HealthTrust showed promising results when used to retrieve diabetes content from YouTube. Our research indicates that social network analysis may be used to identify trustworthy social media in health communities. PMID:22356723

A medical digital library to support scenario and user-tailored information retrieval.

PubMed

Chu, W W; Johnson, D B; Kangarloo, H

2000-06-01

Current large-scale information sources are designed to support general queries and lack the ability to support scenario-specific information navigation, gathering, and presentation. As a result, users are often unable to obtain desired specific information within a well-defined subject area. Today's information systems do not provide efficient content navigation, incremental appropriate matching, or content correlation. We are developing the following innovative technologies to remedy these problems: 1) scenario-based proxies, enabling the gathering and filtering of information customized for users within a pre-defined domain; 2) context-sensitive navigation and matching, providing approximate matching and similarity links when an exact match to a user's request is unavailable; 3) content correlation of documents, creating semantic links between documents and information sources; and 4) user models for customizing retrieved information and result presentation. A digital medical library is currently being constructed using these technologies to provide customized information for the user. The technologies are general in nature and can provide custom and scenario-specific information in many other domains (e.g., crisis management).

Design of a graphical user interface for an intelligent multimedia information system for radiology research

NASA Astrophysics Data System (ADS)

Taira, Ricky K.; Wong, Clement; Johnson, David; Bhushan, Vikas; Rivera, Monica; Huang, Lu J.; Aberle, Denise R.; Cardenas, Alfonso F.; Chu, Wesley W.

1995-05-01

With the increase in the volume and distribution of images and text available in PACS and medical electronic health-care environments it becomes increasingly important to maintain indexes that summarize the content of these multi-media documents. Such indices are necessary to quickly locate relevant patient cases for research, patient management, and teaching. The goal of this project is to develop an intelligent document retrieval system that allows researchers to request for patient cases based on document content. Thus we wish to retrieve patient cases from electronic information archives that could include a combined specification of patient demographics, low level radiologic findings (size, shape, number), intermediate-level radiologic findings (e.g., atelectasis, infiltrates, etc.) and/or high-level pathology constraints (e.g., well-differentiated small cell carcinoma). The cases could be distributed among multiple heterogeneous databases such as PACS, RIS, and HIS. Content- based retrieval systems go beyond the capabilities of simple key-word or string-based retrieval matching systems. These systems require a knowledge base to comprehend the generality/specificity of a concept (thus knowing the subclasses or related concepts to a given concept) and knowledge of the various string representations for each concept (i.e., synonyms, lexical variants, etc.). We have previously reported on a data integration mediation layer that allows transparent access to multiple heterogeneous distributed medical databases (HIS, RIS, and PACS). The data access layer of our architecture currently has limited query processing capabilities. Given a patient hospital identification number, the access mediation layer collects all documents in RIS and HIS and returns this information to a specified workstation location. In this paper we report on our efforts to extend the query processing capabilities of the system by creation of custom query interfaces, an intelligent query processing engine, and a document-content index that can be generated automatically (i.e., no manual authoring or changes to the normal clinical protocols).

Buckets: Smart Objects for Digital Libraries

NASA Technical Reports Server (NTRS)

Nelson, Michael L.

2001-01-01

Current discussion of digital libraries (DLs) is often dominated by the merits of the respective storage, search and retrieval functionality of archives, repositories, search engines, search interfaces and database systems. While these technologies are necessary for information management, the information content is more important than the systems used for its storage and retrieval. Digital information should have the same long-term survivability prospects as traditional hardcopy information and should be protected to the extent possible from evolving search engine technologies and vendor vagaries in database management systems. Information content and information retrieval systems should progress on independent paths and make limited assumptions about the status or capabilities of the other. Digital information can achieve independence from archives and DL systems through the use of buckets. Buckets are an aggregative, intelligent construct for publishing in DLs. Buckets allow the decoupling of information content from information storage and retrieval. Buckets exist within the Smart Objects and Dumb Archives model for DLs in that many of the functionalities and responsibilities traditionally associated with archives are pushed down (making the archives dumber) into the buckets (making them smarter). Some of the responsibilities imbued to buckets are the enforcement of their terms and conditions, and maintenance and display of their contents.

An integrated content and metadata based retrieval system for art.

PubMed

Lewis, Paul H; Martinez, Kirk; Abas, Fazly Salleh; Fauzi, Mohammad Faizal Ahmad; Chan, Stephen C Y; Addis, Matthew J; Boniface, Mike J; Grimwood, Paul; Stevenson, Alison; Lahanier, Christian; Stevenson, James

2004-03-01

A new approach to image retrieval is presented in the domain of museum and gallery image collections. Specialist algorithms, developed to address specific retrieval tasks, are combined with more conventional content and metadata retrieval approaches, and implemented within a distributed architecture to provide cross-collection searching and navigation in a seamless way. External systems can access the different collections using interoperability protocols and open standards, which were extended to accommodate content based as well as text based retrieval paradigms. After a brief overview of the complete system, we describe the novel design and evaluation of some of the specialist image analysis algorithms including a method for image retrieval based on sub-image queries, retrievals based on very low quality images and retrieval using canvas crack patterns. We show how effective retrieval results can be achieved by real end-users consisting of major museums and galleries, accessing the distributed but integrated digital collections.

An architecture for diversity-aware search for medical web content.

PubMed

Denecke, K

2012-01-01

The Web provides a huge source of information, also on medical and health-related issues. In particular the content of medical social media data can be diverse due to the background of an author, the source or the topic. Diversity in this context means that a document covers different aspects of a topic or a topic is described in different ways. In this paper, we introduce an approach that allows to consider the diverse aspects of a search query when providing retrieval results to a user. We introduce a system architecture for a diversity-aware search engine that allows retrieving medical information from the web. The diversity of retrieval results is assessed by calculating diversity measures that rely upon semantic information derived from a mapping to concepts of a medical terminology. Considering these measures, the result set is diversified by ranking more diverse texts higher. The methods and system architecture are implemented in a retrieval engine for medical web content. The diversity measures reflect the diversity of aspects considered in a text and its type of information content. They are used for result presentation, filtering and ranking. In a user evaluation we assess the user satisfaction with an ordering of retrieval results that considers the diversity measures. It is shown through the evaluation that diversity-aware retrieval considering diversity measures in ranking could increase the user satisfaction with retrieval results.

Diversification of visual media retrieval results using saliency detection

NASA Astrophysics Data System (ADS)

Muratov, Oleg; Boato, Giulia; De Natale, Franesco G. B.

2013-03-01

Diversification of retrieval results allows for better and faster search. Recently there has been proposed different methods for diversification of image retrieval results mainly utilizing text information and techniques imported from natural language processing domain. However, images contain visual information that is impossible to describe in text and the use of visual features is inevitable. Visual saliency is information about the main object of an image implicitly included by humans while creating visual content. For this reason it is naturally to exploit this information for the task of diversification of the content. In this work we study whether visual saliency can be used for the task of diversification and propose a method for re-ranking image retrieval results using saliency. The evaluation has shown that the use of saliency information results in higher diversity of retrieval results.

Content Based Image Retrieval and Information Theory: A General Approach.

ERIC Educational Resources Information Center

Zachary, John; Iyengar, S. S.; Barhen, Jacob

2001-01-01

Proposes an alternative real valued representation of color based on the information theoretic concept of entropy. A theoretical presentation of image entropy is accompanied by a practical description of the merits and limitations of image entropy compared to color histograms. Results suggest that image entropy is a promising approach to image…

Evaluation of information-theoretic similarity measures for content-based retrieval and detection of masses in mammograms.

PubMed

Tourassi, Georgia D; Harrawood, Brian; Singh, Swatee; Lo, Joseph Y; Floyd, Carey E

2007-01-01

The purpose of this study was to evaluate image similarity measures employed in an information-theoretic computer-assisted detection (IT-CAD) scheme. The scheme was developed for content-based retrieval and detection of masses in screening mammograms. The study is aimed toward an interactive clinical paradigm where physicians query the proposed IT-CAD scheme on mammographic locations that are either visually suspicious or indicated as suspicious by other cuing CAD systems. The IT-CAD scheme provides an evidence-based, second opinion for query mammographic locations using a knowledge database of mass and normal cases. In this study, eight entropy-based similarity measures were compared with respect to retrieval precision and detection accuracy using a database of 1820 mammographic regions of interest. The IT-CAD scheme was then validated on a separate database for false positive reduction of progressively more challenging visual cues generated by an existing, in-house mass detection system. The study showed that the image similarity measures fall into one of two categories; one category is better suited to the retrieval of semantically similar cases while the second is more effective with knowledge-based decisions regarding the presence of a true mass in the query location. In addition, the IT-CAD scheme yielded a substantial reduction in false-positive detections while maintaining high detection rate for malignant masses.

Novel Algorithm for Classification of Medical Images

NASA Astrophysics Data System (ADS)

Bhushan, Bharat; Juneja, Monika

2010-11-01

Content-based image retrieval (CBIR) methods in medical image databases have been designed to support specific tasks, such as retrieval of medical images. These methods cannot be transferred to other medical applications since different imaging modalities require different types of processing. To enable content-based queries in diverse collections of medical images, the retrieval system must be familiar with the current Image class prior to the query processing. Further, almost all of them deal with the DICOM imaging format. In this paper a novel algorithm based on energy information obtained from wavelet transform for the classification of medical images according to their modalities is described. For this two types of wavelets have been used and have been shown that energy obtained in either case is quite distinct for each of the body part. This technique can be successfully applied to different image formats. The results are shown for JPEG imaging format.

Wave optics-based LEO-LEO radio occultation retrieval

NASA Astrophysics Data System (ADS)

Benzon, Hans-Henrik; Høeg, Per

2016-06-01

This paper describes the theory for performing retrieval of radio occultations that use probing frequencies in the XK and KM band. Normally, radio occultations use frequencies in the L band, and GPS satellites are used as the transmitting source, and the occultation signals are received by a GPS receiver on board a Low Earth Orbit (LEO) satellite. The technique is based on the Doppler shift imposed, by the atmosphere, on the signal emitted from the GPS satellite. Two LEO satellites are assumed in the occultations discussed in this paper, and the retrieval is also dependent on the decrease in the signal amplitude caused by atmospheric absorption. The radio wave transmitter is placed on one of these satellites, while the receiver is placed on the other LEO satellite. One of the drawbacks of normal GPS-based radio occultations is that external information is needed to calculate some of the atmospheric products such as the correct water vapor content in the atmosphere. These limitations can be overcome when a proper selected range of high-frequency waves are used to probe the atmosphere. Probing frequencies close to the absorption line of water vapor have been included, thus allowing the retrieval of the water vapor content. Selecting the correct probing frequencies would make it possible to retrieve other information such as the content of ozone. The retrieval is performed through a number of processing steps which are based on the Full Spectrum Inversion (FSI) technique. The retrieval chain is therefore a wave optics-based retrieval chain, and it is therefore possible to process measurements that include multipath. In this paper simulated LEO to LEO radio occultations based on five different frequencies are used. The five frequencies are placed in the XK or KM frequency band. This new wave optics-based retrieval chain is used on a number of examples, and the retrieved atmospheric parameters are compared to the parameters from a global European Centre for Medium-Range Weather Forecasts analysis model. This model is used in a forward propagator that simulates the electromagnetic field amplitudes and phases at the receiver on board the LEO satellite. LEO-LEO cross-link radio occultations using high frequencies are a relatively new technique, and the possibilities and advantages of the technique still need to be investigated. The retrieval of this type of radio occultations is considerably more complicated than standard GPS to LEO radio occultations, because the attenuation of the probing radio waves is used in the retrieval and the atmospheric parameters are found using a least squares solver. The best algorithms and the number of probing frequencies that is economically viable must also be determined. This paper intends to answer some of these questions using end-to-end simulations.

Content Based Lecture Video Retrieval Using Speech and Video Text Information

ERIC Educational Resources Information Center

Yang, Haojin; Meinel, Christoph

2014-01-01

In the last decade e-lecturing has become more and more popular. The amount of lecture video data on the "World Wide Web" (WWW) is growing rapidly. Therefore, a more efficient method for video retrieval in WWW or within large lecture video archives is urgently needed. This paper presents an approach for automated video indexing and video…

Disregarding familiarity during recollection attempts: content-specific recapitulation as a retrieval orientation strategy.

PubMed

Gray, Stephen J; Gallo, David A

2015-01-01

People can use a content-specific recapitulation strategy to trigger memories (i.e., mentally reinstating encoding conditions), but how people deploy this strategy is unclear. Is recapitulation naturally used to guide all recollection attempts, or is it only used selectively, after retrieving incomplete information that requires additional monitoring? According to a retrieval orientation model, people use recapitulation whenever they search memory for specific information, regardless of what information might come to mind. In contrast, according to a postretrieval monitoring model, people selectively engage recapitulation only after retrieving ambiguous information in order to evaluate this information and guide additional retrieval attempts. We tested between these models using a criterial recollection task, and by manipulating the strength of ambiguous information associated with to-be-rejected foils (i.e., familiarity or noncriterial information). Replicating prior work, foil rejections were greater when people attempted to recollect targets studied at a semantic level (deep test) compared to an orthographic level (shallow test), implicating more accurate retrieval monitoring. To investigate the role of a recapitulation strategy in this monitoring process, a final test assessed memory for the foils that were earlier processed on these recollection tests. Performance on this foil recognition test suggested that people had engaged in more elaborative content-specific recapitulation when initially tested for deep compared to shallow recollections, and critically, this elaboration effect did not interact with the experimental manipulation of foil strength. These results support the retrieval orientation model, whereby a recapitulation strategy was used to orient retrieval toward specific information during every recollection attempt. PsycINFO Database Record (c) 2015 APA, all rights reserved.

MediaNet: a multimedia information network for knowledge representation

NASA Astrophysics Data System (ADS)

Benitez, Ana B.; Smith, John R.; Chang, Shih-Fu

2000-10-01

In this paper, we present MediaNet, which is a knowledge representation framework that uses multimedia content for representing semantic and perceptual information. The main components of MediaNet include conceptual entities, which correspond to real world objects, and relationships among concepts. MediaNet allows the concepts and relationships to be defined or exemplified by multimedia content such as images, video, audio, graphics, and text. MediaNet models the traditional relationship types such as generalization and aggregation but adds additional functionality by modeling perceptual relationships based on feature similarity. For example, MediaNet allows a concept such as car to be defined as a type of a transportation vehicle, but which is further defined and illustrated through example images, videos and sounds of cars. In constructing the MediaNet framework, we have built on the basic principles of semiotics and semantic networks in addition to utilizing the audio-visual content description framework being developed as part of the MPEG-7 multimedia content description standard. By integrating both conceptual and perceptual representations of knowledge, MediaNet has potential to impact a broad range of applications that deal with multimedia content at the semantic and perceptual levels. In particular, we have found that MediaNet can improve the performance of multimedia retrieval applications by using query expansion, refinement and translation across multiple content modalities. In this paper, we report on experiments that use MediaNet in searching for images. We construct the MediaNet knowledge base using both WordNet and an image network built from multiple example images and extracted color and texture descriptors. Initial experimental results demonstrate improved retrieval effectiveness using MediaNet in a content-based retrieval system.

User-Centric Multi-Criteria Information Retrieval

NASA Technical Reports Server (NTRS)

Wolfe, Shawn R.; Zhang, Yi

2009-01-01

Information retrieval models usually represent content only, and not other considerations, such as authority, cost, and recency. How could multiple criteria be utilized in information retrieval, and how would it affect the results? In our experiments, using multiple user-centric criteria always produced better results than a single criteria.

Occam's razor: supporting visual query expression for content-based image queries

NASA Astrophysics Data System (ADS)

Venters, Colin C.; Hartley, Richard J.; Hewitt, William T.

2005-01-01

This paper reports the results of a usability experiment that investigated visual query formulation on three dimensions: effectiveness, efficiency, and user satisfaction. Twenty eight evaluation sessions were conducted in order to assess the extent to which query by visual example supports visual query formulation in a content-based image retrieval environment. In order to provide a context and focus for the investigation, the study was segmented by image type, user group, and use function. The image type consisted of a set of abstract geometric device marks supplied by the UK Trademark Registry. Users were selected from the 14 UK Patent Information Network offices. The use function was limited to the retrieval of images by shape similarity. Two client interfaces were developed for comparison purposes: Trademark Image Browser Engine (TRIBE) and Shape Query Image Retrieval Systems Engine (SQUIRE).

Occam"s razor: supporting visual query expression for content-based image queries

NASA Astrophysics Data System (ADS)

Venters, Colin C.; Hartley, Richard J.; Hewitt, William T.

2004-12-01

This paper reports the results of a usability experiment that investigated visual query formulation on three dimensions: effectiveness, efficiency, and user satisfaction. Twenty eight evaluation sessions were conducted in order to assess the extent to which query by visual example supports visual query formulation in a content-based image retrieval environment. In order to provide a context and focus for the investigation, the study was segmented by image type, user group, and use function. The image type consisted of a set of abstract geometric device marks supplied by the UK Trademark Registry. Users were selected from the 14 UK Patent Information Network offices. The use function was limited to the retrieval of images by shape similarity. Two client interfaces were developed for comparison purposes: Trademark Image Browser Engine (TRIBE) and Shape Query Image Retrieval Systems Engine (SQUIRE).

MRML: an extensible communication protocol for interoperability and benchmarking of multimedia information retrieval systems

NASA Astrophysics Data System (ADS)

Mueller, Wolfgang; Mueller, Henning; Marchand-Maillet, Stephane; Pun, Thierry; Squire, David M.; Pecenovic, Zoran; Giess, Christoph; de Vries, Arjen P.

2000-10-01

While in the area of relational databases interoperability is ensured by common communication protocols (e.g. ODBC/JDBC using SQL), Content Based Image Retrieval Systems (CBIRS) and other multimedia retrieval systems are lacking both a common query language and a common communication protocol. Besides its obvious short term convenience, interoperability of systems is crucial for the exchange and analysis of user data. In this paper, we present and describe an extensible XML-based query markup language, called MRML (Multimedia Retrieval markup Language). MRML is primarily designed so as to ensure interoperability between different content-based multimedia retrieval systems. Further, MRML allows researchers to preserve their freedom in extending their system as needed. MRML encapsulates multimedia queries in a way that enable multimedia (MM) query languages, MM content descriptions, MM query engines, and MM user interfaces to grow independently from each other, reaching a maximum of interoperability while ensuring a maximum of freedom for the developer. For benefitting from this, only a few simple design principles have to be respected when extending MRML for one's fprivate needs. The design of extensions withing the MRML framework will be described in detail in the paper. MRML has been implemented and tested for the CBIRS Viper, using the user interface Snake Charmer. Both are part of the GNU project and can be downloaded at our site.

A Semantic Approach for Geospatial Information Extraction from Unstructured Documents

NASA Astrophysics Data System (ADS)

Sallaberry, Christian; Gaio, Mauro; Lesbegueries, Julien; Loustau, Pierre

Local cultural heritage document collections are characterized by their content, which is strongly attached to a territory and its land history (i.e., geographical references). Our contribution aims at making the content retrieval process more efficient whenever a query includes geographic criteria. We propose a core model for a formal representation of geographic information. It takes into account characteristics of different modes of expression, such as written language, captures of drawings, maps, photographs, etc. We have developed a prototype that fully implements geographic information extraction (IE) and geographic information retrieval (IR) processes. All PIV prototype processing resources are designed as Web Services. We propose a geographic IE process based on semantic treatment as a supplement to classical IE approaches. We implement geographic IR by using intersection computing algorithms that seek out any intersection between formal geocoded representations of geographic information in a user query and similar representations in document collection indexes.

Active retrieval facilitates across-episode binding by modulating the content of memory

PubMed Central

Bridge, Donna J.; Voss, Joel L.

2014-01-01

The contents of memory can be updated when information from the current episode is bound with content retrieved from previous episodes. Little is known regarding factors that determine the memory content that is subject to this across-episode binding. We tested whether across-episode binding preferentially occurs for memory content that is currently “active” and identified relevant neural correlates. After studying objects at specific locations on scene backgrounds, subjects performed one of two retrieval tasks for the objects on different scene backgrounds. In an active condition, subjects recalled object locations, whereas subjects merely dragged objects to predetermined locations in a passive condition. Immediately following each object-location retrieval event, a novel face appeared on a blank screen. We hypothesized that the original episode content would be active in memory during face encoding in the active condition, but not in the passive condition (despite seeing the same content in both conditions). A ramification of the active condition would thus be preferential binding of original episode content to novel faces, with no such across-episode binding in the passive condition. Indeed, memory for faces was better when tested on the original background scenes in the active relative to passive condition, indicating that original episode content was bound with the active condition faces, whereas this occurred to a lesser extent for the passive condition faces. Likewise, early-onset negative ERP effects reflected binding of the face to the original episode content in the active but not the passive condition. In contrast, binding in the passive condition occurred only when faces were physically displayed on the original scenes during recognition testing, and a very similar early-onset negative ERP effect signaled binding in this condition. ERP correlates of binding were thus similar for across-episode and within-episode binding (and were distinct from other encoding and retrieval ERP signals in both cases), indicating that active retrieval modulated when binding occurred, not the nature of the binding process per se. These results suggest that active retrieval promotes binding of new information with contents of memory, whereas without active retrieval, these unrelated pieces of information might be bound only when they are physically paired. PMID:25173711

Visual Based Retrieval Systems and Web Mining--Introduction.

ERIC Educational Resources Information Center

Iyengar, S. S.

2001-01-01

Briefly discusses Web mining and image retrieval techniques, and then presents a summary of articles in this special issue. Articles focus on Web content mining, artificial neural networks as tools for image retrieval, content-based image retrieval systems, and personalizing the Web browsing experience using media agents. (AEF)

Application of MPEG-7 descriptors for content-based indexing of sports videos

NASA Astrophysics Data System (ADS)

Hoeynck, Michael; Auweiler, Thorsten; Ohm, Jens-Rainer

2003-06-01

The amount of multimedia data available worldwide is increasing every day. There is a vital need to annotate multimedia data in order to allow universal content access and to provide content-based search-and-retrieval functionalities. Since supervised video annotation can be time consuming, an automatic solution is appreciated. We review recent approaches to content-based indexing and annotation of videos for different kind of sports, and present our application for the automatic annotation of equestrian sports videos. Thereby, we especially concentrate on MPEG-7 based feature extraction and content description. We apply different visual descriptors for cut detection. Further, we extract the temporal positions of single obstacles on the course by analyzing MPEG-7 edge information and taking specific domain knowledge into account. Having determined single shot positions as well as the visual highlights, the information is jointly stored together with additional textual information in an MPEG-7 description scheme. Using this information, we generate content summaries which can be utilized in a user front-end in order to provide content-based access to the video stream, but further content-based queries and navigation on a video-on-demand streaming server.

Physical retrieval of precipitation water contents from Special Sensor Microwave/Imager (SSM/I) data. Part 2: Retrieval method and applications (report version)

NASA Technical Reports Server (NTRS)

Olson, William S.

1990-01-01

A physical retrieval method for estimating precipitating water distributions and other geophysical parameters based upon measurements from the DMSP-F8 SSM/I is developed. Three unique features of the retrieval method are (1) sensor antenna patterns are explicitly included to accommodate varying channel resolution; (2) precipitation-brightness temperature relationships are quantified using the cloud ensemble/radiative parameterization; and (3) spatial constraints are imposed for certain background parameters, such as humidity, which vary more slowly in the horizontal than the cloud and precipitation water contents. The general framework of the method will facilitate the incorporation of measurements from the SSMJT, SSM/T-2 and geostationary infrared measurements, as well as information from conventional sources (e.g., radiosondes) or numerical forecast model fields.

Multispectral information for gas and aerosol retrieval from TANSO-FTS instrument

NASA Astrophysics Data System (ADS)

Herbin, H.; Labonnote, L. C.; Dubuisson, P.

2012-11-01

The Greenhouse gases Observing SATellite (GOSAT) mission and in particular TANSO-FTS instrument has the advantage to measure simultaneously the same field of view in different spectral ranges with a high spectral resolution. These features are promising to improve, not only, gaseous retrieval in clear sky or scattering atmosphere, but also to retrieve aerosol parameters. Therefore, this paper is dedicated to an Information Content (IC) analysis of potential synergy between thermal infrared, shortwave infrared and visible, in order to obtain a more accurate retrieval of gas and aerosol. The latter is based on Shannon theory and used a sophisticated radiative transfer algorithm developed at "Laboratoire d'Optique Atmosphérique", dealing with multiple scattering. This forward model can be relied to an optimal estimation method, which allows simultaneously retrieving gases profiles and aerosol granulometry and concentration. The analysis of the information provided by the spectral synergy is based on climatology of dust, volcanic ash and biomass burning aerosols. This work was conducted in order to develop a powerful tool that allows retrieving simultaneously not only the gas concentrations but also the aerosol characteristics by selecting the so called "best channels", i.e. the channels that bring most of the information concerning gas and aerosol. The methodology developed in this paper could also be used to define the specifications of future high spectral resolution mission to reach a given accuracy on retrieved parameters.

A multimedia retrieval framework based on semi-supervised ranking and relevance feedback.

PubMed

Yang, Yi; Nie, Feiping; Xu, Dong; Luo, Jiebo; Zhuang, Yueting; Pan, Yunhe

2012-04-01

We present a new framework for multimedia content analysis and retrieval which consists of two independent algorithms. First, we propose a new semi-supervised algorithm called ranking with Local Regression and Global Alignment (LRGA) to learn a robust Laplacian matrix for data ranking. In LRGA, for each data point, a local linear regression model is used to predict the ranking scores of its neighboring points. A unified objective function is then proposed to globally align the local models from all the data points so that an optimal ranking score can be assigned to each data point. Second, we propose a semi-supervised long-term Relevance Feedback (RF) algorithm to refine the multimedia data representation. The proposed long-term RF algorithm utilizes both the multimedia data distribution in multimedia feature space and the history RF information provided by users. A trace ratio optimization problem is then formulated and solved by an efficient algorithm. The algorithms have been applied to several content-based multimedia retrieval applications, including cross-media retrieval, image retrieval, and 3D motion/pose data retrieval. Comprehensive experiments on four data sets have demonstrated its advantages in precision, robustness, scalability, and computational efficiency.

Case retrieval in medical databases by fusing heterogeneous information.

PubMed

Quellec, Gwénolé; Lamard, Mathieu; Cazuguel, Guy; Roux, Christian; Cochener, Béatrice

2011-01-01

A novel content-based heterogeneous information retrieval framework, particularly well suited to browse medical databases and support new generation computer aided diagnosis (CADx) systems, is presented in this paper. It was designed to retrieve possibly incomplete documents, consisting of several images and semantic information, from a database; more complex data types such as videos can also be included in the framework. The proposed retrieval method relies on image processing, in order to characterize each individual image in a document by their digital content, and information fusion. Once the available images in a query document are characterized, a degree of match, between the query document and each reference document stored in the database, is defined for each attribute (an image feature or a metadata). A Bayesian network is used to recover missing information if need be. Finally, two novel information fusion methods are proposed to combine these degrees of match, in order to rank the reference documents by decreasing relevance for the query. In the first method, the degrees of match are fused by the Bayesian network itself. In the second method, they are fused by the Dezert-Smarandache theory: the second approach lets us model our confidence in each source of information (i.e., each attribute) and take it into account in the fusion process for a better retrieval performance. The proposed methods were applied to two heterogeneous medical databases, a diabetic retinopathy database and a mammography screening database, for computer aided diagnosis. Precisions at five of 0.809 ± 0.158 and 0.821 ± 0.177, respectively, were obtained for these two databases, which is very promising.

A Novel Navigation Paradigm for XML Repositories.

ERIC Educational Resources Information Center

Azagury, Alain; Factor, Michael E.; Maarek, Yoelle S.; Mandler, Benny

2002-01-01

Discusses data exchange over the Internet and describes the architecture and implementation of an XML document repository that promotes a navigation paradigm for XML documents based on content and context. Topics include information retrieval and semistructured documents; and file systems as information storage infrastructure, particularly XMLFS.…

PCI bus content-addressable-memory (CAM) implementation on FPGA for pattern recognition/image retrieval in a distributed environment

NASA Astrophysics Data System (ADS)

Megherbi, Dalila B.; Yan, Yin; Tanmay, Parikh; Khoury, Jed; Woods, C. L.

2004-11-01

Recently surveillance and Automatic Target Recognition (ATR) applications are increasing as the cost of computing power needed to process the massive amount of information continues to fall. This computing power has been made possible partly by the latest advances in FPGAs and SOPCs. In particular, to design and implement state-of-the-Art electro-optical imaging systems to provide advanced surveillance capabilities, there is a need to integrate several technologies (e.g. telescope, precise optics, cameras, image/compute vision algorithms, which can be geographically distributed or sharing distributed resources) into a programmable system and DSP systems. Additionally, pattern recognition techniques and fast information retrieval, are often important components of intelligent systems. The aim of this work is using embedded FPGA as a fast, configurable and synthesizable search engine in fast image pattern recognition/retrieval in a distributed hardware/software co-design environment. In particular, we propose and show a low cost Content Addressable Memory (CAM)-based distributed embedded FPGA hardware architecture solution with real time recognition capabilities and computing for pattern look-up, pattern recognition, and image retrieval. We show how the distributed CAM-based architecture offers a performance advantage of an order-of-magnitude over RAM-based architecture (Random Access Memory) search for implementing high speed pattern recognition for image retrieval. The methods of designing, implementing, and analyzing the proposed CAM based embedded architecture are described here. Other SOPC solutions/design issues are covered. Finally, experimental results, hardware verification, and performance evaluations using both the Xilinx Virtex-II and the Altera Apex20k are provided to show the potential and power of the proposed method for low cost reconfigurable fast image pattern recognition/retrieval at the hardware/software co-design level.

Audio-guided audiovisual data segmentation, indexing, and retrieval

NASA Astrophysics Data System (ADS)

Zhang, Tong; Kuo, C.-C. Jay

1998-12-01

While current approaches for video segmentation and indexing are mostly focused on visual information, audio signals may actually play a primary role in video content parsing. In this paper, we present an approach for automatic segmentation, indexing, and retrieval of audiovisual data, based on audio content analysis. The accompanying audio signal of audiovisual data is first segmented and classified into basic types, i.e., speech, music, environmental sound, and silence. This coarse-level segmentation and indexing step is based upon morphological and statistical analysis of several short-term features of the audio signals. Then, environmental sounds are classified into finer classes, such as applause, explosions, bird sounds, etc. This fine-level classification and indexing step is based upon time- frequency analysis of audio signals and the use of the hidden Markov model as the classifier. On top of this archiving scheme, an audiovisual data retrieval system is proposed. Experimental results show that the proposed approach has an accuracy rate higher than 90 percent for the coarse-level classification, and higher than 85 percent for the fine-level classification. Examples of audiovisual data segmentation and retrieval are also provided.

An ambiguity of information content and error in an ill-posed satellite inversion

NASA Astrophysics Data System (ADS)

Koner, Prabhat

According to Rodgers (2000, stochastic approach), the averaging kernel (AK) is the representational matrix to understand the information content in a scholastic inversion. On the other hand, in deterministic approach this is referred to as model resolution matrix (MRM, Menke 1989). The analysis of AK/MRM can only give some understanding of how much regularization is imposed on the inverse problem. The trace of the AK/MRM matrix, which is the so-called degree of freedom from signal (DFS; stochastic) or degree of freedom in retrieval (DFR; deterministic). There are no physical/mathematical explanations in the literature: why the trace of the matrix is a valid form to calculate this quantity? We will present an ambiguity between information and error using a real life problem of SST retrieval from GOES13. The stochastic information content calculation is based on the linear assumption. The validity of such mathematics in satellite inversion will be questioned because it is based on the nonlinear radiative transfer and ill-conditioned inverse problems. References: Menke, W., 1989: Geophysical data analysis: discrete inverse theory. San Diego academic press. Rodgers, C.D., 2000: Inverse methods for atmospheric soundings: theory and practice. Singapore :World Scientific.

Complex Event Processing for Content-Based Text, Image, and Video Retrieval

DTIC Science & Technology

2016-06-01

NY): Wiley- Interscience; 2000. Feldman R, Sanger J. The text mining handbook: advanced approaches in analyzing unstructured data. New York (NY...ARL-TR-7705 ● JUNE 2016 US Army Research Laboratory Complex Event Processing for Content-Based Text , Image, and Video Retrieval...ARL-TR-7705 ● JUNE 2016 US Army Research Laboratory Complex Event Processing for Content-Based Text , Image, and Video Retrieval

Spatial Paradigm for Information Retrieval and Exploration

DOE Office of Scientific and Technical Information (OSTI.GOV)

The SPIRE system consists of software for visual analysis of primarily text based information sources. This technology enables the content analysis of text documents without reading all the documents. It employs several algorithms for text and word proximity analysis. It identifies the key themes within the text documents. From this analysis, it projects the results onto a visual spatial proximity display (Galaxies or Themescape) where items (documents and/or themes) visually close to each other are known to have content which is close to each other. Innovative interaction techniques then allow for dynamic visual analysis of large text based information spaces.

SPIRE1.03. Spatial Paradigm for Information Retrieval and Exploration

DOE Office of Scientific and Technical Information (OSTI.GOV)

Adams, K.J.; Bohn, S.; Crow, V.

The SPIRE system consists of software for visual analysis of primarily text based information sources. This technology enables the content analysis of text documents without reading all the documents. It employs several algorithms for text and word proximity analysis. It identifies the key themes within the text documents. From this analysis, it projects the results onto a visual spatial proximity display (Galaxies or Themescape) where items (documents and/or themes) visually close to each other are known to have content which is close to each other. Innovative interaction techniques then allow for dynamic visual analysis of large text based information spaces.

Texture-specific bag of visual words model and spatial cone matching-based method for the retrieval of focal liver lesions using multiphase contrast-enhanced CT images.

PubMed

Xu, Yingying; Lin, Lanfen; Hu, Hongjie; Wang, Dan; Zhu, Wenchao; Wang, Jian; Han, Xian-Hua; Chen, Yen-Wei

2018-01-01

The bag of visual words (BoVW) model is a powerful tool for feature representation that can integrate various handcrafted features like intensity, texture, and spatial information. In this paper, we propose a novel BoVW-based method that incorporates texture and spatial information for the content-based image retrieval to assist radiologists in clinical diagnosis. This paper presents a texture-specific BoVW method to represent focal liver lesions (FLLs). Pixels in the region of interest (ROI) are classified into nine texture categories using the rotation-invariant uniform local binary pattern method. The BoVW-based features are calculated for each texture category. In addition, a spatial cone matching (SCM)-based representation strategy is proposed to describe the spatial information of the visual words in the ROI. In a pilot study, eight radiologists with different clinical experience performed diagnoses for 20 cases with and without the top six retrieved results. A total of 132 multiphase computed tomography volumes including five pathological types were collected. The texture-specific BoVW was compared to other BoVW-based methods using the constructed dataset of FLLs. The results show that our proposed model outperforms the other three BoVW methods in discriminating different lesions. The SCM method, which adds spatial information to the orderless BoVW model, impacted the retrieval performance. In the pilot trial, the average diagnosis accuracy of the radiologists was improved from 66 to 80% using the retrieval system. The preliminary results indicate that the texture-specific features and the SCM-based BoVW features can effectively characterize various liver lesions. The retrieval system has the potential to improve the diagnostic accuracy and the confidence of the radiologists.

TBIdoc: 3D content-based CT image retrieval system for traumatic brain injury

NASA Astrophysics Data System (ADS)

Li, Shimiao; Gong, Tianxia; Wang, Jie; Liu, Ruizhe; Tan, Chew Lim; Leong, Tze Yun; Pang, Boon Chuan; Lim, C. C. Tchoyoson; Lee, Cheng Kiang; Tian, Qi; Zhang, Zhuo

2010-03-01

Traumatic brain injury (TBI) is a major cause of death and disability. Computed Tomography (CT) scan is widely used in the diagnosis of TBI. Nowadays, large amount of TBI CT data is stacked in the hospital radiology department. Such data and the associated patient information contain valuable information for clinical diagnosis and outcome prediction. However, current hospital database system does not provide an efficient and intuitive tool for doctors to search out cases relevant to the current study case. In this paper, we present the TBIdoc system: a content-based image retrieval (CBIR) system which works on the TBI CT images. In this web-based system, user can query by uploading CT image slices from one study, retrieval result is a list of TBI cases ranked according to their 3D visual similarity to the query case. Specifically, cases of TBI CT images often present diffuse or focal lesions. In TBIdoc system, these pathological image features are represented as bin-based binary feature vectors. We use the Jaccard-Needham measure as the similarity measurement. Based on these, we propose a 3D similarity measure for computing the similarity score between two series of CT slices. nDCG is used to evaluate the system performance, which shows the system produces satisfactory retrieval results. The system is expected to improve the current hospital data management in TBI and to give better support for the clinical decision-making process. It may also contribute to the computer-aided education in TBI.

Mood and the reliance on the ease of retrieval heuristic.

PubMed

Ruder, Markus; Bless, Herbert

2003-07-01

Four studies investigate the relationship between individuals' mood and their reliance on the ease retrieval heuristic. Happy participants were consistently more likely to rely on the ease of retrieval heuristic, whereas sad participants were more likely to rely on the activated content. Additional analyses indicate that this pattern is not due to a differential recall (Experiment 2) and that happy participants ceased to rely on the ease of retrieval when the diagnosticity of this information was called into question (Experiment 3). Experiment 4 shows that reliance on the ease of retrieval heuristic resulted in faster judgments than reliance on content, with the former but not the latter being a function of the amount of activated information.

iPixel: a visual content-based and semantic search engine for retrieving digitized mammograms by using collective intelligence.

PubMed

Alor-Hernández, Giner; Pérez-Gallardo, Yuliana; Posada-Gómez, Rubén; Cortes-Robles, Guillermo; Rodríguez-González, Alejandro; Aguilar-Laserre, Alberto A

2012-09-01

Nowadays, traditional search engines such as Google, Yahoo and Bing facilitate the retrieval of information in the format of images, but the results are not always useful for the users. This is mainly due to two problems: (1) the semantic keywords are not taken into consideration and (2) it is not always possible to establish a query using the image features. This issue has been covered in different domains in order to develop content-based image retrieval (CBIR) systems. The expert community has focussed their attention on the healthcare domain, where a lot of visual information for medical analysis is available. This paper provides a solution called iPixel Visual Search Engine, which involves semantics and content issues in order to search for digitized mammograms. iPixel offers the possibility of retrieving mammogram features using collective intelligence and implementing a CBIR algorithm. Our proposal compares not only features with similar semantic meaning, but also visual features. In this sense, the comparisons are made in different ways: by the number of regions per image, by maximum and minimum size of regions per image and by average intensity level of each region. iPixel Visual Search Engine supports the medical community in differential diagnoses related to the diseases of the breast. The iPixel Visual Search Engine has been validated by experts in the healthcare domain, such as radiologists, in addition to experts in digital image analysis.

Enhanced Information Retrieval Using AJAX

NASA Astrophysics Data System (ADS)

Kachhwaha, Rajendra; Rajvanshi, Nitin

2010-11-01

Information Retrieval deals with the representation, storage, organization of, and access to information items. The representation and organization of information items should provide the user with easy access to the information with the rapid development of Internet, large amounts of digitally stored information is readily available on the World Wide Web. This information is so huge that it becomes increasingly difficult and time consuming for the users to find the information relevant to their needs. The explosive growth of information on the Internet has greatly increased the need for information retrieval systems. However, most of the search engines are using conventional information retrieval systems. An information system needs to implement sophisticated pattern matching tools to determine contents at a faster rate. AJAX has recently emerged as the new tool such the of information retrieval process of information retrieval can become fast and information reaches the use at a faster pace as compared to conventional retrieval systems.

Semantic-based surveillance video retrieval.

PubMed

Hu, Weiming; Xie, Dan; Fu, Zhouyu; Zeng, Wenrong; Maybank, Steve

2007-04-01

Visual surveillance produces large amounts of video data. Effective indexing and retrieval from surveillance video databases are very important. Although there are many ways to represent the content of video clips in current video retrieval algorithms, there still exists a semantic gap between users and retrieval systems. Visual surveillance systems supply a platform for investigating semantic-based video retrieval. In this paper, a semantic-based video retrieval framework for visual surveillance is proposed. A cluster-based tracking algorithm is developed to acquire motion trajectories. The trajectories are then clustered hierarchically using the spatial and temporal information, to learn activity models. A hierarchical structure of semantic indexing and retrieval of object activities, where each individual activity automatically inherits all the semantic descriptions of the activity model to which it belongs, is proposed for accessing video clips and individual objects at the semantic level. The proposed retrieval framework supports various queries including queries by keywords, multiple object queries, and queries by sketch. For multiple object queries, succession and simultaneity restrictions, together with depth and breadth first orders, are considered. For sketch-based queries, a method for matching trajectories drawn by users to spatial trajectories is proposed. The effectiveness and efficiency of our framework are tested in a crowded traffic scene.

A User-Centered Approach to Adaptive Hypertext Based on an Information Relevance Model

NASA Technical Reports Server (NTRS)

Mathe, Nathalie; Chen, James

1994-01-01

Rapid and effective to information in large electronic documentation systems can be facilitated if information relevant in an individual user's content can be automatically supplied to this user. However most of this knowledge on contextual relevance is not found within the contents of documents, it is rather established incrementally by users during information access. We propose a new model for interactively learning contextual relevance during information retrieval, and incrementally adapting retrieved information to individual user profiles. The model, called a relevance network, records the relevance of references based on user feedback for specific queries and user profiles. It also generalizes such knowledge to later derive relevant references for similar queries and profiles. The relevance network lets users filter information by context of relevance. Compared to other approaches, it does not require any prior knowledge nor training. More importantly, our approach to adaptivity is user-centered. It facilitates acceptance and understanding by users by giving them shared control over the adaptation without disturbing their primary task. Users easily control when to adapt and when to use the adapted system. Lastly, the model is independent of the particular application used to access information, and supports sharing of adaptations among users.

Integration of Information Retrieval and Database Management Systems.

ERIC Educational Resources Information Center

Deogun, Jitender S.; Raghavan, Vijay V.

1988-01-01

Discusses the motivation for integrating information retrieval and database management systems, and proposes a probabilistic retrieval model in which records in a file may be composed of attributes (formatted data items) and descriptors (content indicators). The details and resolutions of difficulties involved in integrating such systems are…

Content-based unconstrained color logo and trademark retrieval with color edge gradient co-occurrence histograms

NASA Astrophysics Data System (ADS)

Phan, Raymond; Androutsos, Dimitrios

2008-01-01

In this paper, we present a logo and trademark retrieval system for unconstrained color image databases that extends the Color Edge Co-occurrence Histogram (CECH) object detection scheme. We introduce more accurate information to the CECH, by virtue of incorporating color edge detection using vector order statistics. This produces a more accurate representation of edges in color images, in comparison to the simple color pixel difference classification of edges as seen in the CECH. Our proposed method is thus reliant on edge gradient information, and as such, we call this the Color Edge Gradient Co-occurrence Histogram (CEGCH). We use this as the main mechanism for our unconstrained color logo and trademark retrieval scheme. Results illustrate that the proposed retrieval system retrieves logos and trademarks with good accuracy, and outperforms the CECH object detection scheme with higher precision and recall.

Retrieving Patent Information Online

ERIC Educational Resources Information Center

Kaback, Stuart M.

1978-01-01

This paper discusses patent information retrieval from online files in terms of types of questions, file contents, coverage, timeliness, and other file variations. CLAIMS, Derwent, WPI, APIPAT and Chemical Abstracts Service are described. (KP)

New frontiers for intelligent content-based retrieval

NASA Astrophysics Data System (ADS)

Benitez, Ana B.; Smith, John R.

2001-01-01

In this paper, we examine emerging frontiers in the evolution of content-based retrieval systems that rely on an intelligent infrastructure. Here, we refer to intelligence as the capabilities of the systems to build and maintain situational or world models, utilize dynamic knowledge representation, exploit context, and leverage advanced reasoning and learning capabilities. We argue that these elements are essential to producing effective systems for retrieving audio-visual content at semantic levels matching those of human perception and cognition. In this paper, we review relevant research on the understanding of human intelligence and construction of intelligent system in the fields of cognitive psychology, artificial intelligence, semiotics, and computer vision. We also discus how some of the principal ideas form these fields lead to new opportunities and capabilities for content-based retrieval systems. Finally, we describe some of our efforts in these directions. In particular, we present MediaNet, a multimedia knowledge presentation framework, and some MPEG-7 description tools that facilitate and enable intelligent content-based retrieval.

New frontiers for intelligent content-based retrieval

NASA Astrophysics Data System (ADS)

Benitez, Ana B.; Smith, John R.

2000-12-01

In this paper, we examine emerging frontiers in the evolution of content-based retrieval systems that rely on an intelligent infrastructure. Here, we refer to intelligence as the capabilities of the systems to build and maintain situational or world models, utilize dynamic knowledge representation, exploit context, and leverage advanced reasoning and learning capabilities. We argue that these elements are essential to producing effective systems for retrieving audio-visual content at semantic levels matching those of human perception and cognition. In this paper, we review relevant research on the understanding of human intelligence and construction of intelligent system in the fields of cognitive psychology, artificial intelligence, semiotics, and computer vision. We also discus how some of the principal ideas form these fields lead to new opportunities and capabilities for content-based retrieval systems. Finally, we describe some of our efforts in these directions. In particular, we present MediaNet, a multimedia knowledge presentation framework, and some MPEG-7 description tools that facilitate and enable intelligent content-based retrieval.

Active retrieval facilitates across-episode binding by modulating the content of memory.

PubMed

Bridge, Donna J; Voss, Joel L

2014-10-01

The contents of memory can be updated when information from the current episode is bound with content retrieved from previous episodes. Little is known regarding factors that determine the memory content that is subject to this across-episode binding. We tested whether across-episode binding preferentially occurs for memory content that is currently "active" and identified relevant neural correlates. After studying objects at specific locations on scene backgrounds, subjects performed one of two retrieval tasks for the objects on different scene backgrounds. In an active condition, subjects recalled object locations, whereas subjects merely dragged objects to predetermined locations in a passive condition. Immediately following each object-location retrieval event, a novel face appeared on a blank screen. We hypothesized that the original episode content would be active in memory during face encoding in the active condition, but not in the passive condition (despite seeing the same content in both conditions). A ramification of the active condition would thus be preferential binding of original episode content to novel faces, with no such across-episode binding in the passive condition. Indeed, memory for faces was better when tested on the original background scenes in the active relative to passive condition, indicating that original episode content was bound with the active condition faces, whereas this occurred to a lesser extent for the passive condition faces. Likewise, early-onset negative ERP effects reflected binding of the face to the original episode content in the active but not the passive condition. In contrast, binding in the passive condition occurred only when faces were physically displayed on the original scenes during recognition testing, and a very similar early-onset negative ERP effect signaled binding in this condition. ERP correlates of binding were thus similar for across-episode and within-episode binding (and were distinct from other encoding and retrieval ERP signals in both cases), indicating that active retrieval modulated when binding occurred, not the nature of the binding process per se. These results suggest that active retrieval promotes binding of new information with contents of memory, whereas without active retrieval, these unrelated pieces of information might be bound only when they are physically paired. Copyright © 2014 Elsevier Ltd. All rights reserved.

Information Retrieval and the Philosophy of Language.

ERIC Educational Resources Information Center

Blair, David C.

2003-01-01

Provides an overview of some of the main ideas in the philosophy of language that have relevance to the issues of information retrieval, focusing on the description of the intellectual content. Highlights include retrieval problems; recall and precision; words and meanings; context; externalism and the philosophy of language; and scaffolding and…

Retrieval of Aerosol Microphysical Properties Based on the Optimal Estimation Method: Information Content Analysis for Satellite Polarimetric Remote Sensing Measurements

NASA Astrophysics Data System (ADS)

Hou, W. Z.; Li, Z. Q.; Zheng, F. X.; Qie, L. L.

2018-04-01

This paper evaluates the information content for the retrieval of key aerosol microphysical and surface properties for multispectral single-viewing satellite polarimetric measurements cantered at 410, 443, 555, 670, 865, 1610 and 2250 nm over bright land. To conduct the information content analysis, the synthetic data are simulated by the Unified Linearized Vector Radiative Transfer Model (UNLVTM) with the intensity and polarization together over bare soil surface for various scenarios. Following the optimal estimation theory, a principal component analysis method is employed to reconstruct the multispectral surface reflectance from 410 nm to 2250 nm, and then integrated with a linear one-parametric BPDF model to represent the contribution of polarized surface reflectance, thus further to decouple the surface-atmosphere contribution from the TOA measurements. Focusing on two different aerosol models with the aerosol optical depth equal to 0.8 at 550 nm, the total DFS and DFS component of each retrieval aerosol and surface parameter are analysed. The DFS results show that the key aerosol microphysical properties, such as the fine- and coarse-mode columnar volume concentration, the effective radius and the real part of complex refractive index at 550 nm, could be well retrieved with the surface parameters simultaneously over bare soil surface type. The findings of this study can provide the guidance to the inversion algorithm development over bright surface land by taking full use of the single-viewing satellite polarimetric measurements.

Comparison of Effects of Different Forms of Presentation on the Recall and Retrieval of Information.

ERIC Educational Resources Information Center

Jonassen, David H.; Pace, Ann Jaffe

A study compared the relative effects of typographically cued or mapped text, intact text with signaling, and intact text without signaling on the recall and retrieval of information from prose passages. (Signaling, a noncontent aspect of prose, emphasizes certain aspects of the semantic content or points out aspects of the structure of content.)…

User-oriented evaluation of a medical image retrieval system for radiologists.

PubMed

Markonis, Dimitrios; Holzer, Markus; Baroz, Frederic; De Castaneda, Rafael Luis Ruiz; Boyer, Célia; Langs, Georg; Müller, Henning

2015-10-01

This article reports the user-oriented evaluation of a text- and content-based medical image retrieval system. User tests with radiologists using a search system for images in the medical literature are presented. The goal of the tests is to assess the usability of the system, identify system and interface aspects that need improvement and useful additions. Another objective is to investigate the system's added value to radiology information retrieval. The study provides an insight into required specifications and potential shortcomings of medical image retrieval systems through a concrete methodology for conducting user tests. User tests with a working image retrieval system of images from the biomedical literature were performed in an iterative manner, where each iteration had the participants perform radiology information seeking tasks and then refining the system as well as the user study design itself. During these tasks the interaction of the users with the system was monitored, usability aspects were measured, retrieval success rates recorded and feedback was collected through survey forms. In total, 16 radiologists participated in the user tests. The success rates in finding relevant information were on average 87% and 78% for image and case retrieval tasks, respectively. The average time for a successful search was below 3 min in both cases. Users felt quickly comfortable with the novel techniques and tools (after 5 to 15 min), such as content-based image retrieval and relevance feedback. User satisfaction measures show a very positive attitude toward the system's functionalities while the user feedback helped identifying the system's weak points. The participants proposed several potentially useful new functionalities, such as filtering by imaging modality and search for articles using image examples. The iterative character of the evaluation helped to obtain diverse and detailed feedback on all system aspects. Radiologists are quickly familiar with the functionalities but have several comments on desired functionalities. The analysis of the results can potentially assist system refinement for future medical information retrieval systems. Moreover, the methodology presented as well as the discussion on the limitations and challenges of such studies can be useful for user-oriented medical image retrieval evaluation, as user-oriented evaluation of interactive system is still only rarely performed. Such interactive evaluations can be limited in effort if done iteratively and can give many insights for developing better systems. Copyright © 2015. Published by Elsevier Ireland Ltd.

VidCat: an image and video analysis service for personal media management

NASA Astrophysics Data System (ADS)

Begeja, Lee; Zavesky, Eric; Liu, Zhu; Gibbon, David; Gopalan, Raghuraman; Shahraray, Behzad

2013-03-01

Cloud-based storage and consumption of personal photos and videos provides increased accessibility, functionality, and satisfaction for mobile users. One cloud service frontier that is recently growing is that of personal media management. This work presents a system called VidCat that assists users in the tagging, organization, and retrieval of their personal media by faces and visual content similarity, time, and date information. Evaluations for the effectiveness of the copy detection and face recognition algorithms on standard datasets are also discussed. Finally, the system includes a set of application programming interfaces (API's) allowing content to be uploaded, analyzed, and retrieved on any client with simple HTTP-based methods as demonstrated with a prototype developed on the iOS and Android mobile platforms.

Self-adaptive relevance feedback based on multilevel image content analysis

NASA Astrophysics Data System (ADS)

Gao, Yongying; Zhang, Yujin; Fu, Yu

2001-01-01

In current content-based image retrieval systems, it is generally accepted that obtaining high-level image features is a key to improve the querying. Among the related techniques, relevance feedback has become a hot research aspect because it combines the information from the user to refine the querying results. In practice, many methods have been proposed to achieve the goal of relevance feedback. In this paper, a new scheme for relevance feedback is proposed. Unlike previous methods for relevance feedback, our scheme provides a self-adaptive operation. First, based on multi- level image content analysis, the relevant images from the user could be automatically analyzed in different levels and the querying could be modified in terms of different analysis results. Secondly, to make it more convenient to the user, the procedure of relevance feedback could be led with memory or without memory. To test the performance of the proposed method, a practical semantic-based image retrieval system has been established, and the querying results gained by our self-adaptive relevance feedback are given.

Self-adaptive relevance feedback based on multilevel image content analysis

NASA Astrophysics Data System (ADS)

Gao, Yongying; Zhang, Yujin; Fu, Yu

2000-12-01

In current content-based image retrieval systems, it is generally accepted that obtaining high-level image features is a key to improve the querying. Among the related techniques, relevance feedback has become a hot research aspect because it combines the information from the user to refine the querying results. In practice, many methods have been proposed to achieve the goal of relevance feedback. In this paper, a new scheme for relevance feedback is proposed. Unlike previous methods for relevance feedback, our scheme provides a self-adaptive operation. First, based on multi- level image content analysis, the relevant images from the user could be automatically analyzed in different levels and the querying could be modified in terms of different analysis results. Secondly, to make it more convenient to the user, the procedure of relevance feedback could be led with memory or without memory. To test the performance of the proposed method, a practical semantic-based image retrieval system has been established, and the querying results gained by our self-adaptive relevance feedback are given.

Techniques for Soundscape Retrieval and Synthesis

NASA Astrophysics Data System (ADS)

Mechtley, Brandon Michael

The study of acoustic ecology is concerned with the manner in which life interacts with its environment as mediated through sound. As such, a central focus is that of the soundscape: the acoustic environment as perceived by a listener. This dissertation examines the application of several computational tools in the realms of digital signal processing, multimedia information retrieval, and computer music synthesis to the analysis of the soundscape. Namely, these tools include a) an open source software library, Sirens, which can be used for the segmentation of long environmental field recordings into individual sonic events and compare these events in terms of acoustic content, b) a graph-based retrieval system that can use these measures of acoustic similarity and measures of semantic similarity using the lexical database WordNet to perform both text-based retrieval and automatic annotation of environmental sounds, and c) new techniques for the dynamic, realtime parametric morphing of multiple field recordings, informed by the geographic paths along which they were recorded.

Using deep learning for content-based medical image retrieval

NASA Astrophysics Data System (ADS)

Sun, Qinpei; Yang, Yuanyuan; Sun, Jianyong; Yang, Zhiming; Zhang, Jianguo

2017-03-01

Content-Based medical image retrieval (CBMIR) is been highly active research area from past few years. The retrieval performance of a CBMIR system crucially depends on the feature representation, which have been extensively studied by researchers for decades. Although a variety of techniques have been proposed, it remains one of the most challenging problems in current CBMIR research, which is mainly due to the well-known "semantic gap" issue that exists between low-level image pixels captured by machines and high-level semantic concepts perceived by human[1]. Recent years have witnessed some important advances of new techniques in machine learning. One important breakthrough technique is known as "deep learning". Unlike conventional machine learning methods that are often using "shallow" architectures, deep learning mimics the human brain that is organized in a deep architecture and processes information through multiple stages of transformation and representation. This means that we do not need to spend enormous energy to extract features manually. In this presentation, we propose a novel framework which uses deep learning to retrieval the medical image to improve the accuracy and speed of a CBIR in integrated RIS/PACS.

Evaluation of contents-based image retrieval methods for a database of logos on drug tablets

NASA Astrophysics Data System (ADS)

Geradts, Zeno J.; Hardy, Huub; Poortman, Anneke; Bijhold, Jurrien

2001-02-01

In this research an evaluation has been made of the different ways of contents based image retrieval of logos of drug tablets. On a database of 432 illicitly produced tablets (mostly containing MDMA), we have compared different retrieval methods. Two of these methods were available from commercial packages, QBIC and Imatch, where the implementation of the contents based image retrieval methods are not exactly known. We compared the results for this database with the MPEG-7 shape comparison methods, which are the contour-shape, bounding box and region-based shape methods. In addition, we have tested the log polar method that is available from our own research.

Medical Content Searching, Retrieving, and Sharing Over the Internet: Lessons Learned From the mEducator Through a Scenario-Based Evaluation

PubMed Central

Spachos, Dimitris; Mylläri, Jarkko; Giordano, Daniela; Dafli, Eleni; Mitsopoulou, Evangelia; Schizas, Christos N; Pattichis, Constantinos; Nikolaidou, Maria

2015-01-01

Background The mEducator Best Practice Network (BPN) implemented and extended standards and reference models in e-learning to develop innovative frameworks as well as solutions that enable specialized state-of-the-art medical educational content to be discovered, retrieved, shared, and re-purposed across European Institutions, targeting medical students, doctors, educators and health care professionals. Scenario-based evaluation for usability testing, complemented with data from online questionnaires and field notes of users’ performance, was designed and utilized for the evaluation of these solutions. Objective The objective of this work is twofold: (1) to describe one instantiation of the mEducator BPN solutions (mEducator3.0 - “MEdical Education LINnked Arena” MELINA+) with a focus on the metadata schema used, as well as on other aspects of the system that pertain to usability and acceptance, and (2) to present evaluation results on the suitability of the proposed metadata schema for searching, retrieving, and sharing of medical content and with respect to the overall usability and acceptance of the system from the target users. Methods A comprehensive evaluation methodology framework was developed and applied to four case studies, which were conducted in four different countries (ie, Greece, Cyprus, Bulgaria and Romania), with a total of 126 participants. In these case studies, scenarios referring to creating, sharing, and retrieving medical educational content using mEducator3.0 were used. The data were collected through two online questionnaires, consisting of 36 closed-ended questions and two open-ended questions that referred to mEducator 3.0 and through the use of field notes during scenario-based evaluations. Results The main findings of the study showed that even though the informational needs of the mEducator target groups were addressed to a satisfactory extent and the metadata schema supported content creation, sharing, and retrieval from an end-user perspective, users faced difficulties in achieving a shared understanding of the meaning of some metadata fields and in correctly managing the intellectual property rights of repurposed content. Conclusions The results of this evaluation impact researchers, medical professionals, and designers interested in using similar systems for educational content sharing in medical and other domains. Recommendations on how to improve the search, retrieval, identification, and obtaining of medical resources are provided, by addressing issues of content description metadata, content description procedures, and intellectual property rights for re-purposed content. PMID:26453250

Evaluation of information-theoretic similarity measures for content-based retrieval and detection of masses in mammograms

DOE Office of Scientific and Technical Information (OSTI.GOV)

Tourassi, Georgia D.; Harrawood, Brian; Singh, Swatee

The purpose of this study was to evaluate image similarity measures employed in an information-theoretic computer-assisted detection (IT-CAD) scheme. The scheme was developed for content-based retrieval and detection of masses in screening mammograms. The study is aimed toward an interactive clinical paradigm where physicians query the proposed IT-CAD scheme on mammographic locations that are either visually suspicious or indicated as suspicious by other cuing CAD systems. The IT-CAD scheme provides an evidence-based, second opinion for query mammographic locations using a knowledge database of mass and normal cases. In this study, eight entropy-based similarity measures were compared with respect to retrievalmore » precision and detection accuracy using a database of 1820 mammographic regions of interest. The IT-CAD scheme was then validated on a separate database for false positive reduction of progressively more challenging visual cues generated by an existing, in-house mass detection system. The study showed that the image similarity measures fall into one of two categories; one category is better suited to the retrieval of semantically similar cases while the second is more effective with knowledge-based decisions regarding the presence of a true mass in the query location. In addition, the IT-CAD scheme yielded a substantial reduction in false-positive detections while maintaining high detection rate for malignant masses.« less

Audio-based queries for video retrieval over Java enabled mobile devices

NASA Astrophysics Data System (ADS)

Ahmad, Iftikhar; Cheikh, Faouzi Alaya; Kiranyaz, Serkan; Gabbouj, Moncef

2006-02-01

In this paper we propose a generic framework for efficient retrieval of audiovisual media based on its audio content. This framework is implemented in a client-server architecture where the client application is developed in Java to be platform independent whereas the server application is implemented for the PC platform. The client application adapts to the characteristics of the mobile device where it runs such as screen size and commands. The entire framework is designed to take advantage of the high-level segmentation and classification of audio content to improve speed and accuracy of audio-based media retrieval. Therefore, the primary objective of this framework is to provide an adaptive basis for performing efficient video retrieval operations based on the audio content and types (i.e. speech, music, fuzzy and silence). Experimental results approve that such an audio based video retrieval scheme can be used from mobile devices to search and retrieve video clips efficiently over wireless networks.

Combining Passive Microwave Sounders with CYGNSS information for improved retrievals: Observations during Hurricane Harvey

NASA Astrophysics Data System (ADS)

Schreier, M. M.

2017-12-01

The launch of CYGNSS (Cyclone Global Navigation Satellite System) has added an interesting component to satellite observations: it can provide wind speeds in the tropical area with a high repetition rate. Passive microwave sounders that are overpassing the same region can benefit from this information, when it comes to the retrieval of temperature or water profiles: the uncertainty about wind speeds has a strong impact on emissivity and reflectivity calculations with respect to surface temperature. This has strong influences on the uncertainty of retrieval of temperature and water content, especially under extreme weather conditions. Adding CYGNSS information to the retrieval can help to reduce errors and provide a significantly better sounder retrieval. Based on observations during Hurricane Harvey, we want to show the impact of CYGNSS data on the retrieval of passive microwave sensors. We will show examples on the impact on the retrieval from polar orbiting instruments, like the Advanced Technology Microwave Sounder (ATMS) and AMSU-A/B on NOAA-18 and 19. In addition we will also show the impact on retrievals from HAMSR (High Altitude MMIC Sounding Radiometer), which was flying on the Global Hawk during the EPOCH campaign. We will compare the results with other observations and estimate the impact of additional CYGNSS information on the microwave retrieval, especially on the impact in error and uncertainty reduction. We think, that a synergetic use of these different data sources could significantly help to produce better assimilation products for forecast assimilation.

Recommending Education Materials for Diabetic Questions Using Information Retrieval Approaches

PubMed Central

Wang, Yanshan; Shen, Feichen; Liu, Sijia; Rastegar-Mojarad, Majid; Wang, Liwei

2017-01-01

Background Self-management is crucial to diabetes care and providing expert-vetted content for answering patients’ questions is crucial in facilitating patient self-management. Objective The aim is to investigate the use of information retrieval techniques in recommending patient education materials for diabetic questions of patients. Methods We compared two retrieval algorithms, one based on Latent Dirichlet Allocation topic modeling (topic modeling-based model) and one based on semantic group (semantic group-based model), with the baseline retrieval models, vector space model (VSM), in recommending diabetic patient education materials to diabetic questions posted on the TuDiabetes forum. The evaluation was based on a gold standard dataset consisting of 50 randomly selected diabetic questions where the relevancy of diabetic education materials to the questions was manually assigned by two experts. The performance was assessed using precision of top-ranked documents. Results We retrieved 7510 diabetic questions on the forum and 144 diabetic patient educational materials from the patient education database at Mayo Clinic. The mapping rate of words in each corpus mapped to the Unified Medical Language System (UMLS) was significantly different (P<.001). The topic modeling-based model outperformed the other retrieval algorithms. For example, for the top-retrieved document, the precision of the topic modeling-based, semantic group-based, and VSM models was 67.0%, 62.8%, and 54.3%, respectively. Conclusions This study demonstrated that topic modeling can mitigate the vocabulary difference and it achieved the best performance in recommending education materials for answering patients’ questions. One direction for future work is to assess the generalizability of our findings and to extend our study to other disease areas, other patient education material resources, and online forums. PMID:29038097

Scalable ranked retrieval using document images

NASA Astrophysics Data System (ADS)

Jain, Rajiv; Oard, Douglas W.; Doermann, David

2013-12-01

Despite the explosion of text on the Internet, hard copy documents that have been scanned as images still play a significant role for some tasks. The best method to perform ranked retrieval on a large corpus of document images, however, remains an open research question. The most common approach has been to perform text retrieval using terms generated by optical character recognition. This paper, by contrast, examines whether a scalable segmentation-free image retrieval algorithm, which matches sub-images containing text or graphical objects, can provide additional benefit in satisfying a user's information needs on a large, real world dataset. Results on 7 million scanned pages from the CDIP v1.0 test collection show that content based image retrieval finds a substantial number of documents that text retrieval misses, and that when used as a basis for relevance feedback can yield improvements in retrieval effectiveness.

Biomedical information retrieval across languages.

PubMed

Daumke, Philipp; Markü, Kornél; Poprat, Michael; Schulz, Stefan; Klar, Rüdiger

2007-06-01

This work presents a new dictionary-based approach to biomedical cross-language information retrieval (CLIR) that addresses many of the general and domain-specific challenges in current CLIR research. Our method is based on a multilingual lexicon that was generated partly manually and partly automatically, and currently covers six European languages. It contains morphologically meaningful word fragments, termed subwords. Using subwords instead of entire words significantly reduces the number of lexical entries necessary to sufficiently cover a specific language and domain. Mediation between queries and documents is based on these subwords as well as on lists of word-n-grams that are generated from large monolingual corpora and constitute possible translation units. The translations are then sent to a standard Internet search engine. This process makes our approach an effective tool for searching the biomedical content of the World Wide Web in different languages. We evaluate this approach using the OHSUMED corpus, a large medical document collection, within a cross-language retrieval setting.

Texture characterization for joint compression and classification based on human perception in the wavelet domain.

PubMed

Fahmy, Gamal; Black, John; Panchanathan, Sethuraman

2006-06-01

Today's multimedia applications demand sophisticated compression and classification techniques in order to store, transmit, and retrieve audio-visual information efficiently. Over the last decade, perceptually based image compression methods have been gaining importance. These methods take into account the abilities (and the limitations) of human visual perception (HVP) when performing compression. The upcoming MPEG 7 standard also addresses the need for succinct classification and indexing of visual content for efficient retrieval. However, there has been no research that has attempted to exploit the characteristics of the human visual system to perform both compression and classification jointly. One area of HVP that has unexplored potential for joint compression and classification is spatial frequency perception. Spatial frequency content that is perceived by humans can be characterized in terms of three parameters, which are: 1) magnitude; 2) phase; and 3) orientation. While the magnitude of spatial frequency content has been exploited in several existing image compression techniques, the novel contribution of this paper is its focus on the use of phase coherence for joint compression and classification in the wavelet domain. Specifically, this paper describes a human visual system-based method for measuring the degree to which an image contains coherent (perceptible) phase information, and then exploits that information to provide joint compression and classification. Simulation results that demonstrate the efficiency of this method are presented.

Hyperspectral remote sensing image retrieval system using spectral and texture features.

PubMed

Zhang, Jing; Geng, Wenhao; Liang, Xi; Li, Jiafeng; Zhuo, Li; Zhou, Qianlan

2017-06-01

Although many content-based image retrieval systems have been developed, few studies have focused on hyperspectral remote sensing images. In this paper, a hyperspectral remote sensing image retrieval system based on spectral and texture features is proposed. The main contributions are fourfold: (1) considering the "mixed pixel" in the hyperspectral image, endmembers as spectral features are extracted by an improved automatic pixel purity index algorithm, then the texture features are extracted with the gray level co-occurrence matrix; (2) similarity measurement is designed for the hyperspectral remote sensing image retrieval system, in which the similarity of spectral features is measured with the spectral information divergence and spectral angle match mixed measurement and in which the similarity of textural features is measured with Euclidean distance; (3) considering the limited ability of the human visual system, the retrieval results are returned after synthesizing true color images based on the hyperspectral image characteristics; (4) the retrieval results are optimized by adjusting the feature weights of similarity measurements according to the user's relevance feedback. The experimental results on NASA data sets can show that our system can achieve comparable superior retrieval performance to existing hyperspectral analysis schemes.

A neotropical Miocene pollen database employing image-based search and semantic modeling.

PubMed

Han, Jing Ginger; Cao, Hongfei; Barb, Adrian; Punyasena, Surangi W; Jaramillo, Carlos; Shyu, Chi-Ren

2014-08-01

Digital microscopic pollen images are being generated with increasing speed and volume, producing opportunities to develop new computational methods that increase the consistency and efficiency of pollen analysis and provide the palynological community a computational framework for information sharing and knowledge transfer. • Mathematical methods were used to assign trait semantics (abstract morphological representations) of the images of neotropical Miocene pollen and spores. Advanced database-indexing structures were built to compare and retrieve similar images based on their visual content. A Web-based system was developed to provide novel tools for automatic trait semantic annotation and image retrieval by trait semantics and visual content. • Mathematical models that map visual features to trait semantics can be used to annotate images with morphology semantics and to search image databases with improved reliability and productivity. Images can also be searched by visual content, providing users with customized emphases on traits such as color, shape, and texture. • Content- and semantic-based image searches provide a powerful computational platform for pollen and spore identification. The infrastructure outlined provides a framework for building a community-wide palynological resource, streamlining the process of manual identification, analysis, and species discovery.

Improved image retrieval based on fuzzy colour feature vector

NASA Astrophysics Data System (ADS)

Ben-Ahmeida, Ahlam M.; Ben Sasi, Ahmed Y.

2013-03-01

One of Image indexing techniques is the Content-Based Image Retrieval which is an efficient way for retrieving images from the image database automatically based on their visual contents such as colour, texture, and shape. In this paper will be discuss how using content-based image retrieval (CBIR) method by colour feature extraction and similarity checking. By dividing the query image and all images in the database into pieces and extract the features of each part separately and comparing the corresponding portions in order to increase the accuracy in the retrieval. The proposed approach is based on the use of fuzzy sets, to overcome the problem of curse of dimensionality. The contribution of colour of each pixel is associated to all the bins in the histogram using fuzzy-set membership functions. As a result, the Fuzzy Colour Histogram (FCH), outperformed the Conventional Colour Histogram (CCH) in image retrieving, due to its speedy results, where were images represented as signatures that took less size of memory, depending on the number of divisions. The results also showed that FCH is less sensitive and more robust to brightness changes than the CCH with better retrieval recall values.

The Many Facets and Applications of Text Structure in Supporting Educational Trajectories of Elementary and Middle Grade Children in Content Area Reading Comprehension and Writing

ERIC Educational Resources Information Center

Albro, Elizabeth; Williams, Joanna P.; Wijekumar, Kausalai; Meyer, Bonnie J. F.; Harris, Karen R.

2015-01-01

Content area reading comprehension and writing have been a challenge for children in the U.S. schools for many years as evidenced by state and national assessments. One promising solution to the problem is text structure based instruction that promotes strategic selection, encoding, retrieval, and use of information for myriads of activities…

Cloud information content analysis of multi-angular measurements in the oxygen A-band: application to 3MI and MSPI

NASA Astrophysics Data System (ADS)

Merlin, Guillaume; Riedi, Jérôme; Labonnote, Laurent C.; Cornet, Céline; Davis, Anthony B.; Dubuisson, Phillipe; Desmons, Marine; Ferlay, Nicolas; Parol, Frédéric

2016-10-01

Information content analyses on cloud top altitude (CTOP) and geometrical thickness (CGT) from multi-angular A-band measurements in the case of monolayer homogeneous clouds are conducted. In the framework of future multi-angular radiometer development, we compared the potential performances of the 3MI (Multi-viewing, Multi-channel and Multi-polarization Imaging) instrument developed by EUMETSAT, which is an extension of POLDER/PARASOL instrument and MSPI (Multiangle SpectroPolarimetric Imager) developed by NASA's Jet Propulsion Laboratory. Quantitative information content estimates were realized for thin, moderately opaque and opaque clouds for different surface albedo and viewing geometry configurations. Analyses show that retrieval of CTOP is possible with a high accuracy in most of the cases investigated. Retrieval of CGT is also possible for optically thick clouds above a black surface, at least when CGT > 1-2 km and for thin clouds for CGT > 2-3 km. However, for intermediate optical thicknesses (COT ≃ 4), we show that the retrieval of CGT is not simultaneously possible with CTOP. A comparison between 3MI and MSPI shows a higher information content for MSPI's measurements, traceable to a thinner filter inside the oxygen A-band, yielding higher signal-to-noise ratio for absorption estimation. Cases of cloud scenes above bright surfaces are more complex but it is shown that the retrieval of CTOP remains possible in almost all situations while the information content on CGT appears to be insufficient in many cases, particularly for COT < 4 and CGT < 2-3 km.

Bridging the integration gap between imaging and information systems: a uniform data concept for content-based image retrieval in computer-aided diagnosis.

PubMed

Welter, Petra; Riesmeier, Jörg; Fischer, Benedikt; Grouls, Christoph; Kuhl, Christiane; Deserno, Thomas M

2011-01-01

It is widely accepted that content-based image retrieval (CBIR) can be extremely useful for computer-aided diagnosis (CAD). However, CBIR has not been established in clinical practice yet. As a widely unattended gap of integration, a unified data concept for CBIR-based CAD results and reporting is lacking. Picture archiving and communication systems and the workflow of radiologists must be considered for successful data integration to be achieved. We suggest that CBIR systems applied to CAD should integrate their results in a picture archiving and communication systems environment such as Digital Imaging and Communications in Medicine (DICOM) structured reporting documents. A sample DICOM structured reporting template adaptable to CBIR and an appropriate integration scheme is presented. The proposed CBIR data concept may foster the promulgation of CBIR systems in clinical environments and, thereby, improve the diagnostic process.

Bridging the integration gap between imaging and information systems: a uniform data concept for content-based image retrieval in computer-aided diagnosis

PubMed Central

Riesmeier, Jörg; Fischer, Benedikt; Grouls, Christoph; Kuhl, Christiane; Deserno (né Lehmann), Thomas M

2011-01-01

It is widely accepted that content-based image retrieval (CBIR) can be extremely useful for computer-aided diagnosis (CAD). However, CBIR has not been established in clinical practice yet. As a widely unattended gap of integration, a unified data concept for CBIR-based CAD results and reporting is lacking. Picture archiving and communication systems and the workflow of radiologists must be considered for successful data integration to be achieved. We suggest that CBIR systems applied to CAD should integrate their results in a picture archiving and communication systems environment such as Digital Imaging and Communications in Medicine (DICOM) structured reporting documents. A sample DICOM structured reporting template adaptable to CBIR and an appropriate integration scheme is presented. The proposed CBIR data concept may foster the promulgation of CBIR systems in clinical environments and, thereby, improve the diagnostic process. PMID:21672913

a Clustering-Based Approach for Evaluation of EO Image Indexing

NASA Astrophysics Data System (ADS)

Bahmanyar, R.; Rigoll, G.; Datcu, M.

2013-09-01

The volume of Earth Observation data is increasing immensely in order of several Terabytes a day. Therefore, to explore and investigate the content of this huge amount of data, developing more sophisticated Content-Based Information Retrieval (CBIR) systems are highly demanded. These systems should be able to not only discover unknown structures behind the data, but also provide relevant results to the users' queries. Since in any retrieval system the images are processed based on a discrete set of their features (i.e., feature descriptors), study and assessment of the structure of feature space, build by different feature descriptors, is of high importance. In this paper, we introduce a clustering-based approach to study the content of image collections. In our approach, we claim that using both internal and external evaluation of clusters for different feature descriptors, helps to understand the structure of feature space. Moreover, the semantic understanding of users about the images also can be assessed. To validate the performance of our approach, we used an annotated Synthetic Aperture Radar (SAR) image collection. Quantitative results besides the visualization of feature space demonstrate the applicability of our approach.

Disregarding Familiarity during Recollection Attempts: Content-Specific Recapitulation as a Retrieval Orientation Strategy

ERIC Educational Resources Information Center

Gray, Stephen J.; Gallo, David A.

2015-01-01

People can use a content-specific recapitulation strategy to trigger memories (i.e., mentally reinstating encoding conditions), but how people deploy this strategy is unclear. Is recapitulation naturally used to guide all recollection attempts, or is it only used selectively, after retrieving incomplete information that requires additional…

Beyond Information Retrieval: Ways To Provide Content in Context.

ERIC Educational Resources Information Center

Wiley, Deborah Lynne

1998-01-01

Provides an overview of information retrieval from mainframe systems to Web search engines; discusses collaborative filtering, data extraction, data visualization, agent technology, pattern recognition, classification and clustering, and virtual communities. Argues that rather than huge data-storage centers and proprietary software, we need…

A Novel Image Retrieval Based on Visual Words Integration of SIFT and SURF

PubMed Central

Ali, Nouman; Bajwa, Khalid Bashir; Sablatnig, Robert; Chatzichristofis, Savvas A.; Iqbal, Zeshan; Rashid, Muhammad; Habib, Hafiz Adnan

2016-01-01

With the recent evolution of technology, the number of image archives has increased exponentially. In Content-Based Image Retrieval (CBIR), high-level visual information is represented in the form of low-level features. The semantic gap between the low-level features and the high-level image concepts is an open research problem. In this paper, we present a novel visual words integration of Scale Invariant Feature Transform (SIFT) and Speeded-Up Robust Features (SURF). The two local features representations are selected for image retrieval because SIFT is more robust to the change in scale and rotation, while SURF is robust to changes in illumination. The visual words integration of SIFT and SURF adds the robustness of both features to image retrieval. The qualitative and quantitative comparisons conducted on Corel-1000, Corel-1500, Corel-2000, Oliva and Torralba and Ground Truth image benchmarks demonstrate the effectiveness of the proposed visual words integration. PMID:27315101

Decoding the content of recollection within the core recollection network and beyond.

PubMed

Thakral, Preston P; Wang, Tracy H; Rugg, Michael D

2017-06-01

Recollection - retrieval of qualitative information about a past event - is associated with enhanced neural activity in a consistent set of neural regions (the 'core recollection network') seemingly regardless of the nature of the recollected content. Here, we employed multi-voxel pattern analysis (MVPA) to assess whether retrieval-related functional magnetic resonance imaging (fMRI) activity in core recollection regions - including the hippocampus, angular gyrus, medial prefrontal cortex, retrosplenial/posterior cingulate cortex, and middle temporal gyrus - contain information about studied content and thus demonstrate retrieval-related 'reinstatement' effects. During study, participants viewed objects and concrete words that were subjected to different encoding tasks. Test items included studied words, the names of studied objects, or unstudied words. Participants judged whether the items were recollected, familiar, or new by making 'remember', 'know', and 'new' responses, respectively. The study history of remembered test items could be reliably decoded using MVPA in most regions, as well as from the dorsolateral prefrontal cortex, a region where univariate recollection effects could not be detected. The findings add to evidence that members of the core recollection network, as well as at least one neural region where mean signal is insensitive to recollection success, carry information about recollected content. Importantly, the study history of recognized items endorsed with a 'know' response could be decoded with equal accuracy. The results thus demonstrate a striking dissociation between mean signal and multi-voxel indices of recollection. Moreover, they converge with prior findings in suggesting that, as it is operationalized by classification-based MVPA, reinstatement is not uniquely a signature of recollection. Copyright © 2016 Elsevier Ltd. All rights reserved.

Infrared Retrievals of Ice Cloud Properties and Uncertainties with an Optimal Estimation Retrieval Method

NASA Astrophysics Data System (ADS)

Wang, C.; Platnick, S. E.; Meyer, K.; Zhang, Z.

2014-12-01

We developed an optimal estimation (OE)-based method using infrared (IR) observations to retrieve ice cloud optical thickness (COT), cloud effective radius (CER), and cloud top height (CTH) simultaneously. The OE-based retrieval is coupled with a fast IR radiative transfer model (RTM) that simulates observations of different sensors, and corresponding Jacobians in cloudy atmospheres. Ice cloud optical properties are calculated using the MODIS Collection 6 (C6) ice crystal habit (severely roughened hexagonal column aggregates). The OE-based method can be applied to various IR space-borne and airborne sensors, such as the Moderate Resolution Imaging Spectroradiometer (MODIS) and the enhanced MODIS Airborne Simulator (eMAS), by optimally selecting IR bands with high information content. Four major error sources (i.e., the measurement error, fast RTM error, model input error, and pre-assumed ice crystal habit error) are taken into account in our OE retrieval method. We show that measurement error and fast RTM error have little impact on cloud retrievals, whereas errors from the model input and pre-assumed ice crystal habit significantly increase retrieval uncertainties when the cloud is optically thin. Comparisons between the OE-retrieved ice cloud properties and other operational cloud products (e.g., the MODIS C6 and CALIOP cloud products) are shown.

Information Content of Aerosol Retrievals in the Sunglint Region

NASA Technical Reports Server (NTRS)

Ottaviani, M.; Knobelspiesse, K.; Cairns, B.; Mishchenko, M.

2013-01-01

We exploit quantitative metrics to investigate the information content in retrievals of atmospheric aerosol parameters (with a focus on single-scattering albedo), contained in multi-angle and multi-spectral measurements with sufficient dynamical range in the sunglint region. The simulations are performed for two classes of maritime aerosols with optical and microphysical properties compiled from measurements of the Aerosol Robotic Network. The information content is assessed using the inverse formalism and is compared to that deriving from observations not affected by sunglint. We find that there indeed is additional information in measurements containing sunglint, not just for single-scattering albedo, but also for aerosol optical thickness and the complex refractive index of the fine aerosol size mode, although the amount of additional information varies with aerosol type.

Using Induction to Refine Information Retrieval Strategies

NASA Technical Reports Server (NTRS)

Baudin, Catherine; Pell, Barney; Kedar, Smadar

1994-01-01

Conceptual information retrieval systems use structured document indices, domain knowledge and a set of heuristic retrieval strategies to match user queries with a set of indices describing the document's content. Such retrieval strategies increase the set of relevant documents retrieved (increase recall), but at the expense of returning additional irrelevant documents (decrease precision). Usually in conceptual information retrieval systems this tradeoff is managed by hand and with difficulty. This paper discusses ways of managing this tradeoff by the application of standard induction algorithms to refine the retrieval strategies in an engineering design domain. We gathered examples of query/retrieval pairs during the system's operation using feedback from a user on the retrieved information. We then fed these examples to the induction algorithm and generated decision trees that refine the existing set of retrieval strategies. We found that (1) induction improved the precision on a set of queries generated by another user, without a significant loss in recall, and (2) in an interactive mode, the decision trees pointed out flaws in the retrieval and indexing knowledge and suggested ways to refine the retrieval strategies.

On the functional significance of retrieval mode: Task switching disrupts the recollection of conceptual stimulus information from episodic memory.

PubMed

Küper, Kristina

2018-01-01

Episodic memory retrieval is assumed to be associated with the tonic cognitive state of retrieval mode. Despite extensive research into the neurophysiological correlates of retrieval mode, as of yet, relatively little is known about its functional significance. The present event-related potential (ERP) study was aimed at examining the impact of retrieval mode on the specificity of memory content retrieved in the course of familiarity and recollection processes. In two experiments, participants performed a recognition memory inclusion task in which they had to distinguish identically repeated and re-colored versions of study items from new items. In Experiment 1, participants had to alternate between the episodic memory task and a semantic task requiring a natural/artificial decision. In Experiment 2, the two tasks were instead performed in separate blocks. ERPs locked to the preparatory cues in the test phases indicated that participants did not establish retrieval mode on switch trials in Experiment 1. In the absence of retrieval mode, neither type of studied item elicited ERP correlates of familiarity-based retrieval (FN400). Recollection-related late positive complex (LPC) old/new effects emerged only for identically repeated but not for conceptually identical but perceptually changed versions of study items. With blocked retrieval in Experiment 2, both types of old items instead elicited equivalent FN400 and LPC old/new effects. The LPC data indicate that retrieval mode may play an important role in the successful recollection of conceptual stimulus information. The FN400 results additionally suggest that task switching may have a detrimental effect on familiarity-based memory retrieval. Copyright © 2017 Elsevier B.V. All rights reserved.

TRECVID: the utility of a content-based video retrieval evaluation

NASA Astrophysics Data System (ADS)

Hauptmann, Alexander G.

2006-01-01

TRECVID, an annual retrieval evaluation benchmark organized by NIST, encourages research in information retrieval from digital video. TRECVID benchmarking covers both interactive and manual searching by end users, as well as the benchmarking of some supporting technologies including shot boundary detection, extraction of semantic features, and the automatic segmentation of TV news broadcasts. Evaluations done in the context of the TRECVID benchmarks show that generally, speech transcripts and annotations provide the single most important clue for successful retrieval. However, automatically finding the individual images is still a tremendous and unsolved challenge. The evaluations repeatedly found that none of the multimedia analysis and retrieval techniques provide a significant benefit over retrieval using only textual information such as from automatic speech recognition transcripts or closed captions. In interactive systems, we do find significant differences among the top systems, indicating that interfaces can make a huge difference for effective video/image search. For interactive tasks efficient interfaces require few key clicks, but display large numbers of images for visual inspection by the user. The text search finds the right context region in the video in general, but to select specific relevant images we need good interfaces to easily browse the storyboard pictures. In general, TRECVID has motivated the video retrieval community to be honest about what we don't know how to do well (sometimes through painful failures), and has focused us to work on the actual task of video retrieval, as opposed to flashy demos based on technological capabilities.

Documents Similarity Measurement Using Field Association Terms.

ERIC Educational Resources Information Center

Atlam, El-Sayed; Fuketa, M.; Morita, K.; Aoe, Jun-ichi

2003-01-01

Discussion of text analysis and information retrieval and measurement of document similarity focuses on a new text manipulation system called FA (field association)-Sim that is useful for retrieving information in large heterogeneous texts and for recognizing content similarity in text excerpts. Discusses recall and precision, automatic indexing…

A fully automatic end-to-end method for content-based image retrieval of CT scans with similar liver lesion annotations.

PubMed

Spanier, A B; Caplan, N; Sosna, J; Acar, B; Joskowicz, L

2018-01-01

The goal of medical content-based image retrieval (M-CBIR) is to assist radiologists in the decision-making process by retrieving medical cases similar to a given image. One of the key interests of radiologists is lesions and their annotations, since the patient treatment depends on the lesion diagnosis. Therefore, a key feature of M-CBIR systems is the retrieval of scans with the most similar lesion annotations. To be of value, M-CBIR systems should be fully automatic to handle large case databases. We present a fully automatic end-to-end method for the retrieval of CT scans with similar liver lesion annotations. The input is a database of abdominal CT scans labeled with liver lesions, a query CT scan, and optionally one radiologist-specified lesion annotation of interest. The output is an ordered list of the database CT scans with the most similar liver lesion annotations. The method starts by automatically segmenting the liver in the scan. It then extracts a histogram-based features vector from the segmented region, learns the features' relative importance, and ranks the database scans according to the relative importance measure. The main advantages of our method are that it fully automates the end-to-end querying process, that it uses simple and efficient techniques that are scalable to large datasets, and that it produces quality retrieval results using an unannotated CT scan. Our experimental results on 9 CT queries on a dataset of 41 volumetric CT scans from the 2014 Image CLEF Liver Annotation Task yield an average retrieval accuracy (Normalized Discounted Cumulative Gain index) of 0.77 and 0.84 without/with annotation, respectively. Fully automatic end-to-end retrieval of similar cases based on image information alone, rather that on disease diagnosis, may help radiologists to better diagnose liver lesions.

Natural brain-information interfaces: Recommending information by relevance inferred from human brain signals

PubMed Central

Eugster, Manuel J. A.; Ruotsalo, Tuukka; Spapé, Michiel M.; Barral, Oswald; Ravaja, Niklas; Jacucci, Giulio; Kaski, Samuel

2016-01-01

Finding relevant information from large document collections such as the World Wide Web is a common task in our daily lives. Estimation of a user’s interest or search intention is necessary to recommend and retrieve relevant information from these collections. We introduce a brain-information interface used for recommending information by relevance inferred directly from brain signals. In experiments, participants were asked to read Wikipedia documents about a selection of topics while their EEG was recorded. Based on the prediction of word relevance, the individual’s search intent was modeled and successfully used for retrieving new relevant documents from the whole English Wikipedia corpus. The results show that the users’ interests toward digital content can be modeled from the brain signals evoked by reading. The introduced brain-relevance paradigm enables the recommendation of information without any explicit user interaction and may be applied across diverse information-intensive applications. PMID:27929077

Natural brain-information interfaces: Recommending information by relevance inferred from human brain signals

NASA Astrophysics Data System (ADS)

Eugster, Manuel J. A.; Ruotsalo, Tuukka; Spapé, Michiel M.; Barral, Oswald; Ravaja, Niklas; Jacucci, Giulio; Kaski, Samuel

2016-12-01

Finding relevant information from large document collections such as the World Wide Web is a common task in our daily lives. Estimation of a user’s interest or search intention is necessary to recommend and retrieve relevant information from these collections. We introduce a brain-information interface used for recommending information by relevance inferred directly from brain signals. In experiments, participants were asked to read Wikipedia documents about a selection of topics while their EEG was recorded. Based on the prediction of word relevance, the individual’s search intent was modeled and successfully used for retrieving new relevant documents from the whole English Wikipedia corpus. The results show that the users’ interests toward digital content can be modeled from the brain signals evoked by reading. The introduced brain-relevance paradigm enables the recommendation of information without any explicit user interaction and may be applied across diverse information-intensive applications.

Sensitivity of the RMI's MAGIC/Heliosat-2 method to relevant input data

NASA Astrophysics Data System (ADS)

Demain, C.; Journée, M.; Bertrand, C.

2013-01-01

Appropriate information on solar resources is very important for a variety of technological areas. Based on the potential of retrieving global horizontal irradiance from satellite data, an enhanced version of the Heliosat-2 method has been implemented at the Royal Meteorological Institute of Belgium to estimate surface solar irradiance over Belgium from Meteosat Second Generation at the SEVIRI spatial and temporal resolution. In this contribution, sensitivity of our retrieval scheme to surface albedo, atmospheric aerosol and water vapor contents is investigated. Results indicate that while the use of real-time information instead of climatological values can help to reduce to some extent the RMS error between satellite-retrieved and ground-measured solar irradiance, only the correction of the satellite-derived data with in situ measurements allows to significantly reduce the overall model bias.

Multi-view information fusion for automatic BI-RADS description of mammographic masses

NASA Astrophysics Data System (ADS)

Narvaez, Fabián; Díaz, Gloria; Romero, Eduardo

2011-03-01

Most CBIR-based CAD systems (Content Based Image Retrieval systems for Computer Aided Diagnosis) identify lesions that are eventually relevant. These systems base their analysis upon a single independent view. This article presents a CBIR framework which automatically describes mammographic masses with the BI-RADS lexicon, fusing information from the two mammographic views. After an expert selects a Region of Interest (RoI) at the two views, a CBIR strategy searches similar masses in the database by automatically computing the Mahalanobis distance between shape and texture feature vectors of the mammography. The strategy was assessed in a set of 400 cases, for which the suggested descriptions were compared with the ground truth provided by the data base. Two information fusion strategies were evaluated, allowing a retrieval precision rate of 89.6% in the best scheme. Likewise, the best performance obtained for shape, margin and pathology description, using a ROC methodology, was reported as AUC = 0.86, AUC = 0.72 and AUC = 0.85, respectively.

Content-based histopathology image retrieval using CometCloud.

PubMed

Qi, Xin; Wang, Daihou; Rodero, Ivan; Diaz-Montes, Javier; Gensure, Rebekah H; Xing, Fuyong; Zhong, Hua; Goodell, Lauri; Parashar, Manish; Foran, David J; Yang, Lin

2014-08-26

The development of digital imaging technology is creating extraordinary levels of accuracy that provide support for improved reliability in different aspects of the image analysis, such as content-based image retrieval, image segmentation, and classification. This has dramatically increased the volume and rate at which data are generated. Together these facts make querying and sharing non-trivial and render centralized solutions unfeasible. Moreover, in many cases this data is often distributed and must be shared across multiple institutions requiring decentralized solutions. In this context, a new generation of data/information driven applications must be developed to take advantage of the national advanced cyber-infrastructure (ACI) which enable investigators to seamlessly and securely interact with information/data which is distributed across geographically disparate resources. This paper presents the development and evaluation of a novel content-based image retrieval (CBIR) framework. The methods were tested extensively using both peripheral blood smears and renal glomeruli specimens. The datasets and performance were evaluated by two pathologists to determine the concordance. The CBIR algorithms that were developed can reliably retrieve the candidate image patches exhibiting intensity and morphological characteristics that are most similar to a given query image. The methods described in this paper are able to reliably discriminate among subtle staining differences and spatial pattern distributions. By integrating a newly developed dual-similarity relevance feedback module into the CBIR framework, the CBIR results were improved substantially. By aggregating the computational power of high performance computing (HPC) and cloud resources, we demonstrated that the method can be successfully executed in minutes on the Cloud compared to weeks using standard computers. In this paper, we present a set of newly developed CBIR algorithms and validate them using two different pathology applications, which are regularly evaluated in the practice of pathology. Comparative experimental results demonstrate excellent performance throughout the course of a set of systematic studies. Additionally, we present and evaluate a framework to enable the execution of these algorithms across distributed resources. We show how parallel searching of content-wise similar images in the dataset significantly reduces the overall computational time to ensure the practical utility of the proposed CBIR algorithms.

New Satellite Estimates of Mixed-Phase Cloud Properties: A Synergistic Approach for Application to Global Satellite Imager Data

NASA Astrophysics Data System (ADS)

Smith, W. L., Jr.; Spangenberg, D.; Fleeger, C.; Sun-Mack, S.; Chen, Y.; Minnis, P.

2016-12-01

Determining accurate cloud properties horizontally and vertically over a full range of time and space scales is currently next to impossible using data from either active or passive remote sensors or from modeling systems. Passive satellite imagers provide horizontal and temporal resolution of clouds, but little direct information on vertical structure. Active sensors provide vertical resolution but limited spatial and temporal coverage. Cloud models embedded in NWP can produce realistic clouds but often not at the right time or location. Thus, empirical techniques that integrate information from multiple observing and modeling systems are needed to more accurately characterize clouds and their impacts. Such a strategy is employed here in a new cloud water content profiling technique developed for application to satellite imager cloud retrievals based on VIS, IR and NIR radiances. Parameterizations are developed to relate imager retrievals of cloud top phase, optical depth, effective radius and temperature to ice and liquid water content profiles. The vertical structure information contained in the parameterizations is characterized climatologically from cloud model analyses, aircraft observations, ground-based remote sensing data, and from CloudSat and CALIPSO. Thus, realistic cloud-type dependent vertical structure information (including guidance on cloud phase partitioning) circumvents poor assumptions regarding vertical homogeneity that plague current passive satellite retrievals. This paper addresses mixed phase cloud conditions for clouds with glaciated tops including those associated with convection and mid-latitude storm systems. Novel outcomes of our approach include (1) simultaneous retrievals of ice and liquid water content and path, which are validated with active sensor, microwave and in-situ data, and yield improved global cloud climatologies, and (2) new estimates of super-cooled LWC, which are demonstrated in aviation safety applications and validated with icing PIREPS. The initial validation is encouraging for single-layer cloud conditions. More work is needed to test and refine the method for global application in a wider range of cloud conditions. A brief overview of our current method, applications, verification, and plans for future work will be presented.

Combining semantic technologies with a content-based image retrieval system - Preliminary considerations

NASA Astrophysics Data System (ADS)

Chmiel, P.; Ganzha, M.; Jaworska, T.; Paprzycki, M.

2017-10-01

Nowadays, as a part of systematic growth of volume, and variety, of information that can be found on the Internet, we observe also dramatic increase in sizes of available image collections. There are many ways to help users browsing / selecting images of interest. One of popular approaches are Content-Based Image Retrieval (CBIR) systems, which allow users to search for images that match their interests, expressed in the form of images (query by example). However, we believe that image search and retrieval could take advantage of semantic technologies. We have decided to test this hypothesis. Specifically, on the basis of knowledge captured in the CBIR, we have developed a domain ontology of residential real estate (detached houses, in particular). This allows us to semantically represent each image (and its constitutive architectural elements) represented within the CBIR. The proposed ontology was extended to capture not only the elements resulting from image segmentation, but also "spatial relations" between them. As a result, a new approach to querying the image database (semantic querying) has materialized, thus extending capabilities of the developed system.

A New Understanding for the Rain Rate retrieval of Attenuating Radars Measurement

NASA Astrophysics Data System (ADS)

Koner, P.; Battaglia, A.; Simmer, C.

2009-04-01

The retrieval of rain rate from the attenuated radar (e.g. Cloud Profiling Radar on board of CloudSAT in orbit since June 2006) is a challenging problem. ĹEcuyer and Stephens [1] underlined this difficulty (for rain rates larger than 1.5 mm/h) and suggested the need of additional information (like path-integrated attenuations (PIA) derived from surface reference techniques or precipitation water path estimated from co-located passive microwave radiometer) to constrain the retrieval. It is generally discussed based on the optimal estimation theory that there are no solutions without constraining the problem in a case of visible attenuation because there is no enough information content to solve the problem. However, when the problem is constrained by the additional measurement of PIA, there is a reasonable solution. This raises the spontaneous question: Is all information enclosed in this additional measurement? This also contradicts with the information theory because one measurement can introduce only one degree of freedom in the retrieval. Why is one degree of freedom so important in the above problem? This question cannot be explained using the estimation and information theories of OEM. On the other hand, Koner and Drummond [2] argued that the OEM is basically a regularization method, where a-priori covariance is used as a stabilizer and the regularization strength is determined by the choices of the a-priori and error covariance matrices. The regularization is required for the reduction of the condition number of Jacobian, which drives the noise injection from the measurement and inversion spaces to the state space in an ill-posed inversion. In this work, the above mentioned question will be discussed based on the regularization theory, error mitigation and eigenvalue mathematics. References 1. L'Ecuyer TS and Stephens G. An estimation based precipitation retrieval algorithm for attenuating radar. J. Appl. Met., 2002, 41, 272-85. 2. Koner PK, Drummond JR. A comparison of regularization techniques for atmospheric trace gases retrievals. JQSRT 2008; 109:514-26.

BIRAM: a content-based image retrieval framework for medical images

NASA Astrophysics Data System (ADS)

Moreno, Ramon A.; Furuie, Sergio S.

2006-03-01

In the medical field, digital images are becoming more and more important for diagnostics and therapy of the patients. At the same time, the development of new technologies has increased the amount of image data produced in a hospital. This creates a demand for access methods that offer more than text-based queries for retrieval of the information. In this paper is proposed a framework for the retrieval of medical images that allows the use of different algorithms for the search of medical images by similarity. The framework also enables the search for textual information from an associated medical report and DICOM header information. The proposed system can be used for support of clinical decision making and is intended to be integrated with an open source picture, archiving and communication systems (PACS). The BIRAM has the following advantages: (i) Can receive several types of algorithms for image similarity search; (ii) Allows the codification of the report according to a medical dictionary, improving the indexing of the information and retrieval; (iii) The algorithms can be selectively applied to images with the appropriated characteristics, for instance, only in magnetic resonance images. The framework was implemented in Java language using a MS Access 97 database. The proposed framework can still be improved, by the use of regions of interest (ROI), indexing with slim-trees and integration with a PACS Server.

Image retrieval for identifying house plants

NASA Astrophysics Data System (ADS)

Kebapci, Hanife; Yanikoglu, Berrin; Unal, Gozde

2010-02-01

We present a content-based image retrieval system for plant identification which is intended for providing users with a simple method to locate information about their house plants. A plant image consists of a collection of overlapping leaves and possibly flowers, which makes the problem challenging. We studied the suitability of various well-known color, texture and shape features for this problem, as well as introducing some new ones. The features are extracted from the general plant region that is segmented from the background using the max-flow min-cut technique. Results on a database of 132 different plant images show promise (in about 72% of the queries, the correct plant image is retrieved among the top-15 results).

Data-Base Software For Tracking Technological Developments

NASA Technical Reports Server (NTRS)

Aliberti, James A.; Wright, Simon; Monteith, Steve K.

1996-01-01

Technology Tracking System (TechTracS) computer program developed for use in storing and retrieving information on technology and related patent information developed under auspices of NASA Headquarters and NASA's field centers. Contents of data base include multiple scanned still images and quick-time movies as well as text. TechTracS includes word-processing, report-editing, chart-and-graph-editing, and search-editing subprograms. Extensive keyword searching capabilities enable rapid location of technologies, innovators, and companies. System performs routine functions automatically and serves multiple users.

Recommending Education Materials for Diabetic Questions Using Information Retrieval Approaches.

PubMed

Zeng, Yuqun; Liu, Xusheng; Wang, Yanshan; Shen, Feichen; Liu, Sijia; Rastegar-Mojarad, Majid; Wang, Liwei; Liu, Hongfang

2017-10-16

Self-management is crucial to diabetes care and providing expert-vetted content for answering patients' questions is crucial in facilitating patient self-management. The aim is to investigate the use of information retrieval techniques in recommending patient education materials for diabetic questions of patients. We compared two retrieval algorithms, one based on Latent Dirichlet Allocation topic modeling (topic modeling-based model) and one based on semantic group (semantic group-based model), with the baseline retrieval models, vector space model (VSM), in recommending diabetic patient education materials to diabetic questions posted on the TuDiabetes forum. The evaluation was based on a gold standard dataset consisting of 50 randomly selected diabetic questions where the relevancy of diabetic education materials to the questions was manually assigned by two experts. The performance was assessed using precision of top-ranked documents. We retrieved 7510 diabetic questions on the forum and 144 diabetic patient educational materials from the patient education database at Mayo Clinic. The mapping rate of words in each corpus mapped to the Unified Medical Language System (UMLS) was significantly different (P<.001). The topic modeling-based model outperformed the other retrieval algorithms. For example, for the top-retrieved document, the precision of the topic modeling-based, semantic group-based, and VSM models was 67.0%, 62.8%, and 54.3%, respectively. This study demonstrated that topic modeling can mitigate the vocabulary difference and it achieved the best performance in recommending education materials for answering patients' questions. One direction for future work is to assess the generalizability of our findings and to extend our study to other disease areas, other patient education material resources, and online forums. ©Yuqun Zeng, Xusheng Liu, Yanshan Wang, Feichen Shen, Sijia Liu, Majid Rastegar Mojarad, Liwei Wang, Hongfang Liu. Originally published in the Journal of Medical Internet Research (http://www.jmir.org), 16.10.2017.

Assessing Unmet Information Needs of Breast Cancer Survivors: Exploratory Study of Online Health Forums Using Text Classification and Retrieval.

PubMed

McRoy, Susan; Rastegar-Mojarad, Majid; Wang, Yanshan; Ruddy, Kathryn J; Haddad, Tufia C; Liu, Hongfang

2018-05-15

Patient education materials given to breast cancer survivors may not be a good fit for their information needs. Needs may change over time, be forgotten, or be misreported, for a variety of reasons. An automated content analysis of survivors' postings to online health forums can identify expressed information needs over a span of time and be repeated regularly at low cost. Identifying these unmet needs can guide improvements to existing education materials and the creation of new resources. The primary goals of this project are to assess the unmet information needs of breast cancer survivors from their own perspectives and to identify gaps between information needs and current education materials. This approach employs computational methods for content modeling and supervised text classification to data from online health forums to identify explicit and implicit requests for health-related information. Potential gaps between needs and education materials are identified using techniques from information retrieval. We provide a new taxonomy for the classification of sentences in online health forum data. 260 postings from two online health forums were selected, yielding 4179 sentences for coding. After annotation of data and training alternative one-versus-others classifiers, a random forest-based approach achieved F1 scores from 66% (Other, dataset2) to 90% (Medical, dataset1) on the primary information types. 136 expressions of need were used to generate queries to indexed education materials. Upon examination of the best two pages retrieved for each query, 12% (17/136) of queries were found to have relevant content by all coders, and 33% (45/136) were judged to have relevant content by at least one. Text from online health forums can be analyzed effectively using automated methods. Our analysis confirms that breast cancer survivors have many information needs that are not covered by the written documents they typically receive, as our results suggest that at most a third of breast cancer survivors' questions would be addressed by the materials currently provided to them. ©Susan McRoy, Majid Rastegar-Mojarad, Yanshan Wang, Kathryn J. Ruddy, Tufia C. Haddad, Hongfang Liu. Originally published in JMIR Cancer (http://cancer.jmir.org), 15.05.2018.

Assessing Unmet Information Needs of Breast Cancer Survivors: Exploratory Study of Online Health Forums Using Text Classification and Retrieval

PubMed Central

Rastegar-Mojarad, Majid; Wang, Yanshan; Ruddy, Kathryn J; Haddad, Tufia C; Liu, Hongfang

2018-01-01

Background Patient education materials given to breast cancer survivors may not be a good fit for their information needs. Needs may change over time, be forgotten, or be misreported, for a variety of reasons. An automated content analysis of survivors' postings to online health forums can identify expressed information needs over a span of time and be repeated regularly at low cost. Identifying these unmet needs can guide improvements to existing education materials and the creation of new resources. Objective The primary goals of this project are to assess the unmet information needs of breast cancer survivors from their own perspectives and to identify gaps between information needs and current education materials. Methods This approach employs computational methods for content modeling and supervised text classification to data from online health forums to identify explicit and implicit requests for health-related information. Potential gaps between needs and education materials are identified using techniques from information retrieval. Results We provide a new taxonomy for the classification of sentences in online health forum data. 260 postings from two online health forums were selected, yielding 4179 sentences for coding. After annotation of data and training alternative one-versus-others classifiers, a random forest-based approach achieved F1 scores from 66% (Other, dataset2) to 90% (Medical, dataset1) on the primary information types. 136 expressions of need were used to generate queries to indexed education materials. Upon examination of the best two pages retrieved for each query, 12% (17/136) of queries were found to have relevant content by all coders, and 33% (45/136) were judged to have relevant content by at least one. Conclusions Text from online health forums can be analyzed effectively using automated methods. Our analysis confirms that breast cancer survivors have many information needs that are not covered by the written documents they typically receive, as our results suggest that at most a third of breast cancer survivors’ questions would be addressed by the materials currently provided to them. PMID:29764801

Collaboration Expertise in Medicine - No Evidence for Cross-Domain Application from a Memory Retrieval Study.

PubMed

Kiesewetter, Jan; Fischer, Frank; Fischer, Martin R

2016-01-01

Is there evidence for expertise on collaboration and, if so, is there evidence for cross-domain application? Recall of stimuli was used to measure so-called internal collaboration scripts of novices and experts in two studies. Internal collaboration scripts refer to an individual's knowledge about how to interact with others in a social situation. METHOD— Ten collaboration experts and ten novices of the content domain social science were presented with four pictures of people involved in collaborative activities. The recall texts were coded, distinguishing between superficial and collaboration script information. RESULTS— Experts recalled significantly more collaboration script information (M = 25.20; SD = 5.88) than did novices (M = 13.80; SD = 4.47). Differences in superficial information were not found. Study 2 tested whether the differences found in Study 1 could be replicated. Furthermore, the cross-domain application of internal collaboration scripts was explored. METHOD— Twenty collaboration experts and 20 novices of the content domain medicine were presented with four pictures and four videos of their content domain and a video and picture of another content domain. All stimuli showed collaborative activities typical for the respective content domains. RESULTS— As in Study 1, experts recalled significantly more collaboration script information of their content domain (M = 71.65; SD = 33.23) than did novices (M = 54.25; SD = 15.01). For the novices, no differences were found for the superficial information nor for the retrieval of collaboration script information recalled after the other content domain stimuli. There is evidence for expertise on collaboration in memory tasks. The results show that experts hold substantially more collaboration script information than did novices. Furthermore, the differences between collaboration novices and collaboration experts occurred only in their own content domain, indicating that internal collaboration scripts are not easily stored and retrieved in memory tasks other than in the own content domain.

Collaboration Expertise in Medicine - No Evidence for Cross-Domain Application from a Memory Retrieval Study

PubMed Central

Kiesewetter, Jan; Fischer, Frank; Fischer, Martin R.

2016-01-01

Background Is there evidence for expertise on collaboration and, if so, is there evidence for cross-domain application? Recall of stimuli was used to measure so-called internal collaboration scripts of novices and experts in two studies. Internal collaboration scripts refer to an individual’s knowledge about how to interact with others in a social situation. Method—Study 1 Ten collaboration experts and ten novices of the content domain social science were presented with four pictures of people involved in collaborative activities. The recall texts were coded, distinguishing between superficial and collaboration script information. Results—Study 1 Experts recalled significantly more collaboration script information (M = 25.20; SD = 5.88) than did novices (M = 13.80; SD = 4.47). Differences in superficial information were not found. Study 2 Study 2 tested whether the differences found in Study 1 could be replicated. Furthermore, the cross-domain application of internal collaboration scripts was explored. Method—Study 2 Twenty collaboration experts and 20 novices of the content domain medicine were presented with four pictures and four videos of their content domain and a video and picture of another content domain. All stimuli showed collaborative activities typical for the respective content domains. Results—Study 2 As in Study 1, experts recalled significantly more collaboration script information of their content domain (M = 71.65; SD = 33.23) than did novices (M = 54.25; SD = 15.01). For the novices, no differences were found for the superficial information nor for the retrieval of collaboration script information recalled after the other content domain stimuli. Discussion There is evidence for expertise on collaboration in memory tasks. The results show that experts hold substantially more collaboration script information than did novices. Furthermore, the differences between collaboration novices and collaboration experts occurred only in their own content domain, indicating that internal collaboration scripts are not easily stored and retrieved in memory tasks other than in the own content domain. PMID:26866801

A neotropical Miocene pollen database employing image-based search and semantic modeling1

PubMed Central

Han, Jing Ginger; Cao, Hongfei; Barb, Adrian; Punyasena, Surangi W.; Jaramillo, Carlos; Shyu, Chi-Ren

2014-01-01

• Premise of the study: Digital microscopic pollen images are being generated with increasing speed and volume, producing opportunities to develop new computational methods that increase the consistency and efficiency of pollen analysis and provide the palynological community a computational framework for information sharing and knowledge transfer. • Methods: Mathematical methods were used to assign trait semantics (abstract morphological representations) of the images of neotropical Miocene pollen and spores. Advanced database-indexing structures were built to compare and retrieve similar images based on their visual content. A Web-based system was developed to provide novel tools for automatic trait semantic annotation and image retrieval by trait semantics and visual content. • Results: Mathematical models that map visual features to trait semantics can be used to annotate images with morphology semantics and to search image databases with improved reliability and productivity. Images can also be searched by visual content, providing users with customized emphases on traits such as color, shape, and texture. • Discussion: Content- and semantic-based image searches provide a powerful computational platform for pollen and spore identification. The infrastructure outlined provides a framework for building a community-wide palynological resource, streamlining the process of manual identification, analysis, and species discovery. PMID:25202648

Semantic Storyboard of Judicial Debates: A Novel Multimedia Summarization Environment

ERIC Educational Resources Information Center

Fersini, E.; Sartori, F.

2012-01-01

Purpose: The need of tools for content analysis, information extraction and retrieval of multimedia objects in their native form is strongly emphasized into the judicial domain: digital videos represent a fundamental informative source of events occurring during judicial proceedings that should be stored, organized and retrieved in short time and…

Information Storage and Retrieval Scientific Report No. ISR-22.

ERIC Educational Resources Information Center

Salton, Gerard

The twenty-second in a series, this report describes research in information organization and retrieval conducted by the Department of Computer Science at Cornell University. The report covers work carried out during the period summer 1972 through summer 1974 and is divided into four parts: indexing theory, automatic content analysis, feedback…

A content-based retrieval of mammographic masses using the curvelet descriptor

NASA Astrophysics Data System (ADS)

Narváez, Fabian; Díaz, Gloria; Gómez, Francisco; Romero, Eduardo

2012-03-01

Computer-aided diagnosis (CAD) that uses content based image retrieval (CBIR) strategies has became an important research area. This paper presents a retrieval strategy that automatically recovers mammography masses from a virtual repository of mammographies. Unlike other approaches, we do not attempt to segment masses but instead we characterize the regions previously selected by an expert. These regions are firstly curvelet transformed and further characterized by approximating the marginal curvelet subband distribution with a generalized gaussian density (GGD). The content based retrieval strategy searches similar regions in a database using the Kullback-Leibler divergence as the similarity measure between distributions. The effectiveness of the proposed descriptor was assessed by comparing the automatically assigned label with a ground truth available in the DDSM database.1 A total of 380 masses with different shapes, sizes and margins were used for evaluation, resulting in a mean average precision rate of 89.3% and recall rate of 75.2% for the retrieval task.

Characterizing the information content of cloud thermodynamic phase retrievals from the notional PACE OCI shortwave reflectance measurements

NASA Astrophysics Data System (ADS)

Coddington, O. M.; Vukicevic, T.; Schmidt, K. S.; Platnick, S.

2017-08-01

We rigorously quantify the probability of liquid or ice thermodynamic phase using only shortwave spectral channels specific to the National Aeronautics and Space Administration's Moderate Resolution Imaging Spectroradiometer, Visible Infrared Imaging Radiometer Suite, and the notional future Plankton, Aerosol, Cloud, ocean Ecosystem imager. The results show that two shortwave-infrared channels (2135 and 2250 nm) provide more information on cloud thermodynamic phase than either channel alone; in one case, the probability of ice phase retrieval increases from 65 to 82% by combining 2135 and 2250 nm channels. The analysis is performed with a nonlinear statistical estimation approach, the GEneralized Nonlinear Retrieval Analysis (GENRA). The GENRA technique has previously been used to quantify the retrieval of cloud optical properties from passive shortwave observations, for an assumed thermodynamic phase. Here we present the methodology needed to extend the utility of GENRA to a binary thermodynamic phase space (i.e., liquid or ice). We apply formal information content metrics to quantify our results; two of these (mutual and conditional information) have not previously been used in the field of cloud studies.

Landmark Image Retrieval by Jointing Feature Refinement and Multimodal Classifier Learning.

PubMed

Zhang, Xiaoming; Wang, Senzhang; Li, Zhoujun; Ma, Shuai; Xiaoming Zhang; Senzhang Wang; Zhoujun Li; Shuai Ma; Ma, Shuai; Zhang, Xiaoming; Wang, Senzhang; Li, Zhoujun

2018-06-01

Landmark retrieval is to return a set of images with their landmarks similar to those of the query images. Existing studies on landmark retrieval focus on exploiting the geometries of landmarks for visual similarity matches. However, the visual content of social images is of large diversity in many landmarks, and also some images share common patterns over different landmarks. On the other side, it has been observed that social images usually contain multimodal contents, i.e., visual content and text tags, and each landmark has the unique characteristic of both visual content and text content. Therefore, the approaches based on similarity matching may not be effective in this environment. In this paper, we investigate whether the geographical correlation among the visual content and the text content could be exploited for landmark retrieval. In particular, we propose an effective multimodal landmark classification paradigm to leverage the multimodal contents of social image for landmark retrieval, which integrates feature refinement and landmark classifier with multimodal contents by a joint model. The geo-tagged images are automatically labeled for classifier learning. Visual features are refined based on low rank matrix recovery, and multimodal classification combined with group sparse is learned from the automatically labeled images. Finally, candidate images are ranked by combining classification result and semantic consistence measuring between the visual content and text content. Experiments on real-world datasets demonstrate the superiority of the proposed approach as compared to existing methods.

Investigating the Use of a Simplified Aerosol Parameterization in Space-Based XCO2 Retrievals from OCO-2

NASA Astrophysics Data System (ADS)

Nelson, R. R.; O'Dell, C.

2017-12-01

The primary goal of OCO-2 is to use hyperspectral measurements of reflected near-infrared sunlight to retrieve the column-averaged dry-air mole fraction of carbon dioxide (XCO2) with high accuracy. This is only possible for measurements of scenes nearly free of optically thick clouds and aerosols. As some cloud or aerosol contamination will always be present, the OCO-2 retrieval algorithm includes clouds and aerosols as retrieved properties in its state vector. Information content analyses demonstrate that there are only 2-6 pieces of information about aerosols in the OCO-2 radiances. However, the upcoming OCO-2 algorithm (B8) attempts to retrieve 9 aerosol parameters; this over-fitting can hinder convergence and produce multiple solutions. In this work, we develop a simplified cloud and aerosol parameterization that intelligently reduces the number of retrieved parameters to 5 by only retrieving information about two aerosol layers: a lower tropospheric layer and an upper tropospheric / stratospheric layer. We retrieve the optical depth of each layer and the height of the lower tropospheric layer. Each of these layers contains a mixture of fine and coarse mode aerosol. In comparisons between OCO-2 XCO2 estimates and validation sources including TCCON, this scheme performs about as well as the more complicated OCO-2 retrieval algorithm, but has the potential benefits of more interpretable aerosol results, faster convergence, less nonlinearity, and greater throughput. We also investigate the dependence of our results on the optical properties of the fine and coarse mode aerosol types, such as their effective radii and the environmental relative humidity.

Algorithm for retrieving vegetative canopy and leaf parameters from multi- and hyperspectral imagery

NASA Astrophysics Data System (ADS)

Borel, Christoph

2009-05-01

In recent years hyper-spectral data has been used to retrieve information about vegetative canopies such as leaf area index and canopy water content. For the environmental scientist these two parameters are valuable, but there is potentially more information to be gained as high spatial resolution data becomes available. We developed an Amoeba (Nelder-Mead or Simplex) based program to invert a vegetative canopy radiosity model coupled with a leaf (PROSPECT5) reflectance model and modeled for the background reflectance (e.g. soil, water, leaf litter) to a measured reflectance spectrum. The PROSPECT5 leaf model has five parameters: leaf structure parameter Nstru, chlorophyll a+b concentration Cab, carotenoids content Car, equivalent water thickness Cw and dry matter content Cm. The canopy model has two parameters: total leaf area index (LAI) and number of layers. The background reflectance model is either a single reflectance spectrum from a spectral library() derived from a bare area pixel on an image or a linear mixture of soil spectra. We summarize the radiosity model of a layered canopy and give references to the leaf/needle models. The method is then tested on simulated and measured data. We investigate the uniqueness, limitations and accuracy of the retrieved parameters on canopy parameters (low, medium and high leaf area index) spectral resolution (32 to 211 band hyperspectral), sensor noise and initial conditions.

Shared decision making: empowering the bedside nurse.

PubMed

Slack, Stephanie M; Boguslawski, Jean M; Eickhoff, Rachel M; Klein, Kristi A; Pepin, Teresa M; Schrandt, Kevin; Wise, Carrie A; Zylstra, Jody A

2005-12-01

Shared decision making is a process that has empowered specialty nurses at the Mayo Clinic in Rochester, MN, to solve a practice concern. Staff nurses recognized a lack of concise, collated information available that described what nurses need to know when caring for patients receiving chemotherapy. Many aspects of the administration process were knowledge and experience based and not easily retrievable. The Hematology/Oncology/Blood and Marrow Transplant Clinical Practice Committee identified this as a significant practice issue. Ideas were brainstormed regarding how to make the information available to nursing colleagues. The Chemotherapy Yellow Pages is a resource that was developed to facilitate the rapid retrieval of pertinent information for bedside nurses. The content of this article outlines a'model of shared decision making and the processes used to address and resolve the practice concern.

A framework for automatic information quality ranking of diabetes websites.

PubMed

Belen Sağlam, Rahime; Taskaya Temizel, Tugba

2015-01-01

Objective: When searching for particular medical information on the internet the challenge lies in distinguishing the websites that are relevant to the topic, and contain accurate information. In this article, we propose a framework that automatically identifies and ranks diabetes websites according to their relevance and information quality based on the website content. Design: The proposed framework ranks diabetes websites according to their content quality, relevance and evidence based medicine. The framework combines information retrieval techniques with a lexical resource based on Sentiwordnet making it possible to work with biased and untrusted websites while, at the same time, ensuring the content relevance. Measurement: The evaluation measurements used were Pearson-correlation, true positives, false positives and accuracy. We tested the framework with a benchmark data set consisting of 55 websites with varying degrees of information quality problems. Results: The proposed framework gives good results that are comparable with the non-automated information quality measuring approaches in the literature. The correlation between the results of the proposed automated framework and ground-truth is 0.68 on an average with p < 0.001 which is greater than the other proposed automated methods in the literature (r score in average is 0.33).

Task context and organization in free recall

PubMed Central

Polyn, Sean M.; Norman, Kenneth A.; Kahana, Michael J.

2009-01-01

Prior work on organization in free recall has focused on the ways in which semantic and temporal information determine the order in which material is retrieved from memory. Tulving’s theory of ecphory suggests that these organizational effects arise from the interaction of a retrieval cue with the contents of memory. Using the continual-distraction free-recall paradigm (Bjork & Whitten, 1974) to minimize retrieval during the study period, we show that encoding task context can organize recall, suggesting that task-related information is part of the retrieval cue. We interpret these results in terms of the Context Maintenance and Retrieval model (CMR; Polyn, Norman, & Kahana, in press), in which an internal contextual representation, containing semantic, temporal, and source-related information, serves as the retrieval cue and organizes the retrieval of information from memory. We discuss these results in terms of the guided activation theory (Miller & Cohen, 2001) of the role of prefrontal cortex in task performance, as well as the rich neuropsychological literature implicating prefrontal cortex in memory search (e.g, Schacter, 1987). PMID:19524086

Determining similarity in histological images using graph-theoretic description and matching methods for content-based image retrieval in medical diagnostics.

PubMed

Sharma, Harshita; Alekseychuk, Alexander; Leskovsky, Peter; Hellwich, Olaf; Anand, R S; Zerbe, Norman; Hufnagl, Peter

2012-10-04

Computer-based analysis of digitalized histological images has been gaining increasing attention, due to their extensive use in research and routine practice. The article aims to contribute towards the description and retrieval of histological images by employing a structural method using graphs. Due to their expressive ability, graphs are considered as a powerful and versatile representation formalism and have obtained a growing consideration especially by the image processing and computer vision community. The article describes a novel method for determining similarity between histological images through graph-theoretic description and matching, for the purpose of content-based retrieval. A higher order (region-based) graph-based representation of breast biopsy images has been attained and a tree-search based inexact graph matching technique has been employed that facilitates the automatic retrieval of images structurally similar to a given image from large databases. The results obtained and evaluation performed demonstrate the effectiveness and superiority of graph-based image retrieval over a common histogram-based technique. The employed graph matching complexity has been reduced compared to the state-of-the-art optimal inexact matching methods by applying a pre-requisite criterion for matching of nodes and a sophisticated design of the estimation function, especially the prognosis function. The proposed method is suitable for the retrieval of similar histological images, as suggested by the experimental and evaluation results obtained in the study. It is intended for the use in Content Based Image Retrieval (CBIR)-requiring applications in the areas of medical diagnostics and research, and can also be generalized for retrieval of different types of complex images. The virtual slide(s) for this article can be found here: http://www.diagnosticpathology.diagnomx.eu/vs/1224798882787923.

Determining similarity in histological images using graph-theoretic description and matching methods for content-based image retrieval in medical diagnostics

PubMed Central

2012-01-01

Background Computer-based analysis of digitalized histological images has been gaining increasing attention, due to their extensive use in research and routine practice. The article aims to contribute towards the description and retrieval of histological images by employing a structural method using graphs. Due to their expressive ability, graphs are considered as a powerful and versatile representation formalism and have obtained a growing consideration especially by the image processing and computer vision community. Methods The article describes a novel method for determining similarity between histological images through graph-theoretic description and matching, for the purpose of content-based retrieval. A higher order (region-based) graph-based representation of breast biopsy images has been attained and a tree-search based inexact graph matching technique has been employed that facilitates the automatic retrieval of images structurally similar to a given image from large databases. Results The results obtained and evaluation performed demonstrate the effectiveness and superiority of graph-based image retrieval over a common histogram-based technique. The employed graph matching complexity has been reduced compared to the state-of-the-art optimal inexact matching methods by applying a pre-requisite criterion for matching of nodes and a sophisticated design of the estimation function, especially the prognosis function. Conclusion The proposed method is suitable for the retrieval of similar histological images, as suggested by the experimental and evaluation results obtained in the study. It is intended for the use in Content Based Image Retrieval (CBIR)-requiring applications in the areas of medical diagnostics and research, and can also be generalized for retrieval of different types of complex images. Virtual Slides The virtual slide(s) for this article can be found here: http://www.diagnosticpathology.diagnomx.eu/vs/1224798882787923. PMID:23035717

Content Based Image Retrieval based on Wavelet Transform coefficients distribution

PubMed Central

Lamard, Mathieu; Cazuguel, Guy; Quellec, Gwénolé; Bekri, Lynda; Roux, Christian; Cochener, Béatrice

2007-01-01

In this paper we propose a content based image retrieval method for diagnosis aid in medical fields. We characterize images without extracting significant features by using distribution of coefficients obtained by building signatures from the distribution of wavelet transform. The research is carried out by computing signature distances between the query and database images. Several signatures are proposed; they use a model of wavelet coefficient distribution. To enhance results, a weighted distance between signatures is used and an adapted wavelet base is proposed. Retrieval efficiency is given for different databases including a diabetic retinopathy, a mammography and a face database. Results are promising: the retrieval efficiency is higher than 95% for some cases using an optimization process. PMID:18003013

MRNIDX - Marine Data Index: Database Description, Operation, Retrieval, and Display

USGS Publications Warehouse

Paskevich, Valerie F.

1982-01-01

A database referencing the location and content of data stored on magnetic medium was designed to assist in the indexing of time-series and spatially dependent marine geophysical data collected or processed by the U. S. Geological Survey. The database was designed and created for input to the Geologic Retrieval and Synopsis Program (GRASP) to allow selective retrievals of information pertaining to location of data, data format, cruise, geographical bounds and collection dates of data. This information is then used to locate the stored data for administrative purposes or further processing. Database utilization is divided into three distinct operations. The first is the inventorying of the data and the updating of the database, the second is the retrieval of information from the database, and the third is the graphic display of the geographical boundaries to which the retrieved information pertains.

COM3/369: Knowledge-based Information Systems: A new approach for the representation and retrieval of medical information

PubMed Central

Mann, G; Birkmann, C; Schmidt, T; Schaeffler, V

1999-01-01

Introduction Present solutions for the representation and retrieval of medical information from online sources are not very satisfying. Either the retrieval process lacks of precision and completeness the representation does not support the update and maintenance of the represented information. Most efforts are currently put into improving the combination of search engines and HTML based documents. However, due to the current shortcomings of methods for natural language understanding there are clear limitations to this approach. Furthermore, this approach does not solve the maintenance problem. At least medical information exceeding a certain complexity seems to afford approaches that rely on structured knowledge representation and corresponding retrieval mechanisms. Methods Knowledge-based information systems are based on the following fundamental ideas. The representation of information is based on ontologies that define the structure of the domain's concepts and their relations. Views on domain models are defined and represented as retrieval schemata. Retrieval schemata can be interpreted as canonical query types focussing on specific aspects of the provided information (e.g. diagnosis or therapy centred views). Based on these retrieval schemata it can be decided which parts of the information in the domain model must be represented explicitly and formalised to support the retrieval process. As representation language propositional logic is used. All other information can be represented in a structured but informal way using text, images etc. Layout schemata are used to assign layout information to retrieved domain concepts. Depending on the target environment HTML or XML can be used. Results Based on this approach two knowledge-based information systems have been developed. The 'Ophthalmologic Knowledge-based Information System for Diabetic Retinopathy' (OKIS-DR) provides information on diagnoses, findings, examinations, guidelines, and reference images related to diabetic retinopathy. OKIS-DR uses combinations of findings to specify the information that must be retrieved. The second system focuses on nutrition related allergies and intolerances. Information on allergies and intolerances of a patient are used to retrieve general information on the specified combination of allergies and intolerances. As a special feature the system generates tables showing food types and products that are tolerated or not tolerated by patients. Evaluation by external experts and user groups showed that the described approach of knowledge-based information systems increases the precision and completeness of knowledge retrieval. Due to the structured and non-redundant representation of information the maintenance and update of the information can be simplified. Both systems are available as WWW based online knowledge bases and CD-ROMs (cf. http://mta.gsf.de topic: products).

Content-based retrieval of historical Ottoman documents stored as textual images.

PubMed

Saykol, Ediz; Sinop, Ali Kemal; Güdükbay, Ugur; Ulusoy, Ozgür; Cetin, A Enis

2004-03-01

There is an accelerating demand to access the visual content of documents stored in historical and cultural archives. Availability of electronic imaging tools and effective image processing techniques makes it feasible to process the multimedia data in large databases. In this paper, a framework for content-based retrieval of historical documents in the Ottoman Empire archives is presented. The documents are stored as textual images, which are compressed by constructing a library of symbols occurring in a document, and the symbols in the original image are then replaced with pointers into the codebook to obtain a compressed representation of the image. The features in wavelet and spatial domain based on angular and distance span of shapes are used to extract the symbols. In order to make content-based retrieval in historical archives, a query is specified as a rectangular region in an input image and the same symbol-extraction process is applied to the query region. The queries are processed on the codebook of documents and the query images are identified in the resulting documents using the pointers in textual images. The querying process does not require decompression of images. The new content-based retrieval framework is also applicable to many other document archives using different scripts.

Intuitive color-based visualization of multimedia content as large graphs

NASA Astrophysics Data System (ADS)

Delest, Maylis; Don, Anthony; Benois-Pineau, Jenny

2004-06-01

Data visualization techniques are penetrating in various technological areas. In the field of multimedia such as information search and retrieval in multimedia archives, or digital media production and post-production, data visualization methodologies based on large graphs give an exciting alternative to conventional storyboard visualization. In this paper we develop a new approach to visualization of multimedia (video) documents based both on large graph clustering and preliminary video segmenting and indexing.

Photopolarimetric Retrievals of Snow Properties

NASA Technical Reports Server (NTRS)

Ottaviani, M.; van Diedenhoven, B.; Cairns, B.

2015-01-01

Polarimetric observations of snow surfaces, obtained in the 410-2264 nm range with the Research Scanning Polarimeter onboard the NASA ER-2 high-altitude aircraft, are analyzed and presented. These novel measurements are of interest to the remote sensing community because the overwhelming brightness of snow plagues aerosol and cloud retrievals based on airborne and spaceborne total reflection measurements. The spectral signatures of the polarized reflectance of snow are therefore worthwhile investigating in order to provide guidance for the adaptation of algorithms currently employed for the retrieval of aerosol properties over soil and vegetated surfaces. At the same time, the increased information content of polarimetric measurements allows for a meaningful characterization of the snow medium. In our case, the grains are modeled as hexagonal prisms of variable aspect ratios and microscale roughness, yielding retrievals of the grains' scattering asymmetry parameter, shape and size. The results agree with our previous findings based on a more limited data set, with the majority of retrievals leading to moderately rough crystals of extreme aspect ratios, for each scene corresponding to a single value of the asymmetry parameter.

A memory learning framework for effective image retrieval.

PubMed

Han, Junwei; Ngan, King N; Li, Mingjing; Zhang, Hong-Jiang

2005-04-01

Most current content-based image retrieval systems are still incapable of providing users with their desired results. The major difficulty lies in the gap between low-level image features and high-level image semantics. To address the problem, this study reports a framework for effective image retrieval by employing a novel idea of memory learning. It forms a knowledge memory model to store the semantic information by simply accumulating user-provided interactions. A learning strategy is then applied to predict the semantic relationships among images according to the memorized knowledge. Image queries are finally performed based on a seamless combination of low-level features and learned semantics. One important advantage of our framework is its ability to efficiently annotate images and also propagate the keyword annotation from the labeled images to unlabeled images. The presented algorithm has been integrated into a practical image retrieval system. Experiments on a collection of 10,000 general-purpose images demonstrate the effectiveness of the proposed framework.

Dynamic storage in resource-scarce browsing multimedia applications

NASA Astrophysics Data System (ADS)

Elenbaas, Herman; Dimitrova, Nevenka

1998-10-01

In the convergence of information and entertainment there is a conflict between the consumer's expectation of fast access to high quality multimedia content through narrow bandwidth channels versus the size of this content. During the retrieval and information presentation of a multimedia application there are two problems that have to be solved: the limited bandwidth during transmission of the retrieved multimedia content and the limited memory for temporary caching. In this paper we propose an approach for latency optimization in information browsing applications. We proposed a method for flattening hierarchically linked documents in a manner convenient for network transport over slow channels to minimize browsing latency. Flattening of the hierarchy involves linearization, compression and bundling of the document nodes. After the transfer, the compressed hierarchy is stored on a local device where it can be partly unbundled to fit the caching limits at the local site while giving the user availability to the content.

A spatiotemporal decomposition strategy for personal home video management

NASA Astrophysics Data System (ADS)

Yi, Haoran; Kozintsev, Igor; Polito, Marzia; Wu, Yi; Bouguet, Jean-Yves; Nefian, Ara; Dulong, Carole

2007-01-01

With the advent and proliferation of low cost and high performance digital video recorder devices, an increasing number of personal home video clips are recorded and stored by the consumers. Compared to image data, video data is lager in size and richer in multimedia content. Efficient access to video content is expected to be more challenging than image mining. Previously, we have developed a content-based image retrieval system and the benchmarking framework for personal images. In this paper, we extend our personal image retrieval system to include personal home video clips. A possible initial solution to video mining is to represent video clips by a set of key frames extracted from them thus converting the problem into an image search one. Here we report that a careful selection of key frames may improve the retrieval accuracy. However, because video also has temporal dimension, its key frame representation is inherently limited. The use of temporal information can give us better representation for video content at semantic object and concept levels than image-only based representation. In this paper we propose a bottom-up framework to combine interest point tracking, image segmentation and motion-shape factorization to decompose the video into spatiotemporal regions. We show an example application of activity concept detection using the trajectories extracted from the spatio-temporal regions. The proposed approach shows good potential for concise representation and indexing of objects and their motion in real-life consumer video.

Deliberate and Crisis Action Planning and Execution Segments Increment 2B (DCAPES Inc 2B)

DTIC Science & Technology

2016-03-01

2016 Major Automated Information System Annual Report Deliberate and Crisis Action Planning and Execution Segments Increment 2B (DCAPES Inc 2B...Defense Acquisition Management Information Retrieval (DAMIR) UNCLASSIFIED DCAPES Inc 2B 2016 MAR UNCLASSIFIED 2 Table of Contents Common...Logistics DCAPES Inc 2B 2016 MAR UNCLASSIFIED 3 Lt Col Christopher Thrower 201 East Moore Drive Building 856, Room 154 Maxwell Air Force Base-Gunter

Cortical reinstatement and the confidence and accuracy of source memory.

PubMed

Thakral, Preston P; Wang, Tracy H; Rugg, Michael D

2015-04-01

Cortical reinstatement refers to the overlap between neural activity elicited during the encoding and the subsequent retrieval of an episode, and is held to reflect retrieved mnemonic content. Previous findings have demonstrated that reinstatement effects reflect the quality of retrieved episodic information as this is operationalized by the accuracy of source memory judgments. The present functional magnetic resonance imaging (fMRI) study investigated whether reinstatement-related activity also co-varies with the confidence of accurate source judgments. Participants studied pictures of objects along with their visual or spoken names. At test, they first discriminated between studied and unstudied pictures and then, for each picture judged as studied, they also judged whether it had been paired with a visual or auditory name, using a three-point confidence scale. Accuracy of source memory judgments- and hence the quality of the source-specifying information--was greater for high than for low confidence judgments. Modality-selective retrieval-related activity (reinstatement effects) also co-varied with the confidence of the corresponding source memory judgment. The findings indicate that the quality of the information supporting accurate judgments of source memory is indexed by the relative magnitude of content-selective, retrieval-related neural activity. Copyright © 2015 Elsevier Inc. All rights reserved.

Method for the reduction of image content redundancy in large image databases

DOEpatents

Tobin, Kenneth William; Karnowski, Thomas P.

2010-03-02

A method of increasing information content for content-based image retrieval (CBIR) systems includes the steps of providing a CBIR database, the database having an index for a plurality of stored digital images using a plurality of feature vectors, the feature vectors corresponding to distinct descriptive characteristics of the images. A visual similarity parameter value is calculated based on a degree of visual similarity between features vectors of an incoming image being considered for entry into the database and feature vectors associated with a most similar of the stored images. Based on said visual similarity parameter value it is determined whether to store or how long to store the feature vectors associated with the incoming image in the database.

Automatic indexing of compound words based on mutual information for Korean text retrieval

DOE Office of Scientific and Technical Information (OSTI.GOV)

Pan Koo Kim; Yoo Kun Cho

In this paper, we present an automatic indexing technique for compound words suitable to an aggulutinative language, specifically Korean. Firstly, we present the construction conditions to compose compound words as indexing terms. Also we present the decomposition rules applicable to consecutive nouns to extract all contents of text. Finally we propose a measure to estimate the usefulness of a term, mutual information, to calculate the degree of word association of compound words, based on the information theoretic notion. By applying this method, our system has raised the precision rate of compound words from 72% to 87%.

A Bayesian approach to microwave precipitation profile retrieval

NASA Technical Reports Server (NTRS)

Evans, K. Franklin; Turk, Joseph; Wong, Takmeng; Stephens, Graeme L.

1995-01-01

A multichannel passive microwave precipitation retrieval algorithm is developed. Bayes theorem is used to combine statistical information from numerical cloud models with forward radiative transfer modeling. A multivariate lognormal prior probability distribution contains the covariance information about hydrometeor distribution that resolves the nonuniqueness inherent in the inversion process. Hydrometeor profiles are retrieved by maximizing the posterior probability density for each vector of observations. The hydrometeor profile retrieval method is tested with data from the Advanced Microwave Precipitation Radiometer (10, 19, 37, and 85 GHz) of convection over ocean and land in Florida. The CP-2 multiparameter radar data are used to verify the retrieved profiles. The results show that the method can retrieve approximate hydrometeor profiles, with larger errors over land than water. There is considerably greater accuracy in the retrieval of integrated hydrometeor contents than of profiles. Many of the retrieval errors are traced to problems with the cloud model microphysical information, and future improvements to the algorithm are suggested.

Cloud information content analysis of multi-angular measurements in the oxygen A-band: application to 3MI and MSPI

NASA Astrophysics Data System (ADS)

Merlin, G.; Riedi, J.; Labonnote, L. C.; Cornet, C.; Davis, A. B.; Dubuisson, P.; Desmons, M.; Ferlay, N.; Parol, F.

2015-12-01

The vertical distribution of cloud cover has a significant impact on a large number of meteorological and climatic processes. Cloud top altitude and cloud geometrical thickness are then essential. Previous studies established the possibility of retrieving those parameters from multi-angular oxygen A-band measurements. Here we perform a study and comparison of the performances of future instruments. The 3MI (Multi-angle, Multi-channel and Multi-polarization Imager) instrument developed by EUMETSAT, which is an extension of the POLDER/PARASOL instrument, and MSPI (Multi-angles Spectro-Polarimetric Imager) develoloped by NASA's Jet Propulsion Laboratory will measure total and polarized light reflected by the Earth's atmosphere-surface system in several spectral bands (from UV to SWIR) and several viewing geometries. Those instruments should provide opportunities to observe the links between the cloud structures and the anisotropy of the reflected solar radiation into space. Specific algorithms will need be developed in order to take advantage of the new capabilities of this instrument. However, prior to this effort, we need to understand, through a theoretical Shannon information content analysis, the limits and advantages of these new instruments for retrieving liquid and ice cloud properties, and especially, in this study, the amount of information coming from the A-Band channel on the cloud top altitude (CTOP) and geometrical thickness (CGT). We compare the information content of 3MI A-Band in two configurations and that of MSPI. Quantitative information content estimates show that the retrieval of CTOP with a high accuracy is possible in almost all cases investigated. The retrieval of CGT seems less easy but possible for optically thick clouds above a black surface, at least when CGT > 1-2 km.

The association of personal semantic memory to identity representations: insight into higher-order networks of autobiographical contents.

PubMed

Grilli, Matthew D

2017-11-01

Identity representations are higher-order knowledge structures that organise autobiographical memories on the basis of personality and role-based themes of one's self-concept. In two experiments, the extent to which different types of personal semantic content are reflected in these higher-order networks of memories was investigated. Healthy, young adult participants generated identity representations that varied in remoteness of formation and verbally reflected on these themes in an open-ended narrative task. The narrative responses were scored for retrieval of episodic, experience-near personal semantic and experience-far (i.e., abstract) personal semantic contents. Results revealed that to reflect on remotely formed identity representations, experience-far personal semantic contents were retrieved more than experience-near personal semantic contents. In contrast, to reflect on recently formed identity representations, experience-near personal semantic contents were retrieved more than experience-far personal semantic contents. Although episodic memory contents were retrieved less than both personal semantic content types to reflect on remotely formed identity representations, this content type was retrieved at a similar frequency as experience-far personal semantic content to reflect on recently formed identity representations. These findings indicate that the association of personal semantic content to identity representations is robust and related to time since acquisition of these knowledge structures.

Information content analysis: the potential for methane isotopologue retrieval from GOSAT-2

NASA Astrophysics Data System (ADS)

Malina, Edward; Yoshida, Yukio; Matsunaga, Tsuneo; Muller, Jan-Peter

2018-02-01

Atmospheric methane is comprised of multiple isotopic molecules, with the most abundant being 12CH4 and 13CH4, making up 98 and 1.1 % of atmospheric methane respectively. It has been shown that is it possible to distinguish between sources of methane (biogenic methane, e.g. marshland, or abiogenic methane, e.g. fracking) via a ratio of these main methane isotopologues, otherwise known as the δ13C value. δ13C values typically range between -10 and -80 ‰, with abiogenic sources closer to zero and biogenic sources showing more negative values. Initially, we suggest that a δ13C difference of 10 ‰ is sufficient, in order to differentiate between methane source types, based on this we derive that a precision of 0.2 ppbv on 13CH4 retrievals may achieve the target δ13C variance. Using an application of the well-established information content analysis (ICA) technique for assumed clear-sky conditions, this paper shows that using a combination of the shortwave infrared (SWIR) bands on the planned Greenhouse gases Observing SATellite (GOSAT-2) mission, 13CH4 can be measured with sufficient information content to a precision of between 0.7 and 1.2 ppbv from a single sounding (assuming a total column average value of 19.14 ppbv), which can then be reduced to the target precision through spatial and temporal averaging techniques. We therefore suggest that GOSAT-2 can be used to differentiate between methane source types. We find that large unconstrained covariance matrices are required in order to achieve sufficient information content, while the solar zenith angle has limited impact on the information content.

On Information Retrieval (IR) Systems: Revisiting Their Development, Evaluation Methodologies, and Assumptions (SIGs LAN, ED).

ERIC Educational Resources Information Center

Stirling, Keith

2000-01-01

Describes a session on information retrieval systems that planned to discuss relevance measures with Web-based information retrieval; retrieval system performance and evaluation; probabilistic independence of index terms; vector-based models; metalanguages and digital objects; how users assess the reliability, timeliness and bias of information;…

Competitive retrieval is not a prerequisite for forgetting in the retrieval practice paradigm.

PubMed

Camp, Gino; Dalm, Sander

2016-09-01

Retrieving information from memory can lead to forgetting of other, related information. The inhibition account of this retrieval-induced forgetting effect predicts that this form of forgetting occurs when competition arises between the practiced information and the related information, leading to inhibition of the related information. In the standard retrieval practice paradigm, a retrieval practice task is used in which participants retrieve the items based on a category-plus-stem cue (e.g., FRUIT-or___). In the current experiment, participants instead generated the target based on a cue in which the first 2 letters of the target were transposed (e.g., FRUIT-roange). This noncompetitive task also induced forgetting of unpracticed items from practiced categories. This finding is inconsistent with the inhibition account, which asserts that the forgetting effect depends on competitive retrieval. We argue that interference-based accounts of forgetting and the context-based account of retrieval-induced forgetting can account for this result. (PsycINFO Database Record (c) 2016 APA, all rights reserved).

A hierarchical SVG image abstraction layer for medical imaging

NASA Astrophysics Data System (ADS)

Kim, Edward; Huang, Xiaolei; Tan, Gang; Long, L. Rodney; Antani, Sameer

2010-03-01

As medical imaging rapidly expands, there is an increasing need to structure and organize image data for efficient analysis, storage and retrieval. In response, a large fraction of research in the areas of content-based image retrieval (CBIR) and picture archiving and communication systems (PACS) has focused on structuring information to bridge the "semantic gap", a disparity between machine and human image understanding. An additional consideration in medical images is the organization and integration of clinical diagnostic information. As a step towards bridging the semantic gap, we design and implement a hierarchical image abstraction layer using an XML based language, Scalable Vector Graphics (SVG). Our method encodes features from the raw image and clinical information into an extensible "layer" that can be stored in a SVG document and efficiently searched. Any feature extracted from the raw image including, color, texture, orientation, size, neighbor information, etc., can be combined in our abstraction with high level descriptions or classifications. And our representation can natively characterize an image in a hierarchical tree structure to support multiple levels of segmentation. Furthermore, being a world wide web consortium (W3C) standard, SVG is able to be displayed by most web browsers, interacted with by ECMAScript (standardized scripting language, e.g. JavaScript, JScript), and indexed and retrieved by XML databases and XQuery. Using these open source technologies enables straightforward integration into existing systems. From our results, we show that the flexibility and extensibility of our abstraction facilitates effective storage and retrieval of medical images.

Dialog detection in narrative video by shot and face analysis

NASA Astrophysics Data System (ADS)

Kroon, B.; Nesvadba, J.; Hanjalic, A.

2007-01-01

The proliferation of captured personal and broadcast content in personal consumer archives necessitates comfortable access to stored audiovisual content. Intuitive retrieval and navigation solutions require however a semantic level that cannot be reached by generic multimedia content analysis alone. A fusion with film grammar rules can help to boost the reliability significantly. The current paper describes the fusion of low-level content analysis cues including face parameters and inter-shot similarities to segment commercial content into film grammar rule-based entities and subsequently classify those sequences into so-called shot reverse shots, i.e. dialog sequences. Moreover shot reverse shot specific mid-level cues are analyzed augmenting the shot reverse shot information with dialog specific descriptions.

EPIC/DSCOVR's Oxygen Absorption Channels: A Cloud Profiling Information Content Analysis

NASA Astrophysics Data System (ADS)

Davis, A. B.; Merlin, G.; Labonnote, L. C.; Cornet, C.; Dubuisson, P.; Ferlay, N.; Parol, F.; Riedi, J.; Yang, Y.

2016-12-01

EPIC/DSCOVR has several spectral channels dedicated to cloud characterization, most notably O2 A- and B-band. Differential optical absorption spectroscopy (DOAS) ratios of in-band and reference channels are less prone to calibration error than the 4 individual signals. Using these ratios, we have replicated for mono-directional (quasi-backscattering) EPIC observations the recent cloud information content analysis by Merlin et al. (AMT-D,8:12709-12758,2015) that was focused on A-band-only but multi-angle observations by POLDER in the past, by AirMSPI in the present, and by 3MI and MAIA in the future. The methodology is based on extensive forward 1D radiative transfer (RT) computations using the ARTDECO model that implements a k-distribution technique for the absorbing (in-band) channels. These synthetic signals are combined into a Bayesian Rodgers-type framework for estimating posterior uncertainty on retrieved quantities. Recall that this formalism calls explicitly for: (1) estimates of instrument error, and (2) prior uncertainty on the retrieved quantities, to which we add (3) reasonable estimates of uncertainty in the non- or otherwise-retrieved properties. Wide ranges of cloud top heights (CTHs) and cloud geometrical thicknesses (CGTs) are examined for a representative selection of cloud optical thicknesses (COTs), solar angles, and surface reflectances. We found that CTH should be reliably retrieved from EPIC data under most circumstances as long as COT can be inferred from non-absorbing channels, and the bias from in-cloud absorption is removed. However, CGT will be hard to determine unless CTH is constrained by independent means. EPIC has several UV channels that could be brought to bear. These findings conflict those of Yang et al. (JQSRT,122:141-149,2013), so we also revisit that more preliminary study that did not account for a realistic level of residual instrument noise in the DOAS ratios. In conclusion, we believe that the present information content analysis will inform the EPIC/DSCOVR Level 2 algorithm development team about what cloud properties to target using the A/B-band channels, depending on the availability of other cloud information.

A novel 3D shape descriptor for automatic retrieval of anatomical structures from medical images

NASA Astrophysics Data System (ADS)

Nunes, Fátima L. S.; Bergamasco, Leila C. C.; Delmondes, Pedro H.; Valverde, Miguel A. G.; Jackowski, Marcel P.

2017-03-01

Content-based image retrieval (CBIR) aims at retrieving from a database objects that are similar to an object provided by a query, by taking into consideration a set of extracted features. While CBIR has been widely applied in the two-dimensional image domain, the retrieval of3D objects from medical image datasets using CBIR remains to be explored. In this context, the development of descriptors that can capture information specific to organs or structures is desirable. In this work, we focus on the retrieval of two anatomical structures commonly imaged by Magnetic Resonance Imaging (MRI) and Computed Tomography (CT) techniques, the left ventricle of the heart and blood vessels. Towards this aim, we developed the Area-Distance Local Descriptor (ADLD), a novel 3D local shape descriptor that employs mesh geometry information, namely facet area and distance from centroid to surface, to identify shape changes. Because ADLD only considers surface meshes extracted from volumetric medical images, it substantially diminishes the amount of data to be analyzed. A 90% precision rate was obtained when retrieving both convex (left ventricle) and non-convex structures (blood vessels), allowing for detection of abnormalities associated with changes in shape. Thus, ADLD has the potential to aid in the diagnosis of a wide range of vascular and cardiac diseases.

An Information Retrieval and Recommendation System for Astronomical Observatories

NASA Astrophysics Data System (ADS)

Mukund, Nikhil; Thakur, Saurabh; Abraham, Sheelu; Aniyan, A. K.; Mitra, Sanjit; Sajeeth Philip, Ninan; Vaghmare, Kaustubh; Acharjya, D. P.

2018-03-01

We present a machine-learning-based information retrieval system for astronomical observatories that tries to address user-defined queries related to an instrument. In the modern instrumentation scenario where heterogeneous systems and talents are simultaneously at work, the ability to supply people with the right information helps speed up the tasks for detector operation, maintenance, and upgradation. The proposed method analyzes existing documented efforts at the site to intelligently group related information to a query and to present it online to the user. The user in response can probe the suggested content and explore previously developed solutions or probable ways to address the present situation optimally. We demonstrate natural language-processing-backed knowledge rediscovery by making use of the open source logbook data from the Laser Interferometric Gravitational Observatory (LIGO). We implement and test a web application that incorporates the above idea for LIGO Livingston, LIGO Hanford, and Virgo observatories.

Multi-Modal Surrogates for Retrieving and Making Sense of Videos: Is Synchronization between the Multiple Modalities Optimal?

ERIC Educational Resources Information Center

Song, Yaxiao

2010-01-01

Video surrogates can help people quickly make sense of the content of a video before downloading or seeking more detailed information. Visual and audio features of a video are primary information carriers and might become important components of video retrieval and video sense-making. In the past decades, most research and development efforts on…

Toward an Episodic Context Account of Retrieval-Based Learning: Dissociating Retrieval Practice and Elaboration

ERIC Educational Resources Information Center

Lehman, Melissa; Smith, Megan A.; Karpicke, Jeffrey D.

2014-01-01

We tested the predictions of 2 explanations for retrieval-based learning; while the elaborative retrieval hypothesis assumes that the retrieval of studied information promotes the generation of semantically related information, which aids in later retrieval (Carpenter, 2009), the episodic context account proposed by Karpicke, Lehman, and Aue (in…

Latent Semantic Analysis as a Method of Content-Based Image Retrieval in Medical Applications

ERIC Educational Resources Information Center

Makovoz, Gennadiy

2010-01-01

The research investigated whether a Latent Semantic Analysis (LSA)-based approach to image retrieval can map pixel intensity into a smaller concept space with good accuracy and reasonable computational cost. From a large set of M computed tomography (CT) images, a retrieval query found all images for a particular patient based on semantic…

Automatic information timeliness assessment of diabetes web sites by evidence based medicine.

PubMed

Sağlam, Rahime Belen; Taşkaya Temizel, Tuğba

2014-11-01

Studies on health domain have shown that health websites provide imperfect information and give recommendations which are not up to date with the recent literature even when their last modified dates are quite recent. In this paper, we propose a framework which assesses the timeliness of the content of health websites automatically by evidence based medicine. Our aim is to assess the accordance of website contents with the current literature and information timeliness disregarding the update time stated on the websites. The proposed method is based on automatic term recognition, relevance feedback and information retrieval techniques in order to generate time-aware structured queries. We tested the framework on diabetes health web sites which were archived between 2006 and 2013 by Archive-it using American Diabetes Association's (ADA) guidelines. The results showed that the proposed framework achieves 65% and 77% accuracy in detecting the timeliness of the web content according to years and pre-determined time intervals respectively. Information seekers and web site owners may benefit from the proposed framework in finding relevant and up-to-date diabetes web sites. Copyright © 2014 Elsevier Ireland Ltd. All rights reserved.

Effectiveness of image features and similarity measures in cluster-based approaches for content-based image retrieval

NASA Astrophysics Data System (ADS)

Du, Hongbo; Al-Jubouri, Hanan; Sellahewa, Harin

2014-05-01

Content-based image retrieval is an automatic process of retrieving images according to image visual contents instead of textual annotations. It has many areas of application from automatic image annotation and archive, image classification and categorization to homeland security and law enforcement. The key issues affecting the performance of such retrieval systems include sensible image features that can effectively capture the right amount of visual contents and suitable similarity measures to find similar and relevant images ranked in a meaningful order. Many different approaches, methods and techniques have been developed as a result of very intensive research in the past two decades. Among many existing approaches, is a cluster-based approach where clustering methods are used to group local feature descriptors into homogeneous regions, and search is conducted by comparing the regions of the query image against those of the stored images. This paper serves as a review of works in this area. The paper will first summarize the existing work reported in the literature and then present the authors' own investigations in this field. The paper intends to highlight not only achievements made by recent research but also challenges and difficulties still remaining in this area.

Deeply learnt hashing forests for content based image retrieval in prostate MR images

NASA Astrophysics Data System (ADS)

Shah, Amit; Conjeti, Sailesh; Navab, Nassir; Katouzian, Amin

2016-03-01

Deluge in the size and heterogeneity of medical image databases necessitates the need for content based retrieval systems for their efficient organization. In this paper, we propose such a system to retrieve prostate MR images which share similarities in appearance and content with a query image. We introduce deeply learnt hashing forests (DL-HF) for this image retrieval task. DL-HF effectively leverages the semantic descriptiveness of deep learnt Convolutional Neural Networks. This is used in conjunction with hashing forests which are unsupervised random forests. DL-HF hierarchically parses the deep-learnt feature space to encode subspaces with compact binary code words. We propose a similarity preserving feature descriptor called Parts Histogram which is derived from DL-HF. Correlation defined on this descriptor is used as a similarity metric for retrieval from the database. Validations on publicly available multi-center prostate MR image database established the validity of the proposed approach. The proposed method is fully-automated without any user-interaction and is not dependent on any external image standardization like image normalization and registration. This image retrieval method is generalizable and is well-suited for retrieval in heterogeneous databases other imaging modalities and anatomies.

Retrieving the unretrievable in electronic imaging systems: emotions, themes, and stories

NASA Astrophysics Data System (ADS)

Joergensen, Corinne

1999-05-01

New paradigms such as 'affective computing' and user-based research are extending the realm of facets traditionally addressed in IR systems. This paper builds on previous research reported to the electronic imaging community concerning the need to provide access to more abstract attributes of images than those currently amenable to a variety of content-based and text-based indexing techniques. Empirical research suggest that, for visual materials, in addition to standard bibliographic data and broad subject, and in addition to such visually perceptual attributes such as color, texture, shape, and position or focal point, additional access points such as themes, abstract concepts, emotions, stories, and 'people-related' information such as social status would be useful in image retrieval. More recent research demonstrates that similar results are also obtained with 'fine arts' images, which generally have no access provided for these types of attributes. Current efforts to match image attributes as revealed in empirical research with those addressed both in current textural and content-based indexing systems are discussed, as well as the need for new representations for image attributes and for collaboration among diverse communities of researchers.

Information content of visible and midinfrared radiances for retrieving tropical ice cloud properties

NASA Astrophysics Data System (ADS)

Chang, Kai-Wei; L'Ecuyer, Tristan S.; Kahn, Brian H.; Natraj, Vijay

2017-05-01

Hyperspectral instruments such as Atmospheric Infrared Sounder (AIRS) have spectrally dense observations effective for ice cloud retrievals. However, due to the large number of channels, only a small subset is typically used. It is crucial that this subset of channels be chosen to contain the maximum possible information about the retrieved variables. This study describes an information content analysis designed to select optimal channels for ice cloud retrievals. To account for variations in ice cloud properties, we perform channel selection over an ensemble of cloud regimes, extracted with a clustering algorithm, from a multiyear database at a tropical Atmospheric Radiation Measurement site. Multiple satellite viewing angles over land and ocean surfaces are considered to simulate the variations in observation scenarios. The results suggest that AIRS channels near wavelengths of 14, 10.4, 4.2, and 3.8 μm contain the most information. With an eye toward developing a joint AIRS-MODIS (Moderate Resolution Imaging Spectroradiometer) retrieval, the analysis is also applied to combined measurements from both instruments. While application of this method to MODIS yields results consistent with previous channel sensitivity studies, the analysis shows that this combination may yield substantial improvement in cloud retrievals. MODIS provides most information on optical thickness and particle size, aided by a better constraint on cloud vertical placement from AIRS. An alternate scenario where cloud top boundaries are supplied by the active sensors in the A-train is also explored. The more robust cloud placement afforded by active sensors shifts the optimal channels toward the window region and shortwave infrared, further constraining optical thickness and particle size.

Content-Based Image Retrieval System for Pulmonary Nodules: Assisting Radiologists in Self-Learning and Diagnosis of Lung Cancer.

PubMed

Dhara, Ashis Kumar; Mukhopadhyay, Sudipta; Dutta, Anirvan; Garg, Mandeep; Khandelwal, Niranjan

2017-02-01

Visual information of similar nodules could assist the budding radiologists in self-learning. This paper presents a content-based image retrieval (CBIR) system for pulmonary nodules, observed in lung CT images. The reported CBIR systems of pulmonary nodules cannot be put into practice as radiologists need to draw the boundary of nodules during query formation and feature database creation. In the proposed retrieval system, the pulmonary nodules are segmented using a semi-automated technique, which requires a seed point on the nodule from the end-user. The involvement of radiologists in feature database creation is also reduced, as only a seed point is expected from radiologists instead of manual delineation of the boundary of the nodules. The performance of the retrieval system depends on the accuracy of the segmentation technique. Several 3D features are explored to improve the performance of the proposed retrieval system. A set of relevant shape and texture features are considered for efficient representation of the nodules in the feature space. The proposed CBIR system is evaluated for three configurations such as configuration-1 (composite rank of malignancy "1","2" as benign and "4","5" as malignant), configuration-2 (composite rank of malignancy "1","2", "3" as benign and "4","5" as malignant), and configuration-3 (composite rank of malignancy "1","2" as benign and "3","4","5" as malignant). Considering top 5 retrieved nodules and Euclidean distance metric, the precision achieved by the proposed method for configuration-1, configuration-2, and configuration-3 are 82.14, 75.91, and 74.27 %, respectively. The performance of the proposed CBIR system is close to the most recent technique, which is dependent on radiologists for manual segmentation of nodules. A computer-aided diagnosis (CAD) system is also developed based on CBIR paradigm. Performance of the proposed CBIR-based CAD system is close to performance of the CAD system using support vector machine.

Three-dimensional spatiotemporal features for fast content-based retrieval of focal liver lesions.

PubMed

Roy, Sharmili; Chi, Yanling; Liu, Jimin; Venkatesh, Sudhakar K; Brown, Michael S

2014-11-01

Content-based image retrieval systems for 3-D medical datasets still largely rely on 2-D image-based features extracted from a few representative slices of the image stack. Most 2 -D features that are currently used in the literature not only model a 3-D tumor incompletely but are also highly expensive in terms of computation time, especially for high-resolution datasets. Radiologist-specified semantic labels are sometimes used along with image-based 2-D features to improve the retrieval performance. Since radiological labels show large interuser variability, are often unstructured, and require user interaction, their use as lesion characterizing features is highly subjective, tedious, and slow. In this paper, we propose a 3-D image-based spatiotemporal feature extraction framework for fast content-based retrieval of focal liver lesions. All the features are computer generated and are extracted from four-phase abdominal CT images. Retrieval performance and query processing times for the proposed framework is evaluated on a database of 44 hepatic lesions comprising of five pathological types. Bull's eye percentage score above 85% is achieved for three out of the five lesion pathologies and for 98% of query lesions, at least one same type of lesion is ranked among the top two retrieved results. Experiments show that the proposed system's query processing is more than 20 times faster than other already published systems that use 2-D features. With fast computation time and high retrieval accuracy, the proposed system has the potential to be used as an assistant to radiologists for routine hepatic tumor diagnosis.

Exploring access to scientific literature using content-based image retrieval

NASA Astrophysics Data System (ADS)

Deserno, Thomas M.; Antani, Sameer; Long, Rodney

2007-03-01

The number of articles published in the scientific medical literature is continuously increasing, and Web access to the journals is becoming common. Databases such as SPIE Digital Library, IEEE Xplore, indices such as PubMed, and search engines such as Google provide the user with sophisticated full-text search capabilities. However, information in images and graphs within these articles is entirely disregarded. In this paper, we quantify the potential impact of using content-based image retrieval (CBIR) to access this non-text data. Based on the Journal Citations Report (JCR), the journal Radiology was selected for this study. In 2005, 734 articles were published electronically in this journal. This included 2,587 figures, which yields a rate of 3.52 figures per article. Furthermore, 56.4% of these figures are composed of several individual panels, i.e. the figure combines different images and/or graphs. According to the Image Cross-Language Evaluation Forum (ImageCLEF), the error rate of automatic identification of medical images is about 15%. Therefore, it is expected that, by applying ImageCLEF-like techniques, already 95.5% of articles could be retrieved by means of CBIR. The challenge for CBIR in scientific literature, however, is the use of local texture properties to analyze individual image panels in composite illustrations. Using local features for content-based image representation, 8.81 images per article are available, and the predicted correctness rate may increase to 98.3%. From this study, we conclude that CBIR may have a high impact in medical literature research and suggest that additional research in this area is warranted.

Data augmentation-assisted deep learning of hand-drawn partially colored sketches for visual search

PubMed Central

Muhammad, Khan; Baik, Sung Wook

2017-01-01

In recent years, image databases are growing at exponential rates, making their management, indexing, and retrieval, very challenging. Typical image retrieval systems rely on sample images as queries. However, in the absence of sample query images, hand-drawn sketches are also used. The recent adoption of touch screen input devices makes it very convenient to quickly draw shaded sketches of objects to be used for querying image databases. This paper presents a mechanism to provide access to visual information based on users’ hand-drawn partially colored sketches using touch screen devices. A key challenge for sketch-based image retrieval systems is to cope with the inherent ambiguity in sketches due to the lack of colors, textures, shading, and drawing imperfections. To cope with these issues, we propose to fine-tune a deep convolutional neural network (CNN) using augmented dataset to extract features from partially colored hand-drawn sketches for query specification in a sketch-based image retrieval framework. The large augmented dataset contains natural images, edge maps, hand-drawn sketches, de-colorized, and de-texturized images which allow CNN to effectively model visual contents presented to it in a variety of forms. The deep features extracted from CNN allow retrieval of images using both sketches and full color images as queries. We also evaluated the role of partial coloring or shading in sketches to improve the retrieval performance. The proposed method is tested on two large datasets for sketch recognition and sketch-based image retrieval and achieved better classification and retrieval performance than many existing methods. PMID:28859140

Content-based image retrieval in medical applications for picture archiving and communication systems

NASA Astrophysics Data System (ADS)

Lehmann, Thomas M.; Guld, Mark O.; Thies, Christian; Fischer, Benedikt; Keysers, Daniel; Kohnen, Michael; Schubert, Henning; Wein, Berthold B.

2003-05-01

Picture archiving and communication systems (PACS) aim to efficiently provide the radiologists with all images in a suitable quality for diagnosis. Modern standards for digital imaging and communication in medicine (DICOM) comprise alphanumerical descriptions of study, patient, and technical parameters. Currently, this is the only information used to select relevant images within PACS. Since textual descriptions insufficiently describe the great variety of details in medical images, content-based image retrieval (CBIR) is expected to have a strong impact when integrated into PACS. However, existing CBIR approaches usually are limited to a distinct modality, organ, or diagnostic study. In this state-of-the-art report, we present first results implementing a general approach to content-based image retrieval in medical applications (IRMA) and discuss its integration into PACS environments. Usually, a PACS consists of a DICOM image server and several DICOM-compliant workstations, which are used by radiologists for reading the images and reporting the findings. Basic IRMA components are the relational database, the scheduler, and the web server, which all may be installed on the DICOM image server, and the IRMA daemons running on distributed machines, e.g., the radiologists" workstations. These workstations can also host the web-based front-ends of IRMA applications. Integrating CBIR and PACS, a special focus is put on (a) location and access transparency for data, methods, and experiments, (b) replication transparency for methods in development, (c) concurrency transparency for job processing and feature extraction, (d) system transparency at method implementation time, and (e) job distribution transparency when issuing a query. Transparent integration will have a certain impact on diagnostic quality supporting both evidence-based medicine and case-based reasoning.

Use of controlled vocabularies to improve biomedical information retrieval tasks.

PubMed

Pasche, Emilie; Gobeill, Julien; Vishnyakova, Dina; Ruch, Patrick; Lovis, Christian

2013-01-01

The high heterogeneity of biomedical vocabulary is a major obstacle for information retrieval in large biomedical collections. Therefore, using biomedical controlled vocabularies is crucial for managing these contents. We investigate the impact of query expansion based on controlled vocabularies to improve the effectiveness of two search engines. Our strategy relies on the enrichment of users' queries with additional terms, directly derived from such vocabularies applied to infectious diseases and chemical patents. We observed that query expansion based on pathogen names resulted in improvements of the top-precision of our first search engine, while the normalization of diseases degraded the top-precision. The expansion of chemical entities, which was performed on the second search engine, positively affected the mean average precision. We have shown that query expansion of some types of biomedical entities has a great potential to improve search effectiveness; therefore a fine-tuning of query expansion strategies could help improving the performances of search engines.

AOIPS data base management systems support for GARP data sets

NASA Technical Reports Server (NTRS)

Gary, J. P.

1977-01-01

A data base management system is identified, developed to provide flexible access to data sets produced by GARP during its data systems tests. The content and coverage of the data base are defined and a computer-aided, interactive information storage and retrieval system, implemented to facilitate access to user specified data subsets, is described. The computer programs developed to provide the capability were implemented on the highly interactive, minicomputer-based AOIPS and are referred to as the data retrieval system (DRS). Implemented as a user interactive but menu guided system, the DRS permits users to inventory the data tape library and create duplicate or subset data sets based on a user selected window defined by time and latitude/longitude boundaries. The DRS permits users to select, display, or produce formatted hard copy of individual data items contained within the data records.

Retrieving clinically relevant diabetic retinopathy images using a multi-class multiple-instance framework

NASA Astrophysics Data System (ADS)

Chandakkar, Parag S.; Venkatesan, Ragav; Li, Baoxin

2013-02-01

Diabetic retinopathy (DR) is a vision-threatening complication from diabetes mellitus, a medical condition that is rising globally. Unfortunately, many patients are unaware of this complication because of absence of symptoms. Regular screening of DR is necessary to detect the condition for timely treatment. Content-based image retrieval, using archived and diagnosed fundus (retinal) camera DR images can improve screening efficiency of DR. This content-based image retrieval study focuses on two DR clinical findings, microaneurysm and neovascularization, which are clinical signs of non-proliferative and proliferative diabetic retinopathy. The authors propose a multi-class multiple-instance image retrieval framework which deploys a modified color correlogram and statistics of steerable Gaussian Filter responses, for retrieving clinically relevant images from a database of DR fundus image database.

Aerosol Retrievals from Proposed Satellite Bistatic Lidar Observations: Algorithm and Information Content

NASA Astrophysics Data System (ADS)

Alexandrov, M. D.; Mishchenko, M. I.

2017-12-01

Accurate aerosol retrievals from space remain quite challenging and typically involve solving a severely ill-posed inverse scattering problem. We suggested to address this ill-posedness by flying a bistatic lidar system. Such a system would consist of formation flying constellation of a primary satellite equipped with a conventional monostatic (backscattering) lidar and an additional platform hosting a receiver of the scattered laser light. If successfully implemented, this concept would combine the measurement capabilities of a passive multi-angle multi-spectral polarimeter with the vertical profiling capability of a lidar. Thus, bistatic lidar observations will be free of deficiencies affecting both monostatic lidar measurements (caused by the highly limited information content) and passive photopolarimetric measurements (caused by vertical integration and surface reflection).We present a preliminary aerosol retrieval algorithm for a bistatic lidar system consisting of a high spectral resolution lidar (HSRL) and an additional receiver flown in formation with it at a scattering angle of 165 degrees. This algorithm was applied to synthetic data generated using Mie-theory computations. The model/retrieval parameters in our tests were the effective radius and variance of the aerosol size distribution, complex refractive index of the particles, and their number concentration. Both mono- and bimodal aerosol mixtures were considered. Our algorithm allowed for definitive evaluation of error propagation from measurements to retrievals using a Monte Carlo technique, which involves random distortion of the observations and statistical characterization of the resulting retrieval errors. Our tests demonstrated that supplementing a conventional monostatic HSRL with an additional receiver dramatically increases the information content of the measurements and allows for a sufficiently accurate characterization of tropospheric aerosols.

Information operator approach applied to the retrieval of vertical distributions of atmospheric constituents from ground-based FTIR measurements

NASA Astrophysics Data System (ADS)

Senten, Cindy; de Mazière, Martine; Vanhaelewyn, Gauthier; Vigouroux, Corinne; Delmas, Robert

2010-05-01

The retrieval of information about the vertical distribution of an atmospheric absorber from high spectral resolution ground-based Fourier Transform infrared (FTIR) solar absorption spectra is an important issue in remote sensing. A frequently used technique at present is the optimal estimation method. This work introduces the application of an alternative method, namely the information operator approach (Doicu et al., 2007; Hoogen et al., 1999), for extracting the available information from such FTIR measurements. This approach has been implemented within the well-known retrieval code SFIT2, by adapting the optimal estimation method such as to take into account only the significant contributions to the solution. In particular, we demonstrate the feasibility of the method when applied to ground-based FTIR spectra taken at the southern (sub)tropical site Ile de La Réunion (21° S, 55° E) in 2007. A thorough comparison has been made between the retrieval results obtained with the original optimal estimation method and the ones obtained with the information operator approach, regarding profile and column stability, information content and corresponding full error budget evaluation. This has been done for the target species ozone (O3), methane (CH4), nitrous oxide (N2O), and carbon monoxide (CO). It is shown that the information operator approach performs well and is capable of achieving the same accuracy as optimal estimation, with a gain of stability and with the additional advantage of being less sensitive to the choice of a priori information as well as to the actual signal-to-noise ratio. Keywords: ground-based FTIR, solar absorption spectra, greenhouse gases, information operator approach References Doicu, A., Hilgers, S., von Bargen, A., Rozanov, A., Eichmann, K.-U., von Savigny, C., and Burrows, J.P.: Information operator approach and iterative regularization methods for atmospheric remote sensing, J. Quant. Spectrosc. Radiat. Transfer, 103, 340-350, 2007. Hoogen, R., Rozanov, V.V., and Burrows, J.P.: Ozone profiles from GOME satellite data: description and first validation, J. Geophys. Res., 104(D7), 8263-8280, 1999.

Interactive classification and content-based retrieval of tissue images

NASA Astrophysics Data System (ADS)

Aksoy, Selim; Marchisio, Giovanni B.; Tusk, Carsten; Koperski, Krzysztof

2002-11-01

We describe a system for interactive classification and retrieval of microscopic tissue images. Our system models tissues in pixel, region and image levels. Pixel level features are generated using unsupervised clustering of color and texture values. Region level features include shape information and statistics of pixel level feature values. Image level features include statistics and spatial relationships of regions. To reduce the gap between low-level features and high-level expert knowledge, we define the concept of prototype regions. The system learns the prototype regions in an image collection using model-based clustering and density estimation. Different tissue types are modeled using spatial relationships of these regions. Spatial relationships are represented by fuzzy membership functions. The system automatically selects significant relationships from training data and builds models which can also be updated using user relevance feedback. A Bayesian framework is used to classify tissues based on these models. Preliminary experiments show that the spatial relationship models we developed provide a flexible and powerful framework for classification and retrieval of tissue images.

Retrieval of Vertical Aerosol and Trace Gas Distributions from Polarization Sensitive Multi-Axis Differential Optical Absorption Spectroscopy (MAX-DOAS)

NASA Astrophysics Data System (ADS)

Tirpitz, Jan-Lukas; Friess, Udo; Platt, Ulrich

2017-04-01

An accurate knowledge of the vertical distribution of trace gases and aerosols is crucial for our understanding of the chemical and dynamical processes in the lower troposphere. Their accurate determination is typically only possible by means of laborious and expensive airborne in-situ measurements but in the recent decades, numerous promising ground-based remote sensing approaches have been developed. One of them is to infer vertical distributions from "Differential Optical Absorption Spectroscopy" (DOAS) measurements. DOAS is a technique to analyze UV- and visible radiation spectra of direct or scattered sunlight, which delivers information on different atmospheric parameters, integrated over the light path from space to the instrument. An appropriate set of DOAS measurements, recorded under different viewing directions (Multi-Axis DOAS) and thus different light path geometries, provides information on the atmospheric state. The vertical profiles of aerosol properties and trace gas concentrations can be retrieved from such a set by numerical inversion techniques, incorporating radiative transfer models. The information content of measured data is rarely sufficient for a well-constrained retrieval, particularly for atmospheric layers above 1 km. We showed in first simulations that, apart from spectral properties, the polarization state of skylight is likely to provide a significant amount of additional information on the atmospheric state and thus to enhance retrieval quality. We present first simulations, expectations and ideas on how to implement and characterize a polarization sensitive Multi-Axis DOAS instrument and a corresponding profile retrieval algorithm.

Comparison of quality of internet pages on human papillomavirus immunization in Italian and in English.

PubMed

Tozzi, Alberto Eugenio; Buonuomo, Paola Sabrina; Ciofi degli Atti, Marta Luisa; Carloni, Emanuela; Meloni, Marco; Gamba, Fiorenza

2010-01-01

Information available on the Internet about immunizations may influence parents' perception about human papillomavirus (HPV) immunization and their attitude toward vaccinating their daughters. We hypothesized that the quality of information on HPV available on the Internet may vary with language and with the level of knowledge of parents. To this end we compared the quality of a sample of Web pages in Italian with a sample of Web pages in English. Five reviewers assessed the quality of Web pages retrieved with popular search engines using criteria adapted from the Good Information Practice Essential Criteria for Vaccine Safety Web Sites recommended by the World Health Organization. Quality of Web pages was assessed in the domains of accessibility, credibility, content, and design. Scores in these domains were compared through nonparametric statistical tests. We retrieved and reviewed 74 Web sites in Italian and 117 in English. Most retrieved Web pages (33.5%) were from private agencies. Median scores were higher in Web pages in English compared with those in Italian in the domain of accessibility (p < .01), credibility (p < .01), and content (p < .01). The highest credibility and content scores were those of Web pages from governmental agencies or universities. Accessibility scores were positively associated with content scores (p < .01) and with credibility scores (p < .01). A total of 16.2% of Web pages in Italian opposed HPV immunization compared with 6.0% of those in English (p < .05). Quality of information and number of Web pages opposing HPV immunization may vary with the Web site language. High-quality Web pages on HPV, especially from public health agencies and universities, should be easily accessible and retrievable with common Web search engines. Copyright 2010 Society for Adolescent Medicine. Published by Elsevier Inc. All rights reserved.

Cloud Retrieval Information Content Studies with the Pre-Aerosol, Cloud and ocean Ecosystem (PACE) Ocean Color Imager (OCI)

NASA Astrophysics Data System (ADS)

Coddington, Odele; Platnick, Steven; Pilewskie, Peter; Schmidt, Sebastian

2016-04-01

The NASA Pre-Aerosol, Cloud and ocean Ecosystem (PACE) Science Definition Team (SDT) report released in 2012 defined imager stability requirements for the Ocean Color Instrument (OCI) at the sub-percent level. While the instrument suite and measurement requirements are currently being determined, the PACE SDT report provided details on imager options and spectral specifications. The options for a threshold instrument included a hyperspectral imager from 350-800 nm, two near-infrared (NIR) channels, and three short wave infrared (SWIR) channels at 1240, 1640, and 2130 nm. Other instrument options include a variation of the threshold instrument with 3 additional spectral channels at 940, 1378, and 2250 nm and the inclusion of a spectral polarimeter. In this work, we present cloud retrieval information content studies of optical thickness, droplet effective radius, and thermodynamic phase to quantify the potential for continuing the low cloud climate data record established by the MOderate Resolution and Imaging Spectroradiometer (MODIS) and Visible Infrared Imaging Radiometer Suite (VIIRS) missions with the PACE OCI instrument (i.e., non-polarized cloud reflectances and in the absence of midwave and longwave infrared channels). The information content analysis is performed using the GEneralized Nonlinear Retrieval Analysis (GENRA) methodology and the Collection 6 simulated cloud reflectance data for the common MODIS/VIIRS algorithm (MODAWG) for Cloud Mask, Cloud-Top, and Optical Properties. We show that using both channels near 2 microns improves the probability of cloud phase discrimination with shortwave-only cloud reflectance retrievals. Ongoing work will extend the information content analysis, currently performed for dark ocean surfaces, to different land surface types.

Fast content-based image retrieval using dynamic cluster tree

NASA Astrophysics Data System (ADS)

Chen, Jinyan; Sun, Jizhou; Wu, Rongteng; Zhang, Yaping

2008-03-01

A novel content-based image retrieval data structure is developed in present work. It can improve the searching efficiency significantly. All images are organized into a tree, in which every node is comprised of images with similar features. Images in a children node have more similarity (less variance) within themselves in relative to its parent. It means that every node is a cluster and each of its children nodes is a sub-cluster. Information contained in a node includes not only the number of images, but also the center and the variance of these images. Upon the addition of new images, the tree structure is capable of dynamically changing to ensure the minimization of total variance of the tree. Subsequently, a heuristic method has been designed to retrieve the information from this tree. Given a sample image, the probability of a tree node that contains the similar images is computed using the center of the node and its variance. If the probability is higher than a certain threshold, this node will be recursively checked to locate the similar images. So will its children nodes if their probability is also higher than that threshold. If no sufficient similar images were founded, a reduced threshold value would be adopted to initiate a new seeking from the root node. The search terminates when it found sufficient similar images or the threshold value is too low to give meaningful sense. Experiments have shown that the proposed dynamic cluster tree is able to improve the searching efficiency notably.

Evaluating the content and quality of information about premature ejaculation on the Internet: what are men being exposed to ?

PubMed

Gul, M; Kaynar, M

2017-03-01

Premature ejaculation is one of the most common male sexual dysfunctions; however, only a few patients with premature ejaculation are seeking professional help or advice. Internet has become an important source of knowledge, and thus, more patients are looking online for health information. According to our best knowledge, no study has evaluated the content and quality of websites on premature ejaculation. We, therefore, aimed to evaluate the content and quality of currently available Internet-based information on premature ejaculation. A sample was obtained comprising the 50 top sites retrieved from Google, Bing and Yahoo search engines using the terms 'premature ejaculation'. Each site then was reviewed based on some predefined evaluation criteria to determine the general quality, condition-specific content quality, popularity index and ownership. The websites reviewed were differed highly in terms of quality and ownership. Only a few sites provided comprehensive medical and complete information on premature ejaculation. The online information available is often of uncertain calibre; therefore, men are being exposed to information about premature ejaculation with a highly variable degree quality. This fact should be considered both by health professionals and website owners, and better online resources should be provided for these patients. © 2016 Blackwell Verlag GmbH.

A Usability Survey of a Contents-Based Video Retrieval System by Combining Digital Video and an Electronic Bulletin Board

ERIC Educational Resources Information Center

Haga, Hirohide; Kaneda, Shigeo

2005-01-01

This article describes the survey of the usability of a novel content-based video retrieval system. This system combines video streaming and an electronic bulletin board system (BBS). Comments submitted to the BBS are used to index video data. Following the development of the prototype system an experimental survey with ten subjects was performed.…

Connectionist Interaction Information Retrieval.

ERIC Educational Resources Information Center

Dominich, Sandor

2003-01-01

Discussion of connectionist views for adaptive clustering in information retrieval focuses on a connectionist clustering technique and activation spreading-based information retrieval model using the interaction information retrieval method. Presents theoretical as well as simulation results as regards computational complexity and includes…

Surface reflectance retrieval from imaging spectrometer data using three atmospheric codes

NASA Astrophysics Data System (ADS)

Staenz, Karl; Williams, Daniel J.; Fedosejevs, Gunar; Teillet, Phil M.

1994-12-01

Surface reflectance retrieval from imaging spectrometer data has become important for quantitative information extraction in many application areas. In order to calculate surface reflectance from remotely measured radiance, radiative transfer codes play an important role for removal of the scattering and gaseous absorption effects of the atmosphere. The present study evaluates surface reflectances retrieved from airborne visible/infrared imaging spectrometer (AVIRIS) data using three radiative transfer codes: modified 5S (M5S), 6S, and MODTRAN2. Comparisons of the retrieved surface reflectance with ground-based reflectance were made for different target types such as asphalt, gravel, grass/soil mixture (soccer field), and water (Sooke Lake). The results indicate that the estimation of the atmospheric water vapor content is important for an accurate surface reflectance retrieval regardless of the radiative transfer code used. For the present atmospheric conditions, a difference of 0.1 in aerosol optical depth had little impact on the retrieved surface reflectance. The performance of MODTRAN2 is superior in the gas absorption regions compared to M5S and 6S.

1984-1995 Evolution of Stratospheric Aerosol Size, Surface Area, and Volume Derived by Combining SAGE II and CLAES Extinction Measurements

NASA Technical Reports Server (NTRS)

Russell, Philip B.; Bauman, Jill J.

2000-01-01

This SAGE II Science Team task focuses on the development of a multi-wavelength, multi- sensor Look-Up-Table (LUT) algorithm for retrieving information about stratospheric aerosols from global satellite-based observations of particulate extinction. The LUT algorithm combines the 4-wavelength SAGE II extinction measurements (0.385 <= lambda <= 1.02 microns) with the 7.96 micron and 12.82 micron extinction measurements from the Cryogenic Limb Array Etalon Spectrometer (CLAES) instrument, thus increasing the information content available from either sensor alone. The algorithm uses the SAGE II/CLAES composite spectra in month-latitude-altitude bins to retrieve values and uncertainties of particle effective radius R(sub eff), surface area S, volume V and size distribution width sigma(sub g).

The remains of the day in dissociative amnesia.

PubMed

Staniloiu, Angelica; Markowitsch, Hans J

2012-04-10

Memory is not a unity, but is divided along a content axis and a time axis, respectively. Along the content dimension, five long-term memory systems are described, according to their hierarchical ontogenetic and phylogenetic organization. These memory systems are assumed to be accompanied by different levels of consciousness. While encoding is based on a hierarchical arrangement of memory systems from procedural to episodic-autobiographical memory, retrieval allows independence in the sense that no matter how information is encoded, it can be retrieved in any memory system. Thus, we illustrate the relations between various long-term memory systems by reviewing the spectrum of abnormalities in mnemonic processing that may arise in the dissociative amnesia-a condition that is usually characterized by a retrieval blockade of episodic-autobiographical memories and occurs in the context of psychological trauma, without evidence of brain damage on conventional structural imaging. Furthermore, we comment on the functions of implicit memories in guiding and even adaptively molding the behavior of patients with dissociative amnesia and preserving, in the absence of autonoetic consciousness, the so-called "internal coherence of life".

The Remains of the Day in Dissociative Amnesia

PubMed Central

Staniloiu, Angelica; Markowitsch, Hans J.

2012-01-01

Memory is not a unity, but is divided along a content axis and a time axis, respectively. Along the content dimension, five long-term memory systems are described, according to their hierarchical ontogenetic and phylogenetic organization. These memory systems are assumed to be accompanied by different levels of consciousness. While encoding is based on a hierarchical arrangement of memory systems from procedural to episodic-autobiographical memory, retrieval allows independence in the sense that no matter how information is encoded, it can be retrieved in any memory system. Thus, we illustrate the relations between various long-term memory systems by reviewing the spectrum of abnormalities in mnemonic processing that may arise in the dissociative amnesia—a condition that is usually characterized by a retrieval blockade of episodic-autobiographical memories and occurs in the context of psychological trauma, without evidence of brain damage on conventional structural imaging. Furthermore, we comment on the functions of implicit memories in guiding and even adaptively molding the behavior of patients with dissociative amnesia and preserving, in the absence of autonoetic consciousness, the so-called “internal coherence of life”. PMID:24962768

Knowledge Structures of Entering Computer Networking Students and Their Instructors

ERIC Educational Resources Information Center

DiCerbo, Kristen E.

2007-01-01

Students bring prior knowledge to their learning experiences. This prior knowledge is known to affect how students encode and later retrieve new information learned. Teachers and content developers can use information about students' prior knowledge to create more effective lessons and materials. In many content areas, particularly the sciences,…

New model for distributed multimedia databases and its application to networking of museums

NASA Astrophysics Data System (ADS)

Kuroda, Kazuhide; Komatsu, Naohisa; Komiya, Kazumi; Ikeda, Hiroaki

1998-02-01

This paper proposes a new distributed multimedia data base system where the databases storing MPEG-2 videos and/or super high definition images are connected together through the B-ISDN's, and also refers to an example of the networking of museums on the basis of the proposed database system. The proposed database system introduces a new concept of the 'retrieval manager' which functions an intelligent controller so that the user can recognize a set of image databases as one logical database. A user terminal issues a request to retrieve contents to the retrieval manager which is located in the nearest place to the user terminal on the network. Then, the retrieved contents are directly sent through the B-ISDN's to the user terminal from the server which stores the designated contents. In this case, the designated logical data base dynamically generates the best combination of such a retrieving parameter as a data transfer path referring to directly or data on the basis of the environment of the system. The generated retrieving parameter is then executed to select the most suitable data transfer path on the network. Therefore, the best combination of these parameters fits to the distributed multimedia database system.

Case-based lung image categorization and retrieval for interstitial lung diseases: clinical workflows.

PubMed

Depeursinge, Adrien; Vargas, Alejandro; Gaillard, Frédéric; Platon, Alexandra; Geissbuhler, Antoine; Poletti, Pierre-Alexandre; Müller, Henning

2012-01-01

Clinical workflows and user interfaces of image-based computer-aided diagnosis (CAD) for interstitial lung diseases in high-resolution computed tomography are introduced and discussed. Three use cases are implemented to assist students, radiologists, and physicians in the diagnosis workup of interstitial lung diseases. In a first step, the proposed system shows a three-dimensional map of categorized lung tissue patterns with quantification of the diseases based on texture analysis of the lung parenchyma. Then, based on the proportions of abnormal and normal lung tissue as well as clinical data of the patients, retrieval of similar cases is enabled using a multimodal distance aggregating content-based image retrieval (CBIR) and text-based information search. The global system leads to a hybrid detection-CBIR-based CAD, where detection-based and CBIR-based CAD show to be complementary both on the user's side and on the algorithmic side. The proposed approach is in accordance with the classical workflow of clinicians searching for similar cases in textbooks and personal collections. The developed system enables objective and customizable inter-case similarity assessment, and the performance measures obtained with a leave-one-patient-out cross-validation (LOPO CV) are representative of a clinical usage of the system.

Knowledge-Based Information Retrieval.

ERIC Educational Resources Information Center

Ford, Nigel

1991-01-01

Discussion of information retrieval focuses on theoretical and empirical advances in knowledge-based information retrieval. Topics discussed include the use of natural language for queries; the use of expert systems; intelligent tutoring systems; user modeling; the need for evaluation of system effectiveness; and examples of systems, including…

Retrieval of profile information from airborne multiaxis UV-visible skylight absorption measurements.

PubMed

Bruns, Marco; Buehler, Stefan A; Burrows, John P; Heue, Klaus-Peter; Platt, Ulrich; Pundt, Irene; Richter, Andreas; Rozanov, Alexej; Wagner, Thomas; Wang, Ping

2004-08-01

A recent development in ground-based remote sensing of atmospheric constituents by UV-visible absorption measurements of scattered light is the simultaneous use of several horizon viewing directions in addition to the traditional zenith-sky pointing. The different light paths through the atmosphere enable the vertical distribution of some atmospheric absorbers, such as NO2, BrO, or O3, to be retrieved. This approach has recently been implemented on an airborne platform. This novel instrument, the airborne multiaxis differential optical absorption spectrometer (AMAXDOAS), has been flown for the first time. In this study, the amount of profile information that can be retrieved from such measurements is investigated for the trace gas NO2. Sensitivity studies on synthetic data are performed for a variety of representative measurement conditions including two wavelengths, one in the UV and one in the visible, two different surface spectral reflectances, various lines of sight (LOSs), and for two different flight altitudes. The results demonstrate that the AMAXDOAS measurements contain useful profile information, mainly at flight altitude and below the aircraft. Depending on wavelength and LOS used, the vertical resolution of the retrieved profiles is as good as 2 km near flight altitude. Above 14 km the profile information content of AMAXDOAS measurements is sparse. Airborne multiaxis measurements are thus a promising tool for atmospheric studies in the troposphere and the upper troposphere and lower stratosphere region.

Teaching Three-Dimensional Structural Chemistry Using Crystal Structure Databases. 4. Examples of Discovery-Based Learning Using the Complete Cambridge Structural Database

ERIC Educational Resources Information Center

Battle, Gary M.; Allen, Frank H.; Ferrence, Gregory M.

2011-01-01

Parts 1 and 2 of this series described the educational value of experimental three-dimensional (3D) chemical structures determined by X-ray crystallography and retrieved from the crystallographic databases. In part 1, we described the information content of the Cambridge Structural Database (CSD) and discussed a representative teaching subset of…

pyAudioAnalysis: An Open-Source Python Library for Audio Signal Analysis.

PubMed

Giannakopoulos, Theodoros

2015-01-01

Audio information plays a rather important role in the increasing digital content that is available today, resulting in a need for methodologies that automatically analyze such content: audio event recognition for home automations and surveillance systems, speech recognition, music information retrieval, multimodal analysis (e.g. audio-visual analysis of online videos for content-based recommendation), etc. This paper presents pyAudioAnalysis, an open-source Python library that provides a wide range of audio analysis procedures including: feature extraction, classification of audio signals, supervised and unsupervised segmentation and content visualization. pyAudioAnalysis is licensed under the Apache License and is available at GitHub (https://github.com/tyiannak/pyAudioAnalysis/). Here we present the theoretical background behind the wide range of the implemented methodologies, along with evaluation metrics for some of the methods. pyAudioAnalysis has been already used in several audio analysis research applications: smart-home functionalities through audio event detection, speech emotion recognition, depression classification based on audio-visual features, music segmentation, multimodal content-based movie recommendation and health applications (e.g. monitoring eating habits). The feedback provided from all these particular audio applications has led to practical enhancement of the library.

pyAudioAnalysis: An Open-Source Python Library for Audio Signal Analysis

PubMed Central

Giannakopoulos, Theodoros

2015-01-01

Audio information plays a rather important role in the increasing digital content that is available today, resulting in a need for methodologies that automatically analyze such content: audio event recognition for home automations and surveillance systems, speech recognition, music information retrieval, multimodal analysis (e.g. audio-visual analysis of online videos for content-based recommendation), etc. This paper presents pyAudioAnalysis, an open-source Python library that provides a wide range of audio analysis procedures including: feature extraction, classification of audio signals, supervised and unsupervised segmentation and content visualization. pyAudioAnalysis is licensed under the Apache License and is available at GitHub (https://github.com/tyiannak/pyAudioAnalysis/). Here we present the theoretical background behind the wide range of the implemented methodologies, along with evaluation metrics for some of the methods. pyAudioAnalysis has been already used in several audio analysis research applications: smart-home functionalities through audio event detection, speech emotion recognition, depression classification based on audio-visual features, music segmentation, multimodal content-based movie recommendation and health applications (e.g. monitoring eating habits). The feedback provided from all these particular audio applications has led to practical enhancement of the library. PMID:26656189

Collaborative Information Retrieval Method among Personal Repositories

NASA Astrophysics Data System (ADS)

Kamei, Koji; Yukawa, Takashi; Yoshida, Sen; Kuwabara, Kazuhiro

In this paper, we describe a collaborative information retrieval method among personal repositorie and an implementation of the method on a personal agent framework. We propose a framework for personal agents that aims to enable the sharing and exchange of information resources that are distributed unevenly among individuals. The kernel of a personal agent framework is an RDF(resource description framework)-based information repository for storing, retrieving and manipulating privately collected information, such as documents the user read and/or wrote, email he/she exchanged, web pages he/she browsed, etc. The repository also collects annotations to information resources that describe relationships among information resources and records of interaction between the user and information resources. Since the information resources in a personal repository and their structure are personalized, information retrieval from other users' is an important application of the personal agent. A vector space model with a personalized concept-base is employed as an information retrieval mechanism in a personal repository. Since a personalized concept-base is constructed from information resources in a personal repository, it reflects its user's knowledge and interests. On the other hand, it leads to another problem while querying other users' personal repositories; that is, simply transferring query requests does not provide desirable results. To solve this problem, we propose a query equalization scheme based on a relevance feedback method for collaborative information retrieval between personalized concept-bases. In this paper, we describe an implementation of the collaborative information retrieval method and its user interface on the personal agent framework.

Web information retrieval based on ontology

NASA Astrophysics Data System (ADS)

Zhang, Jian

2013-03-01

The purpose of the Information Retrieval (IR) is to find a set of documents that are relevant for a specific information need of a user. Traditional Information Retrieval model commonly used in commercial search engine is based on keyword indexing system and Boolean logic queries. One big drawback of traditional information retrieval is that they typically retrieve information without an explicitly defined domain of interest to the users so that a lot of no relevance information returns to users, which burden the user to pick up useful answer from these no relevance results. In order to tackle this issue, many semantic web information retrieval models have been proposed recently. The main advantage of Semantic Web is to enhance search mechanisms with the use of Ontology's mechanisms. In this paper, we present our approach to personalize web search engine based on ontology. In addition, key techniques are also discussed in our paper. Compared to previous research, our works concentrate on the semantic similarity and the whole process including query submission and information annotation.

Hybrid Histogram Descriptor: A Fusion Feature Representation for Image Retrieval.

PubMed

Feng, Qinghe; Hao, Qiaohong; Chen, Yuqi; Yi, Yugen; Wei, Ying; Dai, Jiangyan

2018-06-15

Currently, visual sensors are becoming increasingly affordable and fashionable, acceleratingly the increasing number of image data. Image retrieval has attracted increasing interest due to space exploration, industrial, and biomedical applications. Nevertheless, designing effective feature representation is acknowledged as a hard yet fundamental issue. This paper presents a fusion feature representation called a hybrid histogram descriptor (HHD) for image retrieval. The proposed descriptor comprises two histograms jointly: a perceptually uniform histogram which is extracted by exploiting the color and edge orientation information in perceptually uniform regions; and a motif co-occurrence histogram which is acquired by calculating the probability of a pair of motif patterns. To evaluate the performance, we benchmarked the proposed descriptor on RSSCN7, AID, Outex-00013, Outex-00014 and ETHZ-53 datasets. Experimental results suggest that the proposed descriptor is more effective and robust than ten recent fusion-based descriptors under the content-based image retrieval framework. The computational complexity was also analyzed to give an in-depth evaluation. Furthermore, compared with the state-of-the-art convolutional neural network (CNN)-based descriptors, the proposed descriptor also achieves comparable performance, but does not require any training process.

Designing an information search interface for younger and older adults.

PubMed

Pak, Richard; Price, Margaux M

2008-08-01

The present study examined Web-based information retrieval as a function of age for two information organization schemes: hierarchical organization and one organized around tags or keywords. Older adults' performance in information retrieval tasks has traditionally been lower compared with younger adults'. The current study examined the degree to which information organization moderated age-related performance differences on an information retrieval task. The theory of fluid and crystallized intelligence may provide insight into different kinds of information architectures that may reduce age-related differences in computer-based information retrieval performance. Fifty younger (18-23 years of age) and 50 older (55-76 years of age) participants browsed a Web site for answers to specific questions. Half of the participants browsed the hierarchically organized system (taxonomy), which maintained a one-to-one relationship between menu link and page, whereas the other half browsed the tag-based interface, with a many-to-one relationship between menu and page. This difference was expected to interact with age-related differences in fluid and crystallized intelligence. Age-related differences in information retrieval performance persisted; however, a tag-based retrieval interface reduced age-related differences, as compared with a taxonomical interface. Cognitive aging theory can lead to interface interventions that reduce age-related differences in performance with technology. In an information retrieval paradigm, older adults may be able to leverage their increased crystallized intelligence to offset fluid intelligence declines in a computer-based information search task. More research is necessary, but the results suggest that information retrieval interfaces organized around keywords may reduce age-related differences in performance.

Material classification and automatic content enrichment of images using supervised learning and knowledge bases

NASA Astrophysics Data System (ADS)

Mallepudi, Sri Abhishikth; Calix, Ricardo A.; Knapp, Gerald M.

2011-02-01

In recent years there has been a rapid increase in the size of video and image databases. Effective searching and retrieving of images from these databases is a significant current research area. In particular, there is a growing interest in query capabilities based on semantic image features such as objects, locations, and materials, known as content-based image retrieval. This study investigated mechanisms for identifying materials present in an image. These capabilities provide additional information impacting conditional probabilities about images (e.g. objects made of steel are more likely to be buildings). These capabilities are useful in Building Information Modeling (BIM) and in automatic enrichment of images. I2T methodologies are a way to enrich an image by generating text descriptions based on image analysis. In this work, a learning model is trained to detect certain materials in images. To train the model, an image dataset was constructed containing single material images of bricks, cloth, grass, sand, stones, and wood. For generalization purposes, an additional set of 50 images containing multiple materials (some not used in training) was constructed. Two different supervised learning classification models were investigated: a single multi-class SVM classifier, and multiple binary SVM classifiers (one per material). Image features included Gabor filter parameters for texture, and color histogram data for RGB components. All classification accuracy scores using the SVM-based method were above 85%. The second model helped in gathering more information from the images since it assigned multiple classes to the images. A framework for the I2T methodology is presented.

Task Context and Organization in Free Recall

ERIC Educational Resources Information Center

Polyn, Sean M.; Norman, Kenneth A.; Kahana, Michael J.

2009-01-01

Prior work on organization in free recall has focused on the ways in which semantic and temporal information determine the order in which material is retrieved from memory. Tulving's theory of ecphory suggests that these organizational effects arise from the interaction of a retrieval cue with the contents of memory. Using the…

Dow Jones News/Retrieval--An IndepthBxook.

ERIC Educational Resources Information Center

Dempsey, Tim

1984-01-01

This introduction to the nonbibliographic databases offered by the Dow Jones News/Retrieval Service describes file content and search strategies in four groups: Dow Jones Business and Economic News; Dow Jones Quotes (market prices for stocks and other securities); Financial and Investment Services; General News and Information Services. Examples…

XCO2 retrieval error over deserts near critical surface albedo

NASA Astrophysics Data System (ADS)

Zhang, Qiong; Shia, Run-Lie; Sander, Stanley P.; Yung, Yuk L.

2016-02-01

Large retrieval errors in column-weighted CO2 mixing ratio (XCO2) over deserts are evident in the Orbiting Carbon Observatory 2 version 7 L2 products. We argue that these errors are caused by the surface albedo being close to a critical surface albedo (αc). Over a surface with albedo close to αc, increasing the aerosol optical depth (AOD) does not change the continuum radiance. The spectral signature caused by changing the AOD is identical to that caused by changing the absorbing gas column. The degeneracy in the retrievals of AOD and XCO2 results in a loss of degrees of freedom and information content. We employ a two-stream-exact single scattering radiative transfer model to study the physical mechanism of XCO2 retrieval error over a surface with albedo close to αc. Based on retrieval tests over surfaces with different albedos, we conclude that over a surface with albedo close to αc, the XCO2 retrieval suffers from a significant loss of accuracy. We recommend a bias correction approach that has significantly improved the XCO2 retrieval from the California Laboratory for Atmospheric Remote Sensing data in the presence of aerosol loading.

CliniWeb: managing clinical information on the World Wide Web.

PubMed

Hersh, W R; Brown, K E; Donohoe, L C; Campbell, E M; Horacek, A E

1996-01-01

The World Wide Web is a powerful new way to deliver on-line clinical information, but several problems limit its value to health care professionals: content is highly distributed and difficult to find, clinical information is not separated from non-clinical information, and the current Web technology is unable to support some advanced retrieval capabilities. A system called CliniWeb has been developed to address these problems. CliniWeb is an index to clinical information on the World Wide Web, providing a browsing and searching interface to clinical content at the level of the health care student or provider. Its database contains a list of clinical information resources on the Web that are indexed by terms from the Medical Subject Headings disease tree and retrieved with the assistance of SAPHIRE. Limitations of the processes used to build the database are discussed, together with directions for future research.

Content-based image retrieval for interstitial lung diseases using classification confidence

NASA Astrophysics Data System (ADS)

Dash, Jatindra Kumar; Mukhopadhyay, Sudipta; Prabhakar, Nidhi; Garg, Mandeep; Khandelwal, Niranjan

2013-02-01

Content Based Image Retrieval (CBIR) system could exploit the wealth of High-Resolution Computed Tomography (HRCT) data stored in the archive by finding similar images to assist radiologists for self learning and differential diagnosis of Interstitial Lung Diseases (ILDs). HRCT findings of ILDs are classified into several categories (e.g. consolidation, emphysema, ground glass, nodular etc.) based on their texture like appearances. Therefore, analysis of ILDs is considered as a texture analysis problem. Many approaches have been proposed for CBIR of lung images using texture as primitive visual content. This paper presents a new approach to CBIR for ILDs. The proposed approach makes use of a trained neural network (NN) to find the output class label of query image. The degree of confidence of the NN classifier is analyzed using Naive Bayes classifier that dynamically takes a decision on the size of the search space to be used for retrieval. The proposed approach is compared with three simple distance based and one classifier based texture retrieval approaches. Experimental results show that the proposed technique achieved highest average percentage precision of 92.60% with lowest standard deviation of 20.82%.

Information Interaction: Providing a Framework for Information Architecture.

ERIC Educational Resources Information Center

Toms, Elaine G.

2002-01-01

Discussion of information architecture focuses on a model of information interaction that bridges the gap between human and computer and between information behavior and information retrieval. Illustrates how the process of information interaction is affected by the user, the system, and the content. (Contains 93 references.) (LRW)

MetaSEEk: a content-based metasearch engine for images

NASA Astrophysics Data System (ADS)

Beigi, Mandis; Benitez, Ana B.; Chang, Shih-Fu

1997-12-01

Search engines are the most powerful resources for finding information on the rapidly expanding World Wide Web (WWW). Finding the desired search engines and learning how to use them, however, can be very time consuming. The integration of such search tools enables the users to access information across the world in a transparent and efficient manner. These systems are called meta-search engines. The recent emergence of visual information retrieval (VIR) search engines on the web is leading to the same efficiency problem. This paper describes and evaluates MetaSEEk, a content-based meta-search engine used for finding images on the Web based on their visual information. MetaSEEk is designed to intelligently select and interface with multiple on-line image search engines by ranking their performance for different classes of user queries. User feedback is also integrated in the ranking refinement. We compare MetaSEEk with a base line version of meta-search engine, which does not use the past performance of the different search engines in recommending target search engines for future queries.

A model for enhancing Internet medical document retrieval with "medical core metadata".

PubMed

Malet, G; Munoz, F; Appleyard, R; Hersh, W

1999-01-01

Finding documents on the World Wide Web relevant to a specific medical information need can be difficult. The goal of this work is to define a set of document content description tags, or metadata encodings, that can be used to promote disciplined search access to Internet medical documents. The authors based their approach on a proposed metadata standard, the Dublin Core Metadata Element Set, which has recently been submitted to the Internet Engineering Task Force. Their model also incorporates the National Library of Medicine's Medical Subject Headings (MeSH) vocabulary and MEDLINE-type content descriptions. The model defines a medical core metadata set that can be used to describe the metadata for a wide variety of Internet documents. The authors propose that their medical core metadata set be used to assign metadata to medical documents to facilitate document retrieval by Internet search engines.

A Model for Enhancing Internet Medical Document Retrieval with “Medical Core Metadata”

PubMed Central

Malet, Gary; Munoz, Felix; Appleyard, Richard; Hersh, William

1999-01-01

Objective: Finding documents on the World Wide Web relevant to a specific medical information need can be difficult. The goal of this work is to define a set of document content description tags, or metadata encodings, that can be used to promote disciplined search access to Internet medical documents. Design: The authors based their approach on a proposed metadata standard, the Dublin Core Metadata Element Set, which has recently been submitted to the Internet Engineering Task Force. Their model also incorporates the National Library of Medicine's Medical Subject Headings (MeSH) vocabulary and Medline-type content descriptions. Results: The model defines a medical core metadata set that can be used to describe the metadata for a wide variety of Internet documents. Conclusions: The authors propose that their medical core metadata set be used to assign metadata to medical documents to facilitate document retrieval by Internet search engines. PMID:10094069

Studies and Application of Remote Sensing Retrieval Method of Soil Moisture Content in Land Parcel Units in Irrigation Area

NASA Astrophysics Data System (ADS)

Zhu, H.; Zhao, H. L.; Jiang, Y. Z.; Zang, W. B.

2018-05-01

Soil moisture is one of the important hydrological elements. Obtaining soil moisture accurately and effectively is of great significance for water resource management in irrigation area. During the process of soil moisture content retrieval with multiremote sensing data, multi- remote sensing data always brings multi-spatial scale problems which results in inconformity of soil moisture content retrieved by remote sensing in different spatial scale. In addition, agricultural water use management has suitable spatial scale of soil moisture information so as to satisfy the demands of dynamic management of water use and water demand in certain unit. We have proposed to use land parcel unit as the minimum unit to do soil moisture content research in agricultural water using area, according to soil characteristics, vegetation coverage characteristics in underlying layer, and hydrological characteristic into the basis of study unit division. We have proposed division method of land parcel units. Based on multi thermal infrared and near infrared remote sensing data, we calculate the ndvi and tvdi index and make a statistical model between the tvdi index and soil moisture of ground monitoring station. Then we move forward to study soil moisture remote sensing retrieval method on land parcel unit scale. And the method has been applied in Hetao irrigation area. Results show that compared with pixel scale the soil moisture content in land parcel unit scale has displayed stronger correlation with true value. Hence, remote sensing retrieval method of soil moisture content in land parcel unit scale has shown good applicability in Hetao irrigation area. We converted the research unit into the scale of land parcel unit. Using the land parcel units with unified crops and soil attributes as the research units more complies with the characteristics of agricultural water areas, avoids the problems such as decomposition of mixed pixels and excessive dependence on high-resolution data caused by the research units of pixels, and doesn't involve compromises in the spatial scale and simulating precision like the grid simulation. When the application needs are met, the production efficiency of products can also be improved at a certain degree.

Toward privacy-preserving JPEG image retrieval

NASA Astrophysics Data System (ADS)

Cheng, Hang; Wang, Jingyue; Wang, Meiqing; Zhong, Shangping

2017-07-01

This paper proposes a privacy-preserving retrieval scheme for JPEG images based on local variance. Three parties are involved in the scheme: the content owner, the server, and the authorized user. The content owner encrypts JPEG images for privacy protection by jointly using permutation cipher and stream cipher, and then, the encrypted versions are uploaded to the server. With an encrypted query image provided by an authorized user, the server may extract blockwise local variances in different directions without knowing the plaintext content. After that, it can calculate the similarity between the encrypted query image and each encrypted database image by a local variance-based feature comparison mechanism. The authorized user with the encryption key can decrypt the returned encrypted images with plaintext content similar to the query image. The experimental results show that the proposed scheme not only provides effective privacy-preserving retrieval service but also ensures both format compliance and file size preservation for encrypted JPEG images.

Graph-Based Interactive Bibliographic Information Retrieval Systems

ERIC Educational Resources Information Center

Zhu, Yongjun

2017-01-01

In the big data era, we have witnessed the explosion of scholarly literature. This explosion has imposed challenges to the retrieval of bibliographic information. Retrieval of intended bibliographic information has become challenging due to the overwhelming search results returned by bibliographic information retrieval systems for given input…

Effect of Multiangular Observations on Crop Chlorophyll Content Retrieval Using Field Top-Of Spectrometer Data

NASA Astrophysics Data System (ADS)

Jiao, Q.; Liu, L.; Zhang, B.

2017-12-01

Leaf chlorophyll content is an important indicator of crop growth condition that determines final crop yield. A lot of research on remote sensing of leaf chlorophyll content were based on reflectance data acquired from nadir direction. However, reflectance data at nadir may be affected by soil background. In fact, many satellite sensors with capability of chlorophyll retrieval, like the 68.5 degrees field-of-view MERIS, have produced large multiangular data. This study tries to assess the anisotropic effect on the retrieval of leaf chlorophyll content using field hyperspectral data of wheat canopy. The field multi-angle observation experiment of winter wheat was carried out in April 2017 in Xiaotangshan agriculture demonstration study site in Beijing. Field canopy spectra and leaf chlorophyll content of winter wheat were measured. The most used indices for chlorophyll content retrieval, such as CIred-edge, REP, MTCI, MCARI/OSAVI[705,750], TCARI/OSAVI[705,750], were calculated based on the filed multiangular reflectance. The ratio index TCARI/OSAVI owned the best results in estimating leaf chlorophyll content (R2 of 0.62) among all the selected indices, when using the top-of-canopy reflectance at nadir. The determination coefficient of the relationship of TCARI/OSAVI with chlorophyll content reached its peak (R2 of 0.70) at angle of 15 degrees, and the minimum R2 value of only 0.25 at angle of 60 degrees. The MTCI got the peak of determination coefficient (R2 of 0.63) at angle of 15 degrees and the minimum value (R2 of 0.57) for 60 degrees. Our results showed the MTCI could keep a more satisfactory correlation with leaf chlorophyll content of winter wheat, however the mean values of the MTCI basically decreased as the observation angle increases. This work shows the strong anisotropic effects of top-of-canopy reflectance which influences most of selected popular chlorophyll indices. If spectral index selection is proper, multiangular remote sensing could produce higher accuracy for leaf chlorophyll content retrieval than only using nadir observation. Multi-angular remote sensing has the potential of leaf chlorophyll content retrieval for diagnosis of crop nitrogen stress or water stress.

42 CFR 485.60 - Condition of participation: Clinical records.

Code of Federal Regulations, 2013 CFR

2013-10-01

... retrieval and compilation of information. (a) Standard: Content. Each clinical record must contain sufficient information to identify the patient clearly and to justify the diagnosis and treatment. Entries in...: Protection of clinical record information. The facility must safeguard clinical record information against...

42 CFR 485.60 - Condition of participation: Clinical records.

Code of Federal Regulations, 2010 CFR

2010-10-01

... retrieval and compilation of information. (a) Standard: Content. Each clinical record must contain sufficient information to identify the patient clearly and to justify the diagnosis and treatment. Entries in...: Protection of clinical record information. The facility must safeguard clinical record information against...

42 CFR 485.60 - Condition of participation: Clinical records.

Code of Federal Regulations, 2014 CFR

2014-10-01

... retrieval and compilation of information. (a) Standard: Content. Each clinical record must contain sufficient information to identify the patient clearly and to justify the diagnosis and treatment. Entries in...: Protection of clinical record information. The facility must safeguard clinical record information against...

42 CFR 485.60 - Condition of participation: Clinical records.

Code of Federal Regulations, 2011 CFR

2011-10-01

... retrieval and compilation of information. (a) Standard: Content. Each clinical record must contain sufficient information to identify the patient clearly and to justify the diagnosis and treatment. Entries in...: Protection of clinical record information. The facility must safeguard clinical record information against...

Toward semantic-based retrieval of visual information: a model-based approach

NASA Astrophysics Data System (ADS)

Park, Youngchoon; Golshani, Forouzan; Panchanathan, Sethuraman

2002-07-01

This paper center around the problem of automated visual content classification. To enable classification based image or visual object retrieval, we propose a new image representation scheme called visual context descriptor (VCD) that is a multidimensional vector in which each element represents the frequency of a unique visual property of an image or a region. VCD utilizes the predetermined quality dimensions (i.e., types of features and quantization level) and semantic model templates mined in priori. Not only observed visual cues, but also contextually relevant visual features are proportionally incorporated in VCD. Contextual relevance of a visual cue to a semantic class is determined by using correlation analysis of ground truth samples. Such co-occurrence analysis of visual cues requires transformation of a real-valued visual feature vector (e.g., color histogram, Gabor texture, etc.,) into a discrete event (e.g., terms in text). Good-feature to track, rule of thirds, iterative k-means clustering and TSVQ are involved in transformation of feature vectors into unified symbolic representations called visual terms. Similarity-based visual cue frequency estimation is also proposed and used for ensuring the correctness of model learning and matching since sparseness of sample data causes the unstable results of frequency estimation of visual cues. The proposed method naturally allows integration of heterogeneous visual or temporal or spatial cues in a single classification or matching framework, and can be easily integrated into a semantic knowledge base such as thesaurus, and ontology. Robust semantic visual model template creation and object based image retrieval are demonstrated based on the proposed content description scheme.

Application of new type of distributed multimedia databases to networked electronic museum

NASA Astrophysics Data System (ADS)

Kuroda, Kazuhide; Komatsu, Naohisa; Komiya, Kazumi; Ikeda, Hiroaki

1999-01-01

Recently, various kinds of multimedia application systems have actively been developed based on the achievement of advanced high sped communication networks, computer processing technologies, and digital contents-handling technologies. Under this background, this paper proposed a new distributed multimedia database system which can effectively perform a new function of cooperative retrieval among distributed databases. The proposed system introduces a new concept of 'Retrieval manager' which functions as an intelligent controller so that the user can recognize a set of distributed databases as one logical database. The logical database dynamically generates and performs a preferred combination of retrieving parameters on the basis of both directory data and the system environment. Moreover, a concept of 'domain' is defined in the system as a managing unit of retrieval. The retrieval can effectively be performed by cooperation of processing among multiple domains. Communication language and protocols are also defined in the system. These are used in every action for communications in the system. A language interpreter in each machine translates a communication language into an internal language used in each machine. Using the language interpreter, internal processing, such internal modules as DBMS and user interface modules can freely be selected. A concept of 'content-set' is also introduced. A content-set is defined as a package of contents. Contents in the content-set are related to each other. The system handles a content-set as one object. The user terminal can effectively control the displaying of retrieved contents, referring to data indicating the relation of the contents in the content- set. In order to verify the function of the proposed system, a networked electronic museum was experimentally built. The results of this experiment indicate that the proposed system can effectively retrieve the objective contents under the control to a number of distributed domains. The result also indicate that the system can effectively work even if the system becomes large.

Automation and hypermedia technology applications

NASA Technical Reports Server (NTRS)

Jupin, Joseph H.; Ng, Edward W.; James, Mark L.

1993-01-01

This paper represents a progress report on HyLite (Hypermedia Library technology): a research and development activity to produce a versatile system as part of NASA's technology thrusts in automation, information sciences, and communications. HyLite can be used as a system or tool to facilitate the creation and maintenance of large distributed electronic libraries. The contents of such a library may be software components, hardware parts or designs, scientific data sets or databases, configuration management information, etc. Proliferation of computer use has made the diversity and quantity of information too large for any single user to sort, process, and utilize effectively. In response to this information deluge, we have created HyLite to enable the user to process relevant information into a more efficient organization for presentation, retrieval, and readability. To accomplish this end, we have incorporated various AI techniques into the HyLite hypermedia engine to facilitate parameters and properties of the system. The proposed techniques include intelligent searching tools for the libraries, intelligent retrievals, and navigational assistance based on user histories. HyLite itself is based on an earlier project, the Encyclopedia of Software Components (ESC) which used hypermedia to facilitate and encourage software reuse.

Exploiting salient semantic analysis for information retrieval

NASA Astrophysics Data System (ADS)

Luo, Jing; Meng, Bo; Quan, Changqin; Tu, Xinhui

2016-11-01

Recently, many Wikipedia-based methods have been proposed to improve the performance of different natural language processing (NLP) tasks, such as semantic relatedness computation, text classification and information retrieval. Among these methods, salient semantic analysis (SSA) has been proven to be an effective way to generate conceptual representation for words or documents. However, its feasibility and effectiveness in information retrieval is mostly unknown. In this paper, we study how to efficiently use SSA to improve the information retrieval performance, and propose a SSA-based retrieval method under the language model framework. First, SSA model is adopted to build conceptual representations for documents and queries. Then, these conceptual representations and the bag-of-words (BOW) representations can be used in combination to estimate the language models of queries and documents. The proposed method is evaluated on several standard text retrieval conference (TREC) collections. Experiment results on standard TREC collections show the proposed models consistently outperform the existing Wikipedia-based retrieval methods.

Intelligent distributed medical image management

NASA Astrophysics Data System (ADS)

Garcia, Hong-Mei C.; Yun, David Y.

1995-05-01

The rapid advancements in high performance global communication have accelerated cooperative image-based medical services to a new frontier. Traditional image-based medical services such as radiology and diagnostic consultation can now fully utilize multimedia technologies in order to provide novel services, including remote cooperative medical triage, distributed virtual simulation of operations, as well as cross-country collaborative medical research and training. Fast (efficient) and easy (flexible) retrieval of relevant images remains a critical requirement for the provision of remote medical services. This paper describes the database system requirements, identifies technological building blocks for meeting the requirements, and presents a system architecture for our target image database system, MISSION-DBS, which has been designed to fulfill the goals of Project MISSION (medical imaging support via satellite integrated optical network) -- an experimental high performance gigabit satellite communication network with access to remote supercomputing power, medical image databases, and 3D visualization capabilities in addition to medical expertise anywhere and anytime around the country. The MISSION-DBS design employs a synergistic fusion of techniques in distributed databases (DDB) and artificial intelligence (AI) for storing, migrating, accessing, and exploring images. The efficient storage and retrieval of voluminous image information is achieved by integrating DDB modeling and AI techniques for image processing while the flexible retrieval mechanisms are accomplished by combining attribute- based and content-based retrievals.

The EHR-ARCHE project: satisfying clinical information needs in a Shared Electronic Health Record system based on IHE XDS and Archetypes.

PubMed

Duftschmid, Georg; Rinner, Christoph; Kohler, Michael; Huebner-Bloder, Gudrun; Saboor, Samrend; Ammenwerth, Elske

2013-12-01

While contributing to an improved continuity of care, Shared Electronic Health Record (EHR) systems may also lead to information overload of healthcare providers. Document-oriented architectures, such as the commonly employed IHE XDS profile, which only support information retrieval at the level of documents, are particularly susceptible for this problem. The objective of the EHR-ARCHE project was to develop a methodology and a prototype to efficiently satisfy healthcare providers' information needs when accessing a patient's Shared EHR during a treatment situation. We especially aimed to investigate whether this objective can be reached by integrating EHR Archetypes into an IHE XDS environment. Using methodical triangulation, we first analysed the information needs of healthcare providers, focusing on the treatment of diabetes patients as an exemplary application domain. We then designed ISO/EN 13606 Archetypes covering the identified information needs. To support a content-based search for fine-grained information items within EHR documents, we extended the IHE XDS environment with two additional actors. Finally, we conducted a formative and summative evaluation of our approach within a controlled study. We identified 446 frequently needed diabetes-specific information items, representing typical information needs of healthcare providers. We then created 128 Archetypes and 120 EHR documents for two fictive patients. All seven diabetes experts, who evaluated our approach, preferred the content-based search to a conventional XDS search. Success rates of finding relevant information was higher for the content-based search (100% versus 80%) and the latter was also more time-efficient (8-14min versus 20min or more). Our results show that for an efficient satisfaction of health care providers' information needs, a content-based search that rests upon the integration of Archetypes into an IHE XDS-based Shared EHR system is superior to a conventional metadata-based XDS search. Copyright © 2013 The Authors. Published by Elsevier Ireland Ltd.. All rights reserved.

The EHR-ARCHE project: Satisfying clinical information needs in a Shared Electronic Health Record System based on IHE XDS and Archetypes☆

PubMed Central

Duftschmid, Georg; Rinner, Christoph; Kohler, Michael; Huebner-Bloder, Gudrun; Saboor, Samrend; Ammenwerth, Elske

2013-01-01

Purpose While contributing to an improved continuity of care, Shared Electronic Health Record (EHR) systems may also lead to information overload of healthcare providers. Document-oriented architectures, such as the commonly employed IHE XDS profile, which only support information retrieval at the level of documents, are particularly susceptible for this problem. The objective of the EHR-ARCHE project was to develop a methodology and a prototype to efficiently satisfy healthcare providers’ information needs when accessing a patient's Shared EHR during a treatment situation. We especially aimed to investigate whether this objective can be reached by integrating EHR Archetypes into an IHE XDS environment. Methods Using methodical triangulation, we first analysed the information needs of healthcare providers, focusing on the treatment of diabetes patients as an exemplary application domain. We then designed ISO/EN 13606 Archetypes covering the identified information needs. To support a content-based search for fine-grained information items within EHR documents, we extended the IHE XDS environment with two additional actors. Finally, we conducted a formative and summative evaluation of our approach within a controlled study. Results We identified 446 frequently needed diabetes-specific information items, representing typical information needs of healthcare providers. We then created 128 Archetypes and 120 EHR documents for two fictive patients. All seven diabetes experts, who evaluated our approach, preferred the content-based search to a conventional XDS search. Success rates of finding relevant information was higher for the content-based search (100% versus 80%) and the latter was also more time-efficient (8–14 min versus 20 min or more). Conclusions Our results show that for an efficient satisfaction of health care providers’ information needs, a content-based search that rests upon the integration of Archetypes into an IHE XDS-based Shared EHR system is superior to a conventional metadata-based XDS search. PMID:23999002

Future-saving audiovisual content for Data Science: Preservation of geoinformatics video heritage with the TIB|AV-Portal

NASA Astrophysics Data System (ADS)

Löwe, Peter; Plank, Margret; Ziedorn, Frauke

2015-04-01

In data driven research, the access to citation and preservation of the full triad consisting of journal article, research data and -software has started to become good scientific practice. To foster the adoption of this practice the significance of software tools has to be acknowledged, which enable scientists to harness auxiliary audiovisual content in their research work. The advent of ubiquitous computer-based audiovisual recording and corresponding Web 2.0 hosting platforms like Youtube, Slideshare and GitHub has created new ecosystems for contextual information related to scientific software and data, which continues to grow both in size and variety of content. The current Web 2.0 platforms lack capabilities for long term archiving and scientific citation, such as persistent identifiers allowing to reference specific intervals of the overall content. The audiovisual content currently shared by scientists ranges from commented howto-demonstrations on software handling, installation and data-processing, to aggregated visual analytics of the evolution of software projects over time. Such content are crucial additions to the scientific message, as they ensure that software-based data-processing workflows can be assessed, understood and reused in the future. In the context of data driven research, such content needs to be accessible by effective search capabilities, enabling the content to be retrieved and ensuring that the content producers receive credit for their efforts within the scientific community. Improved multimedia archiving and retrieval services for scientific audiovisual content which meet these requirements are currently implemented by the scientific library community. This paper exemplifies the existing challenges, requirements, benefits and the potential of the preservation, accessibility and citability of such audiovisual content for the Open Source communities based on the new audiovisual web service TIB|AV Portal of the German National Library of Science and Technology. The web-based portal allows for extended search capabilities based on enhanced metadata derived by automated video analysis. By combining state-of-the-art multimedia retrieval techniques such as speech-, text-, and image recognition with semantic analysis, content-based access to videos at the segment level is provided. Further, by using the open standard Media Fragment Identifier (MFID), a citable Digital Object Identifier is displayed for each video segment. In addition to the continuously growing footprint of contemporary content, the importance of vintage audiovisual information needs to be considered: This paper showcases the successful application of the TIB|AV-Portal in the preservation and provision of a newly discovered version of a GRASS GIS promotional video produced by US Army -Corps of Enginers Laboratory (US-CERL) in 1987. The video is provides insight into the constraints of the very early days of the GRASS GIS project, which is the oldest active Free and Open Source Software (FOSS) GIS project which has been active for over thirty years. GRASS itself has turned into a collaborative scientific platform and a repository of scientific peer-reviewed code and algorithm/knowledge hub for future generation of scientists [1]. This is a reference case for future preservation activities regarding semantic-enhanced Web 2.0 content from geospatial software projects within Academia and beyond. References: [1] Chemin, Y., Petras V., Petrasova, A., Landa, M., Gebbert, S., Zambelli, P., Neteler, M., Löwe, P.: GRASS GIS: a peer-reviewed scientific platform and future research Repository, Geophysical Research Abstracts, Vol. 17, EGU2015-8314-1, 2015 (submitted)

A Multimodal Search Engine for Medical Imaging Studies.

PubMed

Pinho, Eduardo; Godinho, Tiago; Valente, Frederico; Costa, Carlos

2017-02-01

The use of digital medical imaging systems in healthcare institutions has increased significantly, and the large amounts of data in these systems have led to the conception of powerful support tools: recent studies on content-based image retrieval (CBIR) and multimodal information retrieval in the field hold great potential in decision support, as well as for addressing multiple challenges in healthcare systems, such as computer-aided diagnosis (CAD). However, the subject is still under heavy research, and very few solutions have become part of Picture Archiving and Communication Systems (PACS) in hospitals and clinics. This paper proposes an extensible platform for multimodal medical image retrieval, integrated in an open-source PACS software with profile-based CBIR capabilities. In this article, we detail a technical approach to the problem by describing its main architecture and each sub-component, as well as the available web interfaces and the multimodal query techniques applied. Finally, we assess our implementation of the engine with computational performance benchmarks.

Evaluation of Radar Vegetation Indices for Vegetation Water Content Estimation Using Data from a Ground-Based SMAP Simulator

NASA Technical Reports Server (NTRS)

Srivastava, Prashant K.; O'Neill, Peggy; Cosh, Michael; Lang, Roger; Joseph, Alicia

2015-01-01

Vegetation water content (VWC) is an important component of microwave soil moisture retrieval algorithms. This paper aims to estimate VWC using L band active and passive radar/radiometer datasets obtained from a NASA ground-based Soil Moisture Active Passive (SMAP) simulator known as ComRAD (Combined Radar/Radiometer). Several approaches to derive vegetation information from radar and radiometer data such as HH, HV, VV, Microwave Polarization Difference Index (MPDI), HH/VV ratio, HV/(HH+VV), HV/(HH+HV+VV) and Radar Vegetation Index (RVI) are tested for VWC estimation through a generalized linear model (GLM). The overall analysis indicates that HV radar backscattering could be used for VWC content estimation with highest performance followed by HH, VV, MPDI, RVI, and other ratios.

Using complex networks towards information retrieval and diagnostics in multidimensional imaging

NASA Astrophysics Data System (ADS)

Banerjee, Soumya Jyoti; Azharuddin, Mohammad; Sen, Debanjan; Savale, Smruti; Datta, Himadri; Dasgupta, Anjan Kr; Roy, Soumen

2015-12-01

We present a fresh and broad yet simple approach towards information retrieval in general and diagnostics in particular by applying the theory of complex networks on multidimensional, dynamic images. We demonstrate a successful use of our method with the time series generated from high content thermal imaging videos of patients suffering from the aqueous deficient dry eye (ADDE) disease. Remarkably, network analyses of thermal imaging time series of contact lens users and patients upon whom Laser-Assisted in situ Keratomileusis (Lasik) surgery has been conducted, exhibit pronounced similarity with results obtained from ADDE patients. We also propose a general framework for the transformation of multidimensional images to networks for futuristic biometry. Our approach is general and scalable to other fluctuation-based devices where network parameters derived from fluctuations, act as effective discriminators and diagnostic markers.

Using complex networks towards information retrieval and diagnostics in multidimensional imaging.

PubMed

Banerjee, Soumya Jyoti; Azharuddin, Mohammad; Sen, Debanjan; Savale, Smruti; Datta, Himadri; Dasgupta, Anjan Kr; Roy, Soumen

2015-12-02

We present a fresh and broad yet simple approach towards information retrieval in general and diagnostics in particular by applying the theory of complex networks on multidimensional, dynamic images. We demonstrate a successful use of our method with the time series generated from high content thermal imaging videos of patients suffering from the aqueous deficient dry eye (ADDE) disease. Remarkably, network analyses of thermal imaging time series of contact lens users and patients upon whom Laser-Assisted in situ Keratomileusis (Lasik) surgery has been conducted, exhibit pronounced similarity with results obtained from ADDE patients. We also propose a general framework for the transformation of multidimensional images to networks for futuristic biometry. Our approach is general and scalable to other fluctuation-based devices where network parameters derived from fluctuations, act as effective discriminators and diagnostic markers.

Using complex networks towards information retrieval and diagnostics in multidimensional imaging

PubMed Central

Banerjee, Soumya Jyoti; Azharuddin, Mohammad; Sen, Debanjan; Savale, Smruti; Datta, Himadri; Dasgupta, Anjan Kr; Roy, Soumen

2015-01-01

We present a fresh and broad yet simple approach towards information retrieval in general and diagnostics in particular by applying the theory of complex networks on multidimensional, dynamic images. We demonstrate a successful use of our method with the time series generated from high content thermal imaging videos of patients suffering from the aqueous deficient dry eye (ADDE) disease. Remarkably, network analyses of thermal imaging time series of contact lens users and patients upon whom Laser-Assisted in situ Keratomileusis (Lasik) surgery has been conducted, exhibit pronounced similarity with results obtained from ADDE patients. We also propose a general framework for the transformation of multidimensional images to networks for futuristic biometry. Our approach is general and scalable to other fluctuation-based devices where network parameters derived from fluctuations, act as effective discriminators and diagnostic markers. PMID:26626047

A new method of content based medical image retrieval and its applications to CT imaging sign retrieval.

PubMed

Ma, Ling; Liu, Xiabi; Gao, Yan; Zhao, Yanfeng; Zhao, Xinming; Zhou, Chunwu

2017-02-01

This paper proposes a new method of content based medical image retrieval through considering fused, context-sensitive similarity. Firstly, we fuse the semantic and visual similarities between the query image and each image in the database as their pairwise similarities. Then, we construct a weighted graph whose nodes represent the images and edges measure their pairwise similarities. By using the shortest path algorithm over the weighted graph, we obtain a new similarity measure, context-sensitive similarity measure, between the query image and each database image to complete the retrieval process. Actually, we use the fused pairwise similarity to narrow down the semantic gap for obtaining a more accurate pairwise similarity measure, and spread it on the intrinsic data manifold to achieve the context-sensitive similarity for a better retrieval performance. The proposed method has been evaluated on the retrieval of the Common CT Imaging Signs of Lung Diseases (CISLs) and achieved not only better retrieval results but also the satisfactory computation efficiency. Copyright © 2017 Elsevier Inc. All rights reserved.

The Effect of Bilingual Term List Size on Dictionary-Based Cross-Language Information Retrieval

DTIC Science & Technology

2006-01-01

The Effect of Bilingual Term List Size on Dictionary -Based Cross-Language Information Retrieval Dina Demner-Fushman Department of Computer Science... dictionary -based Cross-Language Information Retrieval (CLIR), in which the goal is to find documents written in one natural language based on queries that...in which the documents are written. In dictionary -based CLIR techniques, the princi- pal source of translation knowledge is a translation lexicon

Content Based Image Retrieval by Using Color Descriptor and Discrete Wavelet Transform.

PubMed

Ashraf, Rehan; Ahmed, Mudassar; Jabbar, Sohail; Khalid, Shehzad; Ahmad, Awais; Din, Sadia; Jeon, Gwangil

2018-01-25

Due to recent development in technology, the complexity of multimedia is significantly increased and the retrieval of similar multimedia content is a open research problem. Content-Based Image Retrieval (CBIR) is a process that provides a framework for image search and low-level visual features are commonly used to retrieve the images from the image database. The basic requirement in any image retrieval process is to sort the images with a close similarity in term of visually appearance. The color, shape and texture are the examples of low-level image features. The feature plays a significant role in image processing. The powerful representation of an image is known as feature vector and feature extraction techniques are applied to get features that will be useful in classifying and recognition of images. As features define the behavior of an image, they show its place in terms of storage taken, efficiency in classification and obviously in time consumption also. In this paper, we are going to discuss various types of features, feature extraction techniques and explaining in what scenario, which features extraction technique will be better. The effectiveness of the CBIR approach is fundamentally based on feature extraction. In image processing errands like object recognition and image retrieval feature descriptor is an immense among the most essential step. The main idea of CBIR is that it can search related images to an image passed as query from a dataset got by using distance metrics. The proposed method is explained for image retrieval constructed on YCbCr color with canny edge histogram and discrete wavelet transform. The combination of edge of histogram and discrete wavelet transform increase the performance of image retrieval framework for content based search. The execution of different wavelets is additionally contrasted with discover the suitability of specific wavelet work for image retrieval. The proposed algorithm is prepared and tried to implement for Wang image database. For Image Retrieval Purpose, Artificial Neural Networks (ANN) is used and applied on standard dataset in CBIR domain. The execution of the recommended descriptors is assessed by computing both Precision and Recall values and compared with different other proposed methods with demonstrate the predominance of our method. The efficiency and effectiveness of the proposed approach outperforms the existing research in term of average precision and recall values.

Active learning methods for interactive image retrieval.

PubMed

Gosselin, Philippe Henri; Cord, Matthieu

2008-07-01

Active learning methods have been considered with increased interest in the statistical learning community. Initially developed within a classification framework, a lot of extensions are now being proposed to handle multimedia applications. This paper provides algorithms within a statistical framework to extend active learning for online content-based image retrieval (CBIR). The classification framework is presented with experiments to compare several powerful classification techniques in this information retrieval context. Focusing on interactive methods, active learning strategy is then described. The limitations of this approach for CBIR are emphasized before presenting our new active selection process RETIN. First, as any active method is sensitive to the boundary estimation between classes, the RETIN strategy carries out a boundary correction to make the retrieval process more robust. Second, the criterion of generalization error to optimize the active learning selection is modified to better represent the CBIR objective of database ranking. Third, a batch processing of images is proposed. Our strategy leads to a fast and efficient active learning scheme to retrieve sets of online images (query concept). Experiments on large databases show that the RETIN method performs well in comparison to several other active strategies.

Retrieving relevant time-course experiments: a study on Arabidopsis microarrays.

PubMed

Şener, Duygu Dede; Oğul, Hasan

2016-06-01

Understanding time-course regulation of genes in response to a stimulus is a major concern in current systems biology. The problem is usually approached by computational methods to model the gene behaviour or its networked interactions with the others by a set of latent parameters. The model parameters can be estimated through a meta-analysis of available data obtained from other relevant experiments. The key question here is how to find the relevant experiments which are potentially useful in analysing current data. In this study, the authors address this problem in the context of time-course gene expression experiments from an information retrieval perspective. To this end, they introduce a computational framework that takes a time-course experiment as a query and reports a list of relevant experiments retrieved from a given repository. These retrieved experiments can then be used to associate the environmental factors of query experiment with the findings previously reported. The model is tested using a set of time-course Arabidopsis microarrays. The experimental results show that relevant experiments can be successfully retrieved based on content similarity.

Towards an improved soil moisture retrieval for organic-rich soils from SMOS passive microwave L-band observations

NASA Astrophysics Data System (ADS)

Bircher, Simone; Richaume, Philippe; Mahmoodi, Ali; Mialon, Arnaud; Fernandez-Moran, Roberto; Wigneron, Jean-Pierre; Demontoux, François; Jonard, François; Weihermüller, Lutz; Andreasen, Mie; Rautiainen, Kimmo; Ikonen, Jaakko; Schwank, Mike; Drusch, Mattias; Kerr, Yann H.

2017-04-01

From the passive L-band microwave radiometer onboard the Soil Moisture and Ocean Salinity (SMOS) space mission global surface soil moisture data is retrieved every 2 - 3 days. Thus far, the empirical L-band Microwave Emission of the Biosphere (L-MEB) radiative transfer model applied in the SMOS soil moisture retrieval algorithm is exclusively calibrated over test sites in dry and temperate climate zones. Furthermore, the included dielectric mixing model relating soil moisture to relative permittivity accounts only for mineral soils. However, soil moisture monitoring over the higher Northern latitudes is crucial since these regions are especially sensitive to climate change. A considerable positive feedback is expected if thawing of these extremely organic soils supports carbon decomposition and release to the atmosphere. Due to differing structural characteristics and thus varying bound water fractions, the relative permittivity of organic material is lower than that of the most mineral soils at a given water content. This assumption was verified by means of L-band relative permittivity laboratory measurements of organic and mineral substrates from various sites in Denmark, Finland, Scotland and Siberia using a resonant cavity. Based on these data, a simple empirical dielectric model for organic soils was derived and implemented in the SMOS Soil Moisture Level 2 Prototype Processor (SML2PP). Unfortunately, the current SMOS retrieved soil moisture product seems to show unrealistically low values compared to in situ soil moisture data collected from organic surface layers in North America, Europe and the Tibetan Plateau so that the impact of the dielectric model for organic soils cannot really be tested. A simplified SMOS processing scheme yielding higher soil moisture levels has recently been proposed and is presently under investigation. Furthermore, recalibration of the model parameters accounting for vegetation and roughness effects that were thus far only evaluated using the default dielectric model for mineral soils is ongoing for the "organic" L-MEB version. Additionally, in order to decide where a soil moisture retrieval using the "organic" dielectric model should be triggered, information on soil organic matter content in the soil surface layer has to be considered in the retrieval algorithm. For this purpose, SoilGrids (www.soilgrids.org) providing soil organic carbon content (SOCC) in g/kg is under study. A SOCC threshold based on the relation between the SoilGrids' SOCC and the presence of organic soil surface layers (relevant to alter the microwave L-band emissions from the land surface) in the SoilGrids' source soil profile information has to be established. In this communication, we present the current status of the above outlined studies with the objective to advance towards an improved soil moisture retrieval for organic-rich soils from SMOS passive microwave L-band observations.

Conjunctive patches subspace learning with side information for collaborative image retrieval.

PubMed

Zhang, Lining; Wang, Lipo; Lin, Weisi

2012-08-01

Content-Based Image Retrieval (CBIR) has attracted substantial attention during the past few years for its potential practical applications to image management. A variety of Relevance Feedback (RF) schemes have been designed to bridge the semantic gap between the low-level visual features and the high-level semantic concepts for an image retrieval task. Various Collaborative Image Retrieval (CIR) schemes aim to utilize the user historical feedback log data with similar and dissimilar pairwise constraints to improve the performance of a CBIR system. However, existing subspace learning approaches with explicit label information cannot be applied for a CIR task, although the subspace learning techniques play a key role in various computer vision tasks, e.g., face recognition and image classification. In this paper, we propose a novel subspace learning framework, i.e., Conjunctive Patches Subspace Learning (CPSL) with side information, for learning an effective semantic subspace by exploiting the user historical feedback log data for a CIR task. The CPSL can effectively integrate the discriminative information of labeled log images, the geometrical information of labeled log images and the weakly similar information of unlabeled images together to learn a reliable subspace. We formally formulate this problem into a constrained optimization problem and then present a new subspace learning technique to exploit the user historical feedback log data. Extensive experiments on both synthetic data sets and a real-world image database demonstrate the effectiveness of the proposed scheme in improving the performance of a CBIR system by exploiting the user historical feedback log data.

Development of a web-based video management and application processing system

NASA Astrophysics Data System (ADS)

Chan, Shermann S.; Wu, Yi; Li, Qing; Zhuang, Yueting

2001-07-01

How to facilitate efficient video manipulation and access in a web-based environment is becoming a popular trend for video applications. In this paper, we present a web-oriented video management and application processing system, based on our previous work on multimedia database and content-based retrieval. In particular, we extend the VideoMAP architecture with specific web-oriented mechanisms, which include: (1) Concurrency control facilities for the editing of video data among different types of users, such as Video Administrator, Video Producer, Video Editor, and Video Query Client; different users are assigned various priority levels for different operations on the database. (2) Versatile video retrieval mechanism which employs a hybrid approach by integrating a query-based (database) mechanism with content- based retrieval (CBR) functions; its specific language (CAROL/ST with CBR) supports spatio-temporal semantics of video objects, and also offers an improved mechanism to describe visual content of videos by content-based analysis method. (3) Query profiling database which records the `histories' of various clients' query activities; such profiles can be used to provide the default query template when a similar query is encountered by the same kind of users. An experimental prototype system is being developed based on the existing VideoMAP prototype system, using Java and VC++ on the PC platform.

Content-based image exploitation for situational awareness

NASA Astrophysics Data System (ADS)

Gains, David

2008-04-01

Image exploitation is of increasing importance to the enterprise of building situational awareness from multi-source data. It involves image acquisition, identification of objects of interest in imagery, storage, search and retrieval of imagery, and the distribution of imagery over possibly bandwidth limited networks. This paper describes an image exploitation application that uses image content alone to detect objects of interest, and that automatically establishes and preserves spatial and temporal relationships between images, cameras and objects. The application features an intuitive user interface that exposes all images and information generated by the system to an operator thus facilitating the formation of situational awareness.

Using Dedal to share and reuse distributed engineering design information

NASA Technical Reports Server (NTRS)

Baya, Vinod; Baudin, Catherine; Mabogunje, Ade; Das, Aseem; Cannon, David M.; Leifer, Larry J.

1994-01-01

The overall goal of the project is to facilitate the reuse of previous design experience for the maintenance, repair and redesign of artifacts in the electromechanical engineering domain. An engineering team creates information in the form of meeting summaries, project memos, progress reports, engineering notes, spreadsheet calculations and CAD drawings. Design information captured in these media is difficult to reuse because the way design concepts are referred to evolve over the life of a project and because decisions, requirements and structure are interrelated but rarely explicitly linked. Based on protocol analysis of the information seeking behavior of designer's, we defined a language to describe the content and the form of design records and implemented this language in Dedal, a tool for indexing, modeling and retrieving design information. We first describe the approach to indexing and retrieval in Dedal. Next we describe ongoing work in extending Dedal's capabilities to a distributed environment by integrating it with World Wide Web. This will enable members of a design team who are not co-located to share and reuse information.

What versus where: Investigating how autobiographical memory retrieval differs when accessed with thematic versus spatial information.

PubMed

Sheldon, Signy; Chu, Sonja

2017-09-01

Autobiographical memory research has investigated how cueing distinct aspects of a past event can trigger different recollective experiences. This research has stimulated theories about how autobiographical knowledge is accessed and organized. Here, we test the idea that thematic information organizes multiple autobiographical events whereas spatial information organizes individual past episodes by investigating how retrieval guided by these two forms of information differs. We used a novel autobiographical fluency task in which participants accessed multiple memory exemplars to event theme and spatial (location) cues followed by a narrative description task in which they described the memories generated to these cues. Participants recalled significantly more memory exemplars to event theme than to spatial cues; however, spatial cues prompted faster access to past memories. Results from the narrative description task revealed that memories retrieved via event theme cues compared to spatial cues had a higher number of overall details, but those recalled to the spatial cues were recollected with a greater concentration on episodic details than those retrieved via event theme cues. These results provide evidence that thematic information organizes and integrates multiple memories whereas spatial information prompts the retrieval of specific episodic content from a past event.

Content based image retrieval using local binary pattern operator and data mining techniques.

PubMed

Vatamanu, Oana Astrid; Frandeş, Mirela; Lungeanu, Diana; Mihalaş, Gheorghe-Ioan

2015-01-01

Content based image retrieval (CBIR) concerns the retrieval of similar images from image databases, using feature vectors extracted from images. These feature vectors globally define the visual content present in an image, defined by e.g., texture, colour, shape, and spatial relations between vectors. Herein, we propose the definition of feature vectors using the Local Binary Pattern (LBP) operator. A study was performed in order to determine the optimum LBP variant for the general definition of image feature vectors. The chosen LBP variant is then subsequently used to build an ultrasound image database, and a database with images obtained from Wireless Capsule Endoscopy. The image indexing process is optimized using data clustering techniques for images belonging to the same class. Finally, the proposed indexing method is compared to the classical indexing technique, which is nowadays widely used.

Hypothesis-confirming information search strategies and computerized information-retrieval systems

DOE Office of Scientific and Technical Information (OSTI.GOV)

Jacobs, S.M.

A recent trend in information-retrieval systems technology is the development of on-line information retrieval systems. One objective of these systems has been to attempt to enhance decision effectiveness by allowing users to preferentially seek information, thereby facilitating the reduction or elimination of information overload. These systems do not necessarily lead to more-effective decision making, however. Recent research in information-search strategy suggests that when users are seeking information subsequent to forming initial beliefs, they may preferentially seek information to confirm these beliefs. It seems that effective computer-based decision support requires an information retrieval system capable of: (a) retrieving a subset ofmore » all available information, in order to reduce information overload, and (b) supporting an information search strategy that considers all relevant information, rather than merely hypothesis-confirming information. An information retrieval system with an expert component (i.e., a knowledge-based DSS) should be able to provide these capabilities. Results of this study are non conclusive; there was neither strong confirmatory evidence nor strong disconfirmatory evidence regarding the effectiveness of the KBDSS.« less

Data Discretization for Novel Relationship Discovery in Information Retrieval.

ERIC Educational Resources Information Center

Benoit, G.

2002-01-01

Describes an information retrieval, visualization, and manipulation model which offers the user multiple ways to exploit the retrieval set, based on weighted query terms, via an interactive interface. Outlines the mathematical model and describes an information retrieval application built on the model to search structured and full-text files.…

Content Classification: Leveraging New Tools and Librarians' Expertise.

ERIC Educational Resources Information Center

Starr, Jennie

1999-01-01

Presents factors for librarians to consider when decision-making about information retrieval. Discusses indexing theory; thesauri aids; controlled vocabulary or thesauri to increase access; humans versus machines; automated tools; product evaluations and evaluation criteria; automated classification tools; content server products; and document…

Multimedia content analysis, management and retrieval: trends and challenges

NASA Astrophysics Data System (ADS)

Hanjalic, Alan; Sebe, Nicu; Chang, Edward

2006-01-01

Recent advances in computing, communications and storage technology have made multimedia data become prevalent. Multimedia has gained enormous potential in improving the processes in a wide range of fields, such as advertising and marketing, education and training, entertainment, medicine, surveillance, wearable computing, biometrics, and remote sensing. Rich content of multimedia data, built through the synergies of the information contained in different modalities, calls for new and innovative methods for modeling, processing, mining, organizing, and indexing of this data for effective and efficient searching, retrieval, delivery, management and sharing of multimedia content, as required by the applications in the abovementioned fields. The objective of this paper is to present our views on the trends that should be followed when developing such methods, to elaborate on the related research challenges, and to introduce the new conference, Multimedia Content Analysis, Management and Retrieval, as a premium venue for presenting and discussing these methods with the scientific community. Starting from 2006, the conference will be held annually as a part of the IS&T/SPIE Electronic Imaging event.

Developing an A Priori Database for Passive Microwave Snow Water Retrievals Over Ocean

NASA Astrophysics Data System (ADS)

Yin, Mengtao; Liu, Guosheng

2017-12-01

A physically optimized a priori database is developed for Global Precipitation Measurement Microwave Imager (GMI) snow water retrievals over ocean. The initial snow water content profiles are derived from CloudSat Cloud Profiling Radar (CPR) measurements. A radiative transfer model in which the single-scattering properties of nonspherical snowflakes are based on the discrete dipole approximate results is employed to simulate brightness temperatures and their gradients. Snow water content profiles are then optimized through a one-dimensional variational (1D-Var) method. The standard deviations of the difference between observed and simulated brightness temperatures are in a similar magnitude to the observation errors defined for observation error covariance matrix after the 1D-Var optimization, indicating that this variational method is successful. This optimized database is applied in a Bayesian retrieval snow water algorithm. The retrieval results indicated that the 1D-Var approach has a positive impact on the GMI retrieved snow water content profiles by improving the physical consistency between snow water content profiles and observed brightness temperatures. Global distribution of snow water contents retrieved from the a priori database is compared with CloudSat CPR estimates. Results showed that the two estimates have a similar pattern of global distribution, and the difference of their global means is small. In addition, we investigate the impact of using physical parameters to subset the database on snow water retrievals. It is shown that using total precipitable water to subset the database with 1D-Var optimization is beneficial for snow water retrievals.

Optimization of infobutton design and Implementation: A systematic review.

PubMed

Teixeira, Miguel; Cook, David A; Heale, Bret S E; Del Fiol, Guilherme

2017-10-01

Infobuttons are clinical decision tools embedded in the electronic health record that attempt to link clinical data with context sensitive knowledge resources. We systematically reviewed technical approaches that contribute to improved infobutton design, implementation and functionality. We searched databases including MEDLINE, EMBASE, and the Cochrane Library database from inception to March 1, 2016 for studies describing the use of infobuttons. We selected full review comparative studies, usability studies, and qualitative studies examining infobutton design and implementation. We abstracted usability measures such as user satisfaction, impact, and efficiency, as well as prediction accuracy of infobutton content retrieval algorithms and infobutton adoption/interoperability. We found 82 original research studies on infobuttons. Twelve studies met criteria for detailed abstraction. These studies investigated infobutton interoperability (1 study); tools to help tailor infobutton functionality (1 study); interventions to improve user experience (7 studies); and interventions to improve content retrieval by improving prediction of relevant knowledge resources and information needs (3 studies). In-depth interviews with implementers showed the Health Level Seven (HL7) Infobutton standard to be simple and easy to implement. A usability study demonstrated the feasibility of a tool to help medical librarians tailor infobutton functionality. User experience studies showed that access to resources with which users are familiar increased user satisfaction ratings; and that links to specific subsections of drug monographs increased information seeking efficiency. However, none of the user experience improvements led to increased usage uptake. Recommender systems based on machine learning algorithms outperformed hand-crafted rules in the prediction of relevant resources and clinicians' information needs in a laboratory setting, but no studies were found using these techniques in clinical settings. Improved content indexing in one study led to improved content retrieval across three health care organizations. Best practice technical approaches to ensure optimal infobutton functionality, design and implementation remain understudied. The HL7 Infobutton standard has supported wide adoption of infobutton functionality among clinical information systems and knowledge resources. Limited evidence supports infobutton enhancements such as links to specific subtopics, configuration of optimal resources for specific tasks and users, and improved indexing and content coverage. Further research is needed to investigate user experience improvements to increase infobutton use and effectiveness. Copyright © 2017 Elsevier Inc. All rights reserved.

A new approach to the concept of "relevance" in information retrieval (IR).

PubMed

Kagolovsky, Y; Möhr, J R

2001-01-01

The concept of "relevance" is the fundamental concept of information science in general and information retrieval, in particular. Although "relevance" is extensively used in evaluation of information retrieval, there are considerable problems associated with reaching an agreement on its definition, meaning, evaluation, and application in information retrieval. There are a number of different views on "relevance" and its use for evaluation. Based on a review of the literature the main problems associated with the concept of "relevance" in information retrieval are identified. The authors argue that the proposal for the solution of the problems can be based on the conceptual IR framework built using a systems analytic approach to IR. Using this framework different kinds of "relevance" relationships in the IR process are identified, and a methodology for evaluation of "relevance" based on methods of semantics capturing and comparison is proposed.

[Memory and brain--neurobiological correlates of memory disturbances].

PubMed

Calabrese, P; Markowitsch, H J

2003-04-01

A differentiation of memory is possible on the basis of chronological and contents-related aspects. Furthermore, it is possible to make process-specific subdivisions (encoding, transfer, consolidation, retrieval). The time-related division on the one hand refers to the general differentiation into short-term and long-term memory, and, on the other, to that between anterograde and retrograde memory ("new" and "old memory"; measured from a given time point, usually that when brain damage occurred). Anterograde memory means the successful encoding and storing of new information; retrograde the ability to retrieve successfully acquired and/or stored information. On the contents-based level, memory can be divided into five basic long-term systems--episodic memory, the knowledge system, perceptual, procedural and the priming form of memory. Neural correlates for these divisions are discussed with special emphasis of the episodic and the knowledge systems, based both on normal individuals and brain-damaged subjects. It is argued that structures of the limbic system are important for encoding of information and for its transfer into long-term memory. For this, two independent, but interacting memory circuits are proposed--one of them controlling and integrating primarily the emotional, and the other primarily the cognitive components of newly incoming information. For information storage principally neocortical structures are regarded as important and for the recall of information from the episodic and semantic memory systems the combined action of portions of prefrontal and anterior temporal regions is regarded as essential. Within this fronto-temporal agglomerate, a moderate hemispheric-specificity is assumed to exist with the right-hemispheric combination being mainly engaged in episodic memory retrieval and the left-hemispheric in that of semantic information. Evidence for this specialization comes from the results from focally brain-damaged patients as well as from that functional brain imaging in normal human subjects. Comparing results from imaging studies in memory disturbed patients with brain damage and from patients with a psychiatric diagnosis (e. g., psychogenic amnesia) revealed that both patient groups demonstrate comparable metabolic changes on the brain level. It can therefore be concluded that in neurological patients distinct, identifiable tissue damage is existent, while in psychiatric patients changes in the brain's biochemistry (release of stress hormones, and transmitters) constitute the physiological bases for the memory disturbances.

Interactive radiographic image retrieval system.

PubMed

Kundu, Malay Kumar; Chowdhury, Manish; Das, Sudeb

2017-02-01

Content based medical image retrieval (CBMIR) systems enable fast diagnosis through quantitative assessment of the visual information and is an active research topic over the past few decades. Most of the state-of-the-art CBMIR systems suffer from various problems: computationally expensive due to the usage of high dimensional feature vectors and complex classifier/clustering schemes. Inability to properly handle the "semantic gap" and the high intra-class versus inter-class variability problem of the medical image database (like radiographic image database). This yields an exigent demand for developing highly effective and computationally efficient retrieval system. We propose a novel interactive two-stage CBMIR system for diverse collection of medical radiographic images. Initially, Pulse Coupled Neural Network based shape features are used to find out the most probable (similar) image classes using a novel "similarity positional score" mechanism. This is followed by retrieval using Non-subsampled Contourlet Transform based texture features considering only the images of the pre-identified classes. Maximal information compression index is used for unsupervised feature selection to achieve better results. To reduce the semantic gap problem, the proposed system uses a novel fuzzy index based relevance feedback mechanism by incorporating subjectivity of human perception in an analytic manner. Extensive experiments were carried out to evaluate the effectiveness of the proposed CBMIR system on a subset of Image Retrieval in Medical Applications (IRMA)-2009 database consisting of 10,902 labeled radiographic images of 57 different modalities. We obtained overall average precision of around 98% after only 2-3 iterations of relevance feedback mechanism. We assessed the results by comparisons with some of the state-of-the-art CBMIR systems for radiographic images. Unlike most of the existing CBMIR systems, in the proposed two-stage hierarchical framework, main importance is given on constructing efficient and compact feature vector representation, search-space reduction and handling the "semantic gap" problem effectively, without compromising the retrieval performance. Experimental results and comparisons show that the proposed system performs efficiently in the radiographic medical image retrieval field. Copyright © 2016 Elsevier Ireland Ltd. All rights reserved.

Natural language information retrieval in digital libraries

DOE Office of Scientific and Technical Information (OSTI.GOV)

Strzalkowski, T.; Perez-Carballo, J.; Marinescu, M.

In this paper we report on some recent developments in joint NYU and GE natural language information retrieval system. The main characteristic of this system is the use of advanced natural language processing to enhance the effectiveness of term-based document retrieval. The system is designed around a traditional statistical backbone consisting of the indexer module, which builds inverted index files from pre-processed documents, and a retrieval engine which searches and ranks the documents in response to user queries. Natural language processing is used to (1) preprocess the documents in order to extract content-carrying terms, (2) discover inter-term dependencies and buildmore » a conceptual hierarchy specific to the database domain, and (3) process user`s natural language requests into effective search queries. This system has been used in NIST-sponsored Text Retrieval Conferences (TREC), where we worked with approximately 3.3 GBytes of text articles including material from the Wall Street Journal, the Associated Press newswire, the Federal Register, Ziff Communications`s Computer Library, Department of Energy abstracts, U.S. Patents and the San Jose Mercury News, totaling more than 500 million words of English. The system have been designed to facilitate its scalability to deal with ever increasing amounts of data. In particular, a randomized index-splitting mechanism has been installed which allows the system to create a number of smaller indexes that can be independently and efficiently searched.« less

The NASA ADS Abstract Service and the Distributed Astronomy Digital Library [and] Project Soup: Comparing Evaluations of Digital Collection Efforts [and] Cross-Organizational Access Management: A Digital Library Authentication and Authorization Architecture [and] BibRelEx: Exploring Bibliographic Databases by Visualization of Annotated Content-based Relations [and] Semantics-Sensitive Retrieval for Digital Picture Libraries [and] Encoded Archival Description: An Introduction and Overview.

ERIC Educational Resources Information Center

Kurtz, Michael J.; Eichorn, Guenther; Accomazzi, Alberto; Grant, Carolyn S.; Demleitner, Markus; Murray, Stephen S.; Jones, Michael L. W.; Gay, Geri K.; Rieger, Robert H.; Millman, David; Bruggemann-Klein, Anne; Klein, Rolf; Landgraf, Britta; Wang, James Ze; Li, Jia; Chan, Desmond; Wiederhold, Gio; Pitti, Daniel V.

1999-01-01

Includes six articles that discuss a digital library for astronomy; comparing evaluations of digital collection efforts; cross-organizational access management of Web-based resources; searching scientific bibliographic databases based on content-based relations between documents; semantics-sensitive retrieval for digital picture libraries; and…

Single-footprint retrievals of temperature, water vapor and cloud properties from AIRS

NASA Astrophysics Data System (ADS)

Irion, Fredrick W.; Kahn, Brian H.; Schreier, Mathias M.; Fetzer, Eric J.; Fishbein, Evan; Fu, Dejian; Kalmus, Peter; Wilson, R. Chris; Wong, Sun; Yue, Qing

2018-02-01

Single-footprint Atmospheric Infrared Sounder spectra are used in an optimal estimation-based algorithm (AIRS-OE) for simultaneous retrieval of atmospheric temperature, water vapor, surface temperature, cloud-top temperature, effective cloud optical depth and effective cloud particle radius. In a departure from currently operational AIRS retrievals (AIRS V6), cloud scattering and absorption are in the radiative transfer forward model and AIRS single-footprint thermal infrared data are used directly rather than cloud-cleared spectra (which are calculated using nine adjacent AIRS infrared footprints). Coincident MODIS cloud data are used for cloud a priori data. Using single-footprint spectra improves the horizontal resolution of the AIRS retrieval from ˜ 45 to ˜ 13.5 km at nadir, but as microwave data are not used, the retrieval is not made at altitudes below thick clouds. An outline of the AIRS-OE retrieval procedure and information content analysis is presented. Initial comparisons of AIRS-OE to AIRS V6 results show increased horizontal detail in the water vapor and relative humidity fields in the free troposphere above the clouds. Initial comparisons of temperature, water vapor and relative humidity profiles with coincident radiosondes show good agreement. Future improvements to the retrieval algorithm, and to the forward model in particular, are discussed.

Minimizing the semantic gap in biomedical content-based image retrieval

NASA Astrophysics Data System (ADS)

Guan, Haiying; Antani, Sameer; Long, L. Rodney; Thoma, George R.

2010-03-01

A major challenge in biomedical Content-Based Image Retrieval (CBIR) is to achieve meaningful mappings that minimize the semantic gap between the high-level biomedical semantic concepts and the low-level visual features in images. This paper presents a comprehensive learning-based scheme toward meeting this challenge and improving retrieval quality. The article presents two algorithms: a learning-based feature selection and fusion algorithm and the Ranking Support Vector Machine (Ranking SVM) algorithm. The feature selection algorithm aims to select 'good' features and fuse them using different similarity measurements to provide a better representation of the high-level concepts with the low-level image features. Ranking SVM is applied to learn the retrieval rank function and associate the selected low-level features with query concepts, given the ground-truth ranking of the training samples. The proposed scheme addresses four major issues in CBIR to improve the retrieval accuracy: image feature extraction, selection and fusion, similarity measurements, the association of the low-level features with high-level concepts, and the generation of the rank function to support high-level semantic image retrieval. It models the relationship between semantic concepts and image features, and enables retrieval at the semantic level. We apply it to the problem of vertebra shape retrieval from a digitized spine x-ray image set collected by the second National Health and Nutrition Examination Survey (NHANES II). The experimental results show an improvement of up to 41.92% in the mean average precision (MAP) over conventional image similarity computation methods.

Analysis of Information Content in High-Spectral Resolution Sounders using Subset Selection Analysis

NASA Technical Reports Server (NTRS)

Velez-Reyes, Miguel; Joiner, Joanna

1998-01-01

In this paper, we summarize the results of the sensitivity analysis and data reduction carried out to determine the information content of AIRS and IASI channels. The analysis and data reduction was based on the use of subset selection techniques developed in the linear algebra and statistical community to study linear dependencies in high dimensional data sets. We applied the subset selection method to study dependency among channels by studying the dependency among their weighting functions. Also, we applied the technique to study the information provided by the different levels in which the atmosphere is discretized for retrievals and analysis. Results from the method correlate well with intuition in many respects and point out to possible modifications for band selection in sensor design and number and location of levels in the analysis process.

Placing User-Generated Content on the Map with Confidence

DTIC Science & Technology

2014-11-03

Terms Theory,Algorithms Keywords Geographic information retrieval, Geolocation 1. INTRODUCTION We describe a method that places on the map short text...we collected using twitter4j, a Java library for the Twitter API . After filtering, there were 44,289 documents in the Twitter test set We evaluate how...Baldwin. Text-based twitter user geolocation prediction. J. Artif. Intell. Res.(JAIR), 49:451–500, 2014. [4] C. Hauff, B. Thomee, and M. Trevisiol

Information content in Medline record fields.

PubMed

Kostoff, Ronald N; Block, Joel A; Stump, Jesse A; Pfeil, Kirstin M

2004-06-30

The authors have been conducting text mining analyses (extraction of useful information from text) of Medline records, using Abstracts as the main data source. For literature-based discovery, and other text mining applications as well, all records in a discipline need to be evaluated for determining prior art. Many Medline records do not contain Abstracts, but typically contain Titles and Mesh terms. Substitution of these fields for Abstracts in the non-Abstract records would restore the missing literature to some degree. Determine how well the information content of Title and Mesh fields approximates that of Abstracts in Medline records. Select historical Medline records related to Raynaud's Phenomenon that contain Abstracts. Determine the information content in the Abstract fields through text mining. Then, determine the information content in the Title fields, the Mesh fields, and the combined Title-Mesh fields, and compare with the information content in the Abstracts. Four metrics were used to compare the information content related to Raynaud's Phenomenon in the different fields: total number of phrases; number of unique phrases; content of factors from factor analyses; content of clusters from multi-link clustering. The Abstract field contains almost an order of magnitude more phrases than the other fields, and slightly more than an order of magnitude more unique phrases than the other fields. Each field used a factor matrix with 14 factors, and the combination of all 56 factors for the four fields represented 27 separate, but not unique, themes. These themes could be placed in two major categories, with two sub-categories per major category: Auto-immunity (antibodies, inflammation) and circulation (peripheral vessel circulation, coronary vessel circulation). All four sub-categories included representation from each field. Thus, while the focus of the representation of each field in each sub-category was moderately different, the four sub-category structure could be identified by analyzing the total factors in each field. In the cluster comparison phase of the study, the phrases used to create the clusters were the most important phrases identified for each factor. Thus, the factor matrix served as a filter for words used for clustering. While clusters were generated for all four fields, the Title hierarchy tended to be fragmented due to sparsity of the co-occurrence matrix that underlies the clusters. Therefore, the Title clusters were examined at only the lower levels of aggregation. The Abstract, Mesh, and Mesh + Title fields had the same first level taxonomy categories, auto-immunity and circulation. At the second level, the Abstract, Mesh, and Mesh + Title fields had the autoimmune diseases and antibodies sub-category in common. The Abstract and Mesh fields shared fascia inflammation as the other auto-immunity sub-category, while the other Mesh + Title sub-category focuses on vinyl chloride poisoning from industrial contact, and consequences of antineoplastic agents. However, in both cases, even though the words may be different, inflammation may be the common theme. For taxonomy generation, especially at the higher levels, each of the four fields has a similar thematic structure. At very detailed levels, the Mesh and Title fields run out of phrases relative to the Abstract field. Therefore, selection of field (s) to be employed for taxonomy generation depends on the objectives of the study, particularly the level of categorization required for the taxonomy. For information retrieval, or literature-based discovery, selection of the appropriate field again depends on the study objectives. If large queries, or large numbers of concepts or themes are desired, then the field with the largest number of technical phrases would be desirable. If queries or concepts represented by the more accepted popular terminology is adequate, then the smaller fields may be sufficient. Because of its established and controlled vocabulary, the Mesh field lags the Title or Abss the Title or Abstract fields in currency. Thus, the Title or Abstract fields would retrieve records with the most explicitly stated current concepts, but the Mesh field would capture a larger swath of fields that contained a concept of interest but perhaps had a wider range of specific terminology in the Abstract or Title text. In addition, this study provides the first validated estimate of the disparity in information retrieved through text mining limited to Titles and Mesh terms relative to entire Abstracts. As much of the older biomedical literature was entered into electronic databases without associated Abstracts, literature-based discovery exercises that search the older medical literature may miss a substantial proportion of relevant information. On the basis of this study, it may be estimated that up to a log order more information may be retrieved when complete Abstracts are searched.

[The added value of information summaries supporting clinical decisions at the point-of-care.

PubMed

Banzi, Rita; González-Lorenzo, Marien; Kwag, Koren Hyogene; Bonovas, Stefanos; Moja, Lorenzo

2016-11-01

Evidence-based healthcare requires the integration of the best research evidence with clinical expertise and patients' values. International publishers are developing evidence-based information services and resources designed to overcome the difficulties in retrieving, assessing and updating medical information as well as to facilitate a rapid access to valid clinical knowledge. Point-of-care information summaries are defined as web-based medical compendia that are specifically designed to deliver pre-digested, rapidly accessible, comprehensive, and periodically updated information to health care providers. Their validity must be assessed against marketing claims that they are evidence-based. We periodically evaluate the content development processes of several international point-of-care information summaries. The number of these products has increased along with their quality. The last analysis done in 2014 identified 26 products and found that three of them (Best Practice, Dynamed e Uptodate) scored the highest across all evaluated dimensions (volume, quality of the editorial process and evidence-based methodology). Point-of-care information summaries as stand-alone products or integrated with other systems, are gaining ground to support clinical decisions. The choice of one product over another depends both on the properties of the service and the preference of users. However, even the most innovative information system must rely on transparent and valid contents. Individuals and institutions should regularly assess the value of point-of-care summaries as their quality changes rapidly over time.

Psychophysical experiments on the PicHunter image retrieval system

NASA Astrophysics Data System (ADS)

Papathomas, Thomas V.; Cox, Ingemar J.; Yianilos, Peter N.; Miller, Matt L.; Minka, Thomas P.; Conway, Tiffany E.; Ghosn, Joumana

2001-01-01

Psychophysical experiments were conducted on PicHunter, a content-based image retrieval (CBIR) experimental prototype with the following properties: (1) Based on a model of how users respond, it uses Bayes's rule to predict what target users want, given their actions. (2) It possesses an extremely simple user interface. (3) It employs an entropy- based scheme to improve convergence. (4) It introduces a paradigm for assessing the performance of CBIR systems. Experiments 1-3 studied human judgment of image similarity to obtain data for the model. Experiment 4 studied the importance of using: (a) semantic information, (b) memory of earlier input, and (c) relative and absolute judgments of similarity. Experiment 5 tested an approach that we propose for comparing performances of CBIR systems objectively. Finally, experiment 6 evaluated the most informative display-updating scheme that is based on entropy minimization, and confirmed earlier simulation results. These experiments represent one of the first attempts to quantify CBIR performance based on psychophysical studies, and they provide valuable data for improving CBIR algorithms. Even though they were designed with PicHunter in mind, their results can be applied to any CBIR system and, more generally, to any system that involves judgment of image similarity by humans.

Surfing for suicide methods and help: content analysis of websites retrieved with search engines in Austria and the United States.

PubMed

Till, Benedikt; Niederkrotenthaler, Thomas

2014-08-01

The Internet provides a variety of resources for individuals searching for suicide-related information. Structured content-analytic approaches to assess intercultural differences in web contents retrieved with method-related and help-related searches are scarce. We used the 2 most popular search engines (Google and Yahoo/Bing) to retrieve US-American and Austrian search results for the term suicide, method-related search terms (e.g., suicide methods, how to kill yourself, painless suicide, how to hang yourself), and help-related terms (e.g., suicidal thoughts, suicide help) on February 11, 2013. In total, 396 websites retrieved with US search engines and 335 websites from Austrian searches were analyzed with content analysis on the basis of current media guidelines for suicide reporting. We assessed the quality of websites and compared findings across search terms and between the United States and Austria. In both countries, protective outweighed harmful website characteristics by approximately 2:1. Websites retrieved with method-related search terms (e.g., how to hang yourself) contained more harmful (United States: P < .001, Austria: P < .05) and fewer protective characteristics (United States: P < .001, Austria: P < .001) compared to the term suicide. Help-related search terms (e.g., suicidal thoughts) yielded more websites with protective characteristics (United States: P = .07, Austria: P < .01). Websites retrieved with U.S. search engines generally had more protective characteristics (P < .001) than searches with Austrian search engines. Resources with harmful characteristics were better ranked than those with protective characteristics (United States: P < .01, Austria: P < .05). The quality of suicide-related websites obtained depends on the search terms used. Preventive efforts to improve the ranking of preventive web content, particularly regarding method-related search terms, seem necessary. © Copyright 2014 Physicians Postgraduate Press, Inc.

Out-of-Sample Extrapolation utilizing Semi-Supervised Manifold Learning (OSE-SSL): Content Based Image Retrieval for Histopathology Images

PubMed Central

Sparks, Rachel; Madabhushi, Anant

2016-01-01

Content-based image retrieval (CBIR) retrieves database images most similar to the query image by (1) extracting quantitative image descriptors and (2) calculating similarity between database and query image descriptors. Recently, manifold learning (ML) has been used to perform CBIR in a low dimensional representation of the high dimensional image descriptor space to avoid the curse of dimensionality. ML schemes are computationally expensive, requiring an eigenvalue decomposition (EVD) for every new query image to learn its low dimensional representation. We present out-of-sample extrapolation utilizing semi-supervised ML (OSE-SSL) to learn the low dimensional representation without recomputing the EVD for each query image. OSE-SSL incorporates semantic information, partial class label, into a ML scheme such that the low dimensional representation co-localizes semantically similar images. In the context of prostate histopathology, gland morphology is an integral component of the Gleason score which enables discrimination between prostate cancer aggressiveness. Images are represented by shape features extracted from the prostate gland. CBIR with OSE-SSL for prostate histology obtained from 58 patient studies, yielded an area under the precision recall curve (AUPRC) of 0.53 ± 0.03 comparatively a CBIR with Principal Component Analysis (PCA) to learn a low dimensional space yielded an AUPRC of 0.44 ± 0.01. PMID:27264985

Teaching Three-Dimensional Structural Chemistry Using Crystal Structure Databases. 3. The Cambridge Structural Database System: Information Content and Access Software in Educational Applications

ERIC Educational Resources Information Center

Battle, Gary M.; Allen, Frank H.; Ferrence, Gregory M.

2011-01-01

Parts 1 and 2 of this series described the educational value of experimental three-dimensional (3D) chemical structures determined by X-ray crystallography and retrieved from the crystallographic databases. In part 1, we described the information content of the Cambridge Structural Database (CSD) and discussed a representative teaching subset of…

PageRank without hyperlinks: Reranking with PubMed related article networks for biomedical text retrieval

PubMed Central

Lin, Jimmy

2008-01-01

Background Graph analysis algorithms such as PageRank and HITS have been successful in Web environments because they are able to extract important inter-document relationships from manually-created hyperlinks. We consider the application of these techniques to biomedical text retrieval. In the current PubMed® search interface, a MEDLINE® citation is connected to a number of related citations, which are in turn connected to other citations. Thus, a MEDLINE record represents a node in a vast content-similarity network. This article explores the hypothesis that these networks can be exploited for text retrieval, in the same manner as hyperlink graphs on the Web. Results We conducted a number of reranking experiments using the TREC 2005 genomics track test collection in which scores extracted from PageRank and HITS analysis were combined with scores returned by an off-the-shelf retrieval engine. Experiments demonstrate that incorporating PageRank scores yields significant improvements in terms of standard ranked-retrieval metrics. Conclusion The link structure of content-similarity networks can be exploited to improve the effectiveness of information retrieval systems. These results generalize the applicability of graph analysis algorithms to text retrieval in the biomedical domain. PMID:18538027

Science information systems: Archive, access, and retrieval

NASA Technical Reports Server (NTRS)

Campbell, William J.

1991-01-01

The objective of this research is to develop technology for the automated characterization and interactive retrieval and visualization of very large, complex scientific data sets. Technologies will be developed for the following specific areas: (1) rapidly archiving data sets; (2) automatically characterizing and labeling data in near real-time; (3) providing users with the ability to browse contents of databases efficiently and effectively; (4) providing users with the ability to access and retrieve system independent data sets electronically; and (5) automatically alerting scientists to anomalies detected in data.

Recounting a Common Experience: On the Effectiveness of Instructing Eyewitness Pairs

PubMed Central

Vredeveldt, Annelies; van Koppen, Peter J.

2018-01-01

Pairs of eyewitnesses with a content-focused interaction style remember significantly more about witnessed incidents. We examined whether content-focused retrieval strategies can be taught. Seventy-five pairs of witnesses were interviewed thrice about an event. The first and third interview were conducted individually for all witnesses. The second interview was individual, collaborative without instruction, or collaborative with instruction. Pairs in the latter condition were instructed to actively listen to and elaborate upon each other's contributions. The strategy instruction had no effect on retrieval strategies used, nor on the amount or accuracy of reported information. However, pairs who spontaneously adopted a content-focused interaction style during the collaborative interview remembered significantly more. Thus, our findings show that effective retrieval strategies cannot be taught, at least not with the current instructions. During the second interview, we observed collaborative inhibition and error pruning. When considering the total amount of information reported across the first two interviews, however, collaboration had no inhibitory effect on correct recall, yet the error pruning benefits remained. These findings suggest that investigative interviewers should interview witnesses separately first, and then interview pairs of witnesses collaboratively. PMID:29593599

An interference model of visual working memory.

PubMed

Oberauer, Klaus; Lin, Hsuan-Yu

2017-01-01

The article introduces an interference model of working memory for information in a continuous similarity space, such as the features of visual objects. The model incorporates the following assumptions: (a) Probability of retrieval is determined by the relative activation of each retrieval candidate at the time of retrieval; (b) activation comes from 3 sources in memory: cue-based retrieval using context cues, context-independent memory for relevant contents, and noise; (c) 1 memory object and its context can be held in the focus of attention, where it is represented with higher precision, and partly shielded against interference. The model was fit to data from 4 continuous-reproduction experiments testing working memory for colors or orientations. The experiments involved variations of set size, kind of context cues, precueing, and retro-cueing of the to-be-tested item. The interference model fit the data better than 2 competing models, the Slot-Averaging model and the Variable-Precision resource model. The interference model also fared well in comparison to several new models incorporating alternative theoretical assumptions. The experiments confirm 3 novel predictions of the interference model: (a) Nontargets intrude in recall to the extent that they are close to the target in context space; (b) similarity between target and nontarget features improves recall, and (c) precueing-but not retro-cueing-the target substantially reduces the set-size effect. The success of the interference model shows that working memory for continuous visual information works according to the same principles as working memory for more discrete (e.g., verbal) contents. Data and model codes are available at https://osf.io/wgqd5/. (PsycINFO Database Record (c) 2016 APA, all rights reserved).

Secure quantum private information retrieval using phase-encoded queries

NASA Astrophysics Data System (ADS)

Olejnik, Lukasz

2011-08-01

We propose a quantum solution to the classical private information retrieval (PIR) problem, which allows one to query a database in a private manner. The protocol offers privacy thresholds and allows the user to obtain information from a database in a way that offers the potential adversary, in this model the database owner, no possibility of deterministically establishing the query contents. This protocol may also be viewed as a solution to the symmetrically private information retrieval problem in that it can offer database security (inability for a querying user to steal its contents). Compared to classical solutions, the protocol offers substantial improvement in terms of communication complexity. In comparison with the recent quantum private queries [Phys. Rev. Lett.PRLTAO0031-900710.1103/PhysRevLett.100.230502 100, 230502 (2008)] protocol, it is more efficient in terms of communication complexity and the number of rounds, while offering a clear privacy parameter. We discuss the security of the protocol and analyze its strengths and conclude that using this technique makes it challenging to obtain the unconditional (in the information-theoretic sense) privacy degree; nevertheless, in addition to being simple, the protocol still offers a privacy level. The oracle used in the protocol is inspired both by the classical computational PIR solutions as well as the Deutsch-Jozsa oracle.

Secure quantum private information retrieval using phase-encoded queries

DOE Office of Scientific and Technical Information (OSTI.GOV)

Olejnik, Lukasz

We propose a quantum solution to the classical private information retrieval (PIR) problem, which allows one to query a database in a private manner. The protocol offers privacy thresholds and allows the user to obtain information from a database in a way that offers the potential adversary, in this model the database owner, no possibility of deterministically establishing the query contents. This protocol may also be viewed as a solution to the symmetrically private information retrieval problem in that it can offer database security (inability for a querying user to steal its contents). Compared to classical solutions, the protocol offersmore » substantial improvement in terms of communication complexity. In comparison with the recent quantum private queries [Phys. Rev. Lett. 100, 230502 (2008)] protocol, it is more efficient in terms of communication complexity and the number of rounds, while offering a clear privacy parameter. We discuss the security of the protocol and analyze its strengths and conclude that using this technique makes it challenging to obtain the unconditional (in the information-theoretic sense) privacy degree; nevertheless, in addition to being simple, the protocol still offers a privacy level. The oracle used in the protocol is inspired both by the classical computational PIR solutions as well as the Deutsch-Jozsa oracle.« less

JURASSIC Retrieval Processing

NASA Astrophysics Data System (ADS)

Blank, J.; Ungermann, J.; Guggenmoser, T.; Kaufmann, M.; Riese, M.

2012-04-01

The Gimballed Limb Observer for Radiance Imaging in the Atmosphere (GLORIA) is an aircraft based infrared limb-sounder. This presentation will give an overview of the retrieval techniques used for the analysis of data produced by the GLORIA instrument. For data processing, the JUelich RApid Spectral SImulation Code 2 (JURASSIC2) was developed. It consists of a set of programs to retrieve atmospheric profiles from GLORIA measurements. The GLORIA Michelson interferometer can run with a wide range of parameters. In the dynamics mode, spectra are generate with a medium spectral and a very high temporal and spatial resolution. Each sample can contain thousands of spectral lines for each contributing trace gas. In the JURASSIC retrieval code this is handled by using a radiative transport model based on the Emissivity Growth Approximation. Deciding which samples should be included in the retrieval is a non-trivial task and requires specific domain knowledge. To ease this problem we developed an automatic selection program by analysing the Shannon information content. By taking into account data for all relevant trace gases and instrument effects, optimal integrated spectral windows are computed. This includes considerations for cross-influence of trace gases, which has non-obvious consequence for the contribution of spectral samples. We developed methods to assess the influence of spectral windows on the retrieval. While we can not exhaustively search the whole range of possible spectral sample combinations, it is possible to optimize information content using a genetic algorithm. The GLORIA instrument is mounted with a viewing direction perpendicular to the flight direction. A gimbal frame makes it possible to move the instrument 45° to both direction. By flying on a circular path, it is possible to generate images of an area of interest from a wide range of angles. These can be analyzed in a 3D-tomographic fashion, which yields superior spatial resolution along line of site. Usually limb instruments have a resolution of several hundred kilometers. In studies we have shown to get a resolution of 35km in all horizontal directions. Even when only linear flight patterns can be realized, resolutions of ≈70km can be obtained. This technique can be used to observe features of the Upper Troposphere Lower Stratosphere (UTLS), where important mixing processes take place. Especially tropopause folds are difficult to image, as their main features need to be along line of flight when using common 1D approach.

Clinical Information Systems as the Backbone of a Complex Information Logistics Process: Findings from the Clinical Information Systems Perspective for 2016.

PubMed

Hackl, W O; Ganslandt, T

2017-08-01

Objective: To summarize recent research and to propose a selection of best papers published in 2016 in the field of Clinical Information Systems (CIS). Method: The query used to retrieve the articles for the CIS section of the 2016 edition of the IMIA Yearbook of Medical Informatics was reused. It again aimed at identifying relevant publications in the field of CIS from PubMed and Web of Science and comprised search terms from the Medical Subject Headings (MeSH) catalog as well as additional free text search terms. The retrieved articles were categorized in a multi-pass review carried out by the two section editors. The final selection of candidate papers was then peer-reviewed by Yearbook editors and external reviewers. Based on the review results, the best papers were then chosen at the selection meeting with the IMIA Yearbook editorial board. Text mining, term co-occurrence mapping, and topic modelling techniques were used to get an overview on the content of the retrieved articles. Results: The query was carried out in mid-January 2017, yielding a consolidated result set of 2,190 articles published in 921 different journals. Out of them, 14 papers were nominated as candidate best papers and three of them were finally selected as the best papers of the CIS field. The content analysis of the articles revealed the broad spectrum of topics covered by CIS research. Conclusions: The CIS field is multi-dimensional and complex. It is hard to draw a well-defined outline between CIS and other domains or other sections of the IMIA Yearbook. The trends observed in the previous years are progressing. Clinical information systems are more than just sociotechnical systems for data collection, processing, exchange, presentation, and archiving. They are the backbone of a complex, trans-institutional information logistics process. Georg Thieme Verlag KG Stuttgart.

Computer-Assisted Search Of Large Textual Data Bases

NASA Technical Reports Server (NTRS)

Driscoll, James R.

1995-01-01

"QA" denotes high-speed computer system for searching diverse collections of documents including (but not limited to) technical reference manuals, legal documents, medical documents, news releases, and patents. Incorporates previously available and emerging information-retrieval technology to help user intelligently and rapidly locate information found in large textual data bases. Technology includes provision for inquiries in natural language; statistical ranking of retrieved information; artificial-intelligence implementation of semantics, in which "surface level" knowledge found in text used to improve ranking of retrieved information; and relevance feedback, in which user's judgements of relevance of some retrieved documents used automatically to modify search for further information.

Informatics in radiology: RADTF: a semantic search-enabled, natural language processor-generated radiology teaching file.

PubMed

Do, Bao H; Wu, Andrew; Biswal, Sandip; Kamaya, Aya; Rubin, Daniel L

2010-11-01

Storing and retrieving radiology cases is an important activity for education and clinical research, but this process can be time-consuming. In the process of structuring reports and images into organized teaching files, incidental pathologic conditions not pertinent to the primary teaching point can be omitted, as when a user saves images of an aortic dissection case but disregards the incidental osteoid osteoma. An alternate strategy for identifying teaching cases is text search of reports in radiology information systems (RIS), but retrieved reports are unstructured, teaching-related content is not highlighted, and patient identifying information is not removed. Furthermore, searching unstructured reports requires sophisticated retrieval methods to achieve useful results. An open-source, RadLex(®)-compatible teaching file solution called RADTF, which uses natural language processing (NLP) methods to process radiology reports, was developed to create a searchable teaching resource from the RIS and the picture archiving and communication system (PACS). The NLP system extracts and de-identifies teaching-relevant statements from full reports to generate a stand-alone database, thus converting existing RIS archives into an on-demand source of teaching material. Using RADTF, the authors generated a semantic search-enabled, Web-based radiology archive containing over 700,000 cases with millions of images. RADTF combines a compact representation of the teaching-relevant content in radiology reports and a versatile search engine with the scale of the entire RIS-PACS collection of case material. ©RSNA, 2010

[Design and implementation of medical instrument standard information retrieval system based on APS.NET].

PubMed

Yu, Kaijun

2010-07-01

This paper Analys the design goals of Medical Instrumentation standard information retrieval system. Based on the B /S structure,we established a medical instrumentation standard retrieval system with ASP.NET C # programming language, IIS f Web server, SQL Server 2000 database, in the. NET environment. The paper also Introduces the system structure, retrieval system modules, system development environment and detailed design of the system.

Health consumer-oriented information retrieval.

PubMed

Claveau, Vincent; Hamon, Thierry; Le Maguer, Sébastien; Grabar, Natalia

2015-01-01

While patients can freely access their Electronic Health Records or online health information, they may not be able to correctly understand the content of these documents. One of the challenges is related to the difference between expert and non-expert languages. We propose to investigate this issue within the Information Retrieval field. The patient queries have to be associated with the corresponding expert documents, that provide trustworthy information. Our approach relies on a state-of-the-art IR system called Indri and on semantic resources. Different query expansion strategies are explored. Our system shows up to 0.6740 P@10, up to 0.7610 R@10, and up to 0.6793 NDCG@10.

Content based information retrieval in forensic image databases.

PubMed

Geradts, Zeno; Bijhold, Jurrien

2002-03-01

This paper gives an overview of the various available image databases and ways of searching these databases on image contents. The developments in research groups of searching in image databases is evaluated and compared with the forensic databases that exist. Forensic image databases of fingerprints, faces, shoeprints, handwriting, cartridge cases, drugs tablets, and tool marks are described. The developments in these fields appear to be valuable for forensic databases, especially that of the framework in MPEG-7, where the searching in image databases is standardized. In the future, the combination of the databases (also DNA-databases) and possibilities to combine these can result in stronger forensic evidence.

Joint Leaf chlorophyll and leaf area index retrieval from Landsat data using a regularized model inversion system

USDA-ARS?s Scientific Manuscript database

Leaf area index (LAI) and leaf chlorophyll (Chl) content represent key biophysical and biochemical controls on water, energy and carbon exchange processes in the terrestrial biosphere. In combination, LAI and leaf Chl content provide critical information on vegetation density, vitality and photosynt...

An Inter-Personal Information Sharing Model Based on Personalized Recommendations

NASA Astrophysics Data System (ADS)

Kamei, Koji; Funakoshi, Kaname; Akahani, Jun-Ichi; Satoh, Tetsuji

In this paper, we propose an inter-personal information sharing model among individuals based on personalized recommendations. In the proposed model, we define an information resource as shared between people when both of them consider it important --- not merely when they both possess it. In other words, the model defines the importance of information resources based on personalized recommendations from identifiable acquaintances. The proposed method is based on a collaborative filtering system that focuses on evaluations from identifiable acquaintances. It utilizes both user evaluations for documents and their contents. In other words, each user profile is represented as a matrix of credibility to the other users' evaluations on each domain of interests. We extended the content-based collaborative filtering method to distinguish other users to whom the documents should be recommended. We also applied a concept-based vector space model to represent the domain of interests instead of the previous method which represented them by a term-based vector space model. We introduce a personalized concept-base compiled from each user's information repository to improve the information retrieval in the user's environment. Furthermore, the concept-spaces change from user to user since they reflect the personalities of the users. Because of different concept-spaces, the similarity between a document and a user's interest varies for each user. As a result, a user receives recommendations from other users who have different view points, achieving inter-personal information sharing based on personalized recommendations. This paper also describes an experimental simulation of our information sharing model. In our laboratory, five participants accumulated a personal repository of e-mails and web pages from which they built their own concept-base. Then we estimated the user profiles according to personalized concept-bases and sets of documents which others evaluated. We simulated inter-personal recommendation based on the user profiles and evaluated the performance of the recommendation method by comparing the recommended documents to the result of the content-based collaborative filtering.

Aerosol typing - key information from aerosol studies

NASA Astrophysics Data System (ADS)

Mona, Lucia; Kahn, Ralph; Papagiannopoulos, Nikolaos; Holzer-Popp, Thomas; Pappalardo, Gelsomina

2016-04-01

Aerosol typing is a key source of aerosol information from ground-based and satellite-borne instruments. Depending on the specific measurement technique, aerosol typing can be used as input for retrievals or represents an output for other applications. Typically aerosol retrievals require some a priori or external aerosol type information. The accuracy of the derived aerosol products strongly depends on the reliability of these assumptions. Different sensors can make use of different aerosol type inputs. A critical review and harmonization of these procedures could significantly reduce related uncertainties. On the other hand, satellite measurements in recent years are providing valuable information about the global distribution of aerosol types, showing for example the main source regions and typical transport paths. Climatological studies of aerosol load at global and regional scales often rely on inferred aerosol type. There is still a high degree of inhomogeneity among satellite aerosol typing schemes, which makes the use different sensor datasets in a consistent way difficult. Knowledge of the 4d aerosol type distribution at these scales is essential for understanding the impact of different aerosol sources on climate, precipitation and air quality. All this information is needed for planning upcoming aerosol emissions policies. The exchange of expertise and the communication among satellite and ground-based measurement communities is fundamental for improving long-term dataset consistency, and for reducing aerosol type distribution uncertainties. Aerosol typing has been recognized as one of its high-priority activities of the AEROSAT (International Satellite Aerosol Science Network, http://aero-sat.org/) initiative. In the AEROSAT framework, a first critical review of aerosol typing procedures has been carried out. The review underlines the high heterogeneity in many aspects: approach, nomenclature, assumed number of components and parameters used for the classification. The harmonization of the aerosol typing procedures is a fundamental need in aerosol studies for long-term perspectives, satellite validation, and accuracy. However, the possibilities and limits in defining a common set of aerosol types for satellite missions and ground-based measurements depends on different information content among measurement techniques and for different retrieval conditions (e.g. for low aerosol content there is smaller satellite aerosol type retrieval sensitivity), as well as different historical choices. The concept of aReference database for aerosol typing (REDAT) is developed with the specific purpose of providing a dataset suitable for the comparison of typing procedures (from ground-based, and satellite measurements) and to be used as reference dataset for the modelling community. It will also allow the definition of translating rules between the different aerosol typing nomenclature, information strongly needed for the more and more increased audience of scientific data with no scientific background, as well as policy and decision makers. Acknowledgments: The research leading to these results is partially funded by ACTRIS2 Research Infrastructure Project by the European Union's Horizon 2020 research and innovation programme under the grant agreement n. 654169.

Modeling the Time Course of Feature Perception and Feature Information Retrieval

ERIC Educational Resources Information Center

Kent, Christopher; Lamberts, Koen

2006-01-01

Three experiments investigated whether retrieval of information about different dimensions of a visual object varies as a function of the perceptual properties of those dimensions. The experiments involved two perception-based matching tasks and two retrieval-based matching tasks. A signal-to-respond methodology was used in all tasks. A stochastic…

Advanced Feedback Methods in Information Retrieval.

ERIC Educational Resources Information Center

Salton, G.; And Others

1985-01-01

In this study, automatic feedback techniques are applied to Boolean query statements in online information retrieval to generate improved query statements based on information contained in previously retrieved documents. Feedback operations are carried out using conventional Boolean logic and extended logic. Experimental output is included to…

A new randomized Kaczmarz based kernel canonical correlation analysis algorithm with applications to information retrieval.

PubMed

Cai, Jia; Tang, Yi

2018-02-01

Canonical correlation analysis (CCA) is a powerful statistical tool for detecting the linear relationship between two sets of multivariate variables. Kernel generalization of it, namely, kernel CCA is proposed to describe nonlinear relationship between two variables. Although kernel CCA can achieve dimensionality reduction results for high-dimensional data feature selection problem, it also yields the so called over-fitting phenomenon. In this paper, we consider a new kernel CCA algorithm via randomized Kaczmarz method. The main contributions of the paper are: (1) A new kernel CCA algorithm is developed, (2) theoretical convergence of the proposed algorithm is addressed by means of scaled condition number, (3) a lower bound which addresses the minimum number of iterations is presented. We test on both synthetic dataset and several real-world datasets in cross-language document retrieval and content-based image retrieval to demonstrate the effectiveness of the proposed algorithm. Numerical results imply the performance and efficiency of the new algorithm, which is competitive with several state-of-the-art kernel CCA methods. Copyright © 2017 Elsevier Ltd. All rights reserved.

The CMIP5 Model Documentation Questionnaire: Development of a Metadata Retrieval System for the METAFOR Common Information Model

NASA Astrophysics Data System (ADS)

Pascoe, Charlotte; Lawrence, Bryan; Moine, Marie-Pierre; Ford, Rupert; Devine, Gerry

2010-05-01

The EU METAFOR Project (http://metaforclimate.eu) has created a web-based model documentation questionnaire to collect metadata from the modelling groups that are running simulations in support of the Coupled Model Intercomparison Project - 5 (CMIP5). The CMIP5 model documentation questionnaire will retrieve information about the details of the models used, how the simulations were carried out, how the simulations conformed to the CMIP5 experiment requirements and details of the hardware used to perform the simulations. The metadata collected by the CMIP5 questionnaire will allow CMIP5 data to be compared in a scientifically meaningful way. This paper describes the life-cycle of the CMIP5 questionnaire development which starts with relatively unstructured input from domain specialists and ends with formal XML documents that comply with the METAFOR Common Information Model (CIM). Each development step is associated with a specific tool. (1) Mind maps are used to capture information requirements from domain experts and build a controlled vocabulary, (2) a python parser processes the XML files generated by the mind maps, (3) Django (python) is used to generate the dynamic structure and content of the web based questionnaire from processed xml and the METAFOR CIM, (4) Python parsers ensure that information entered into the CMIP5 questionnaire is output as CIM compliant xml, (5) CIM compliant output allows automatic information capture tools to harvest questionnaire content into databases such as the Earth System Grid (ESG) metadata catalogue. This paper will focus on how Django (python) and XML input files are used to generate the structure and content of the CMIP5 questionnaire. It will also address how the choice of development tools listed above provided a framework that enabled working scientists (who we would never ordinarily get to interact with UML and XML) to be part the iterative development process and ensure that the CMIP5 model documentation questionnaire reflects what scientists want to know about the models. Keywords: metadata, CMIP5, automatic information capture, tool development

Retrieval of tropospheric carbon monoxide for the MOPITT experiment

NASA Astrophysics Data System (ADS)

Pan, Liwen; Gille, John C.; Edwards, David P.; Bailey, Paul L.; Rodgers, Clive D.

1998-12-01

A retrieval method for deriving the tropospheric carbon monoxide (CO) profile and column amount under clear sky conditions has been developed for the Measurements of Pollution In The Troposphere (MOPITT) instrument, scheduled for launch in 1998 onboard the EOS-AM1 satellite. This paper presents a description of the method along with analyses of retrieval information content. These analyses characterize the forward measurement sensitivity, the contribution of a priori information, and the retrieval vertical resolution. Ensembles of tropospheric CO profiles were compiled both from aircraft in situ measurements and from chemical model results and were used in retrieval experiments to characterize the method and to study the sensitivity to different parameters. Linear error analyses were carried out in parallel with the ensemble experiments. Results of these experiments and analyses indicate that MOPITT CO column measurements will have better than 10% precision, and CO profile measurement will have approximately three pieces of independent information that will resolve 3-5 tropospheric layers to approximately 10% precision. These analyses are important for understanding MOPITT data, both for application of data in tropospheric chemistry studies and for comparison with in situ measurements.

Distributed representations in memory: Insights from functional brain imaging

PubMed Central

Rissman, Jesse; Wagner, Anthony D.

2015-01-01

Forging new memories for facts and events, holding critical details in mind on a moment-to-moment basis, and retrieving knowledge in the service of current goals all depend on a complex interplay between neural ensembles throughout the brain. Over the past decade, researchers have increasingly leveraged powerful analytical tools (e.g., multi-voxel pattern analysis) to decode the information represented within distributed fMRI activity patterns. In this review, we discuss how these methods can sensitively index neural representations of perceptual and semantic content, and how leverage on the engagement of distributed representations provides unique insights into distinct aspects of memory-guided behavior. We emphasize that, in addition to characterizing the contents of memories, analyses of distributed patterns shed light on the processes that influence how information is encoded, maintained, or retrieved, and thus inform memory theory. We conclude by highlighting open questions about memory that can be addressed through distributed pattern analyses. PMID:21943171

A novel content-based medical image retrieval method based on query topic dependent image features (QTDIF)

NASA Astrophysics Data System (ADS)

Xiong, Wei; Qiu, Bo; Tian, Qi; Mueller, Henning; Xu, Changsheng

2005-04-01

Medical image retrieval is still mainly a research domain with a large variety of applications and techniques. With the ImageCLEF 2004 benchmark, an evaluation framework has been created that includes a database, query topics and ground truth data. Eleven systems (with a total of more than 50 runs) compared their performance in various configurations. The results show that there is not any one feature that performs well on all query tasks. Key to successful retrieval is rather the selection of features and feature weights based on a specific set of input features, thus on the query task. In this paper we propose a novel method based on query topic dependent image features (QTDIF) for content-based medical image retrieval. These feature sets are designed to capture both inter-category and intra-category statistical variations to achieve good retrieval performance in terms of recall and precision. We have used Gaussian Mixture Models (GMM) and blob representation to model medical images and construct the proposed novel QTDIF for CBIR. Finally, trained multi-class support vector machines (SVM) are used for image similarity ranking. The proposed methods have been tested over the Casimage database with around 9000 images, for the given 26 image topics, used for imageCLEF 2004. The retrieval performance has been compared with the medGIFT system, which is based on the GNU Image Finding Tool (GIFT). The experimental results show that the proposed QTDIF-based CBIR can provide significantly better performance than systems based general features only.

The Effect of Bilingual Term List Size on Dictionary-Based Cross-Language Information Retrieval

DTIC Science & Technology

2003-02-01

FEB 2003 2. REPORT TYPE 3. DATES COVERED 00-00-2003 to 00-00-2003 4. TITLE AND SUBTITLE The Effect of Bilingual Term List Size on Dictionary ...298 (Rev. 8-98) Prescribed by ANSI Std Z39-18 The Effect of Bilingual Term List Size on Dictionary -Based Cross-Language Information Retrieval Dina...are extensively used as a resource for dictionary -based Cross-Language Information Retrieval (CLIR), in which the goal is to find documents written

Web-based multimedia information retrieval for clinical application research

NASA Astrophysics Data System (ADS)

Cao, Xinhua; Hoo, Kent S., Jr.; Zhang, Hong; Ching, Wan; Zhang, Ming; Wong, Stephen T. C.

2001-08-01

We described a web-based data warehousing method for retrieving and analyzing neurological multimedia information. The web-based method supports convenient access, effective search and retrieval of clinical textual and image data, and on-line analysis. To improve the flexibility and efficiency of multimedia information query and analysis, a three-tier, multimedia data warehouse for epilepsy research has been built. The data warehouse integrates clinical multimedia data related to epilepsy from disparate sources and archives them into a well-defined data model.

Toward visual user interfaces supporting collaborative multimedia content management

NASA Astrophysics Data System (ADS)

Husein, Fathi; Leissler, Martin; Hemmje, Matthias

2000-12-01

Supporting collaborative multimedia content management activities, as e.g., image and video acquisition, exploration, and access dialogues between naive users and multi media information systems is a non-trivial task. Although a wide variety of experimental and prototypical multimedia storage technologies as well as corresponding indexing and retrieval engines are available, most of them lack appropriate support for collaborative end-user oriented user interface front ends. The development of advanced user adaptable interfaces is necessary for building collaborative multimedia information- space presentations based upon advanced tools for information browsing, searching, filtering, and brokering to be applied on potentially very large and highly dynamic multimedia collections with a large number of users and user groups. Therefore, the development of advanced and at the same time adaptable and collaborative computer graphical information presentation schemes that allow to easily apply adequate visual metaphors for defined target user stereotypes has to become a key focus within ongoing research activities trying to support collaborative information work with multimedia collections.

Assessing the content and quality of information on the treatment of postmenopausal osteoporosis on the World Wide Web.

PubMed

Pérez-López, Faustino R; Pérez Roncero, Gonzalo R

2006-12-01

To evaluate the content and quality of currently available Internet-based information on the treatment of postmenopausal osteoporosis. A sample was obtained comprising the 75 top sites retrieved with the Google search engine using 'treatment of postmenopausal osteoporosis' and then evaluated according to predefined general and specific criteria, content type, language and quality. Using a systematic scoring tool, each site was assessed for factual information provided and site quality. The sites studied were heterogeneous in content and quality. The most frequent type of website corresponded to non-profit organizations (n = 40), followed by commercial sites (n = 19), professional sites (n = 8) and government sites (n = 8). There were no significant differences in the popularity index, medical content score or quality score among the four groups of sites. Twelve websites were papers published in peer-reviewed medical journals. Few sites provided comprehensive medical and complete information on the treatment of postmenopausal osteoporosis oriented towards consumers. The consumer-oriented webpage with the most balanced and complete information was that of the National Osteoporosis Foundation which, at the same time, had the highest popularity index of all the resources studied. The content and quality of websites concerning the treatment of postmenopausal osteoporosis are highly varied and sometimes biased. The most frequent high-quality information corresponds to peer-reviewed medical journals. It is necessary to increase the number of resources, with rigorous language that is understandable for consumers, in relation to the treatment of postmenopausal osteoporosis.

CDAPubMed: a browser extension to retrieve EHR-based biomedical literature.

PubMed

Perez-Rey, David; Jimenez-Castellanos, Ana; Garcia-Remesal, Miguel; Crespo, Jose; Maojo, Victor

2012-04-05

Over the last few decades, the ever-increasing output of scientific publications has led to new challenges to keep up to date with the literature. In the biomedical area, this growth has introduced new requirements for professionals, e.g., physicians, who have to locate the exact papers that they need for their clinical and research work amongst a huge number of publications. Against this backdrop, novel information retrieval methods are even more necessary. While web search engines are widespread in many areas, facilitating access to all kinds of information, additional tools are required to automatically link information retrieved from these engines to specific biomedical applications. In the case of clinical environments, this also means considering aspects such as patient data security and confidentiality or structured contents, e.g., electronic health records (EHRs). In this scenario, we have developed a new tool to facilitate query building to retrieve scientific literature related to EHRs. We have developed CDAPubMed, an open-source web browser extension to integrate EHR features in biomedical literature retrieval approaches. Clinical users can use CDAPubMed to: (i) load patient clinical documents, i.e., EHRs based on the Health Level 7-Clinical Document Architecture Standard (HL7-CDA), (ii) identify relevant terms for scientific literature search in these documents, i.e., Medical Subject Headings (MeSH), automatically driven by the CDAPubMed configuration, which advanced users can optimize to adapt to each specific situation, and (iii) generate and launch literature search queries to a major search engine, i.e., PubMed, to retrieve citations related to the EHR under examination. CDAPubMed is a platform-independent tool designed to facilitate literature searching using keywords contained in specific EHRs. CDAPubMed is visually integrated, as an extension of a widespread web browser, within the standard PubMed interface. It has been tested on a public dataset of HL7-CDA documents, returning significantly fewer citations since queries are focused on characteristics identified within the EHR. For instance, compared with more than 200,000 citations retrieved by breast neoplasm, fewer than ten citations were retrieved when ten patient features were added using CDAPubMed. This is an open source tool that can be freely used for non-profit purposes and integrated with other existing systems.

CDAPubMed: a browser extension to retrieve EHR-based biomedical literature

PubMed Central

2012-01-01

Background Over the last few decades, the ever-increasing output of scientific publications has led to new challenges to keep up to date with the literature. In the biomedical area, this growth has introduced new requirements for professionals, e.g., physicians, who have to locate the exact papers that they need for their clinical and research work amongst a huge number of publications. Against this backdrop, novel information retrieval methods are even more necessary. While web search engines are widespread in many areas, facilitating access to all kinds of information, additional tools are required to automatically link information retrieved from these engines to specific biomedical applications. In the case of clinical environments, this also means considering aspects such as patient data security and confidentiality or structured contents, e.g., electronic health records (EHRs). In this scenario, we have developed a new tool to facilitate query building to retrieve scientific literature related to EHRs. Results We have developed CDAPubMed, an open-source web browser extension to integrate EHR features in biomedical literature retrieval approaches. Clinical users can use CDAPubMed to: (i) load patient clinical documents, i.e., EHRs based on the Health Level 7-Clinical Document Architecture Standard (HL7-CDA), (ii) identify relevant terms for scientific literature search in these documents, i.e., Medical Subject Headings (MeSH), automatically driven by the CDAPubMed configuration, which advanced users can optimize to adapt to each specific situation, and (iii) generate and launch literature search queries to a major search engine, i.e., PubMed, to retrieve citations related to the EHR under examination. Conclusions CDAPubMed is a platform-independent tool designed to facilitate literature searching using keywords contained in specific EHRs. CDAPubMed is visually integrated, as an extension of a widespread web browser, within the standard PubMed interface. It has been tested on a public dataset of HL7-CDA documents, returning significantly fewer citations since queries are focused on characteristics identified within the EHR. For instance, compared with more than 200,000 citations retrieved by breast neoplasm, fewer than ten citations were retrieved when ten patient features were added using CDAPubMed. This is an open source tool that can be freely used for non-profit purposes and integrated with other existing systems. PMID:22480327

An Abstraction-Based Data Model for Information Retrieval

NASA Astrophysics Data System (ADS)

McAllister, Richard A.; Angryk, Rafal A.

Language ontologies provide an avenue for automated lexical analysis that may be used to supplement existing information retrieval methods. This paper presents a method of information retrieval that takes advantage of WordNet, a lexical database, to generate paths of abstraction, and uses them as the basis for an inverted index structure to be used in the retrieval of documents from an indexed corpus. We present this method as a entree to a line of research on using ontologies to perform word-sense disambiguation and improve the precision of existing information retrieval techniques.

Multi-source and ontology-based retrieval engine for maize mutant phenotypes

PubMed Central

Green, Jason M.; Harnsomburana, Jaturon; Schaeffer, Mary L.; Lawrence, Carolyn J.; Shyu, Chi-Ren

2011-01-01

Model Organism Databases, including the various plant genome databases, collect and enable access to massive amounts of heterogeneous information, including sequence data, gene product information, images of mutant phenotypes, etc, as well as textual descriptions of many of these entities. While a variety of basic browsing and search capabilities are available to allow researchers to query and peruse the names and attributes of phenotypic data, next-generation search mechanisms that allow querying and ranking of text descriptions are much less common. In addition, the plant community needs an innovative way to leverage the existing links in these databases to search groups of text descriptions simultaneously. Furthermore, though much time and effort have been afforded to the development of plant-related ontologies, the knowledge embedded in these ontologies remains largely unused in available plant search mechanisms. Addressing these issues, we have developed a unique search engine for mutant phenotypes from MaizeGDB. This advanced search mechanism integrates various text description sources in MaizeGDB to aid a user in retrieving desired mutant phenotype information. Currently, descriptions of mutant phenotypes, loci and gene products are utilized collectively for each search, though expansion of the search mechanism to include other sources is straightforward. The retrieval engine, to our knowledge, is the first engine to exploit the content and structure of available domain ontologies, currently the Plant and Gene Ontologies, to expand and enrich retrieval results in major plant genomic databases. Database URL: http:www.PhenomicsWorld.org/QBTA.php PMID:21558151

Information content of MOPITT CO profile retrievals: Temporal and geographical variability

NASA Astrophysics Data System (ADS)

Deeter, M. N.; Edwards, D. P.; Gille, J. C.; Worden, H. M.

2015-12-01

Satellite measurements of tropospheric carbon monoxide (CO) enable a wide array of applications including studies of air quality and pollution transport. The MOPITT (Measurements of Pollution in the Troposphere) instrument on the Earth Observing System Terra platform has been measuring CO concentrations globally since March 2000. As indicated by the Degrees of Freedom for Signal (DFS), the standard metric for trace-gas retrieval information content, MOPITT retrieval performance varies over a wide range. We show that both instrumental and geophysical effects yield significant geographical and temporal variability in MOPITT DFS values. Instrumental radiance uncertainties, which describe random errors (or "noise") in the calibrated radiances, vary over long time scales (e.g., months to years) and vary between the four detector elements of MOPITT's linear detector array. MOPITT retrieval performance depends on several factors including thermal contrast, fine-scale variability of surface properties, and CO loading. The relative importance of these various effects is highly variable, as demonstrated by analyses of monthly mean DFS values for the United States and the Amazon Basin. An understanding of the geographical and temporal variability of MOPITT retrieval performance is potentially valuable to data users seeking to limit the influence of the a priori through data filtering. To illustrate, it is demonstrated that calculated regional-average CO mixing ratios may be improved by excluding observations from a subset of pixels in MOPITT's linear detector array.

Global Contrast Based Salient Region Detection.

PubMed

Cheng, Ming-Ming; Mitra, Niloy J; Huang, Xiaolei; Torr, Philip H S; Hu, Shi-Min

2015-03-01

Automatic estimation of salient object regions across images, without any prior assumption or knowledge of the contents of the corresponding scenes, enhances many computer vision and computer graphics applications. We introduce a regional contrast based salient object detection algorithm, which simultaneously evaluates global contrast differences and spatial weighted coherence scores. The proposed algorithm is simple, efficient, naturally multi-scale, and produces full-resolution, high-quality saliency maps. These saliency maps are further used to initialize a novel iterative version of GrabCut, namely SaliencyCut, for high quality unsupervised salient object segmentation. We extensively evaluated our algorithm using traditional salient object detection datasets, as well as a more challenging Internet image dataset. Our experimental results demonstrate that our algorithm consistently outperforms 15 existing salient object detection and segmentation methods, yielding higher precision and better recall rates. We also show that our algorithm can be used to efficiently extract salient object masks from Internet images, enabling effective sketch-based image retrieval (SBIR) via simple shape comparisons. Despite such noisy internet images, where the saliency regions are ambiguous, our saliency guided image retrieval achieves a superior retrieval rate compared with state-of-the-art SBIR methods, and additionally provides important target object region information.

Optimality in Data Assimilation

NASA Astrophysics Data System (ADS)

Nearing, Grey; Yatheendradas, Soni

2016-04-01

It costs a lot more to develop and launch an earth-observing satellite than it does to build a data assimilation system. As such, we propose that it is important to understand the efficiency of our assimilation algorithms at extracting information from remote sensing retrievals. To address this, we propose that it is necessary to adopt completely general definition of "optimality" that explicitly acknowledges all differences between the parametric constraints of our assimilation algorithm (e.g., Gaussianity, partial linearity, Markovian updates) and the true nature of the environmetnal system and observing system. In fact, it is not only possible, but incredibly straightforward, to measure the optimality (in this more general sense) of any data assimilation algorithm as applied to any intended model or natural system. We measure the information content of remote sensing data conditional on the fact that we are already running a model and then measure the actual information extracted by data assimilation. The ratio of the two is an efficiency metric, and optimality is defined as occurring when the data assimilation algorithm is perfectly efficient at extracting information from the retrievals. We measure the information content of the remote sensing data in a way that, unlike triple collocation, does not rely on any a priori presumed relationship (e.g., linear) between the retrieval and the ground truth, however, like triple-collocation, is insensitive to the spatial mismatch between point-based measurements and grid-scale retrievals. This theory and method is therefore suitable for use with both dense and sparse validation networks. Additionally, the method we propose is *constructive* in the sense that it provides guidance on how to improve data assimilation systems. All data assimilation strategies can be reduced to approximations of Bayes' law, and we measure the fractions of total information loss that are due to individual assumptions or approximations in the prior (i.e., the model uncertainty distribution), and in the likelihood (i.e., the observation operator and observation uncertainty distribution). In this way, we can directly identify the parts of a data assimilation algorithm that contribute most to assimilation error in a way that (unlike traditional DA performance metrics) considers nonlinearity in the model and observation and non-optimality in the fit between filter assumptions and the real system. To reiterate, the method we propose is theoretically rigorous but also dead-to-rights simple, and can be implemented in no more than a few hours by a competent programmer. We use this to show that careful applications of the Ensemble Kalman Filter use substantially less than half of the information contained in remote sensing soil moisture retrievals (LPRM, AMSR-E, SMOS, and SMOPS). We propose that this finding may explain some of the results from several recent large-scale experiments that show lower-than-expected value to assimilating soil moisture retrievals into land surface models forced by high-quality precipitation data. Our results have important implications for the SMAP mission because over half of the SMAP-affiliated "early adopters" plan to use the EnKF as their primary method for extracting information from SMAP retrievals.

Practical life log video indexing based on content and context

NASA Astrophysics Data System (ADS)

Tancharoen, Datchakorn; Yamasaki, Toshihiko; Aizawa, Kiyoharu

2006-01-01

Today, multimedia information has gained an important role in daily life and people can use imaging devices to capture their visual experiences. In this paper, we present our personal Life Log system to record personal experiences in form of wearable video and environmental data; in addition, an efficient retrieval system is demonstrated to recall the desirable media. We summarize the practical video indexing techniques based on Life Log content and context to detect talking scenes by using audio/visual cues and semantic key frames from GPS data. Voice annotation is also demonstrated as a practical indexing method. Moreover, we apply body media sensors to record continuous life style and use body media data to index the semantic key frames. In the experiments, we demonstrated various video indexing results which provided their semantic contents and showed Life Log visualizations to examine personal life effectively.

An Online Needs Assessment of a Virtual Community: What Men who use the Internet to seek Sex with Men want in Internet-based HIV Prevention

PubMed Central

Hooper, Simon; Rosser, B. R. Simon; Horvath, Keith J.; Oakes, J. Michael; Danilenko, Gene

2008-01-01

As part of a study to develop effective Internet-based HIV prevention interventions for Men who use the Internet to seek Sex with Men (MISM), we sought information from the target population on (a) acceptability of sexually explicit media; (b) interest in specific content areas; and (c) identification of credible sources of information. A cross-sectional stratified Internet-based survey design was employed. Between September and November 2005, we recruited 2,716 MISM through Gay.com stratified across race/ethnicity to ensure adequate racial/ethnic diversity. Sixteen Likert-type items assessed acceptability of sexual explicitness, 24 items identified topics for inclusion, and two assessed sources of information. There was near universal acceptability for highly sexually explicit education. Over 75 percent reported high interest in ten sexual health topics. HIV positive MISM and MISM engaged in unprotected anal sex with multiple male partners reported significantly less interest in HIV prevention specific content. Differences across age, race/ethnicity and education were identified. Idiosyncratic searches and gay sites were frequently cited sources of information; however blogs, government, and media sites were not. It is acceptable for web-based HIV prevention for MISM to be highly sexually explicit and to provide detailed content relevant to men's sexual health. Since demographic differences in acceptability and content were minor, it is appropriate for interventions to target across demographics. Interventions to re-engage men engaging in high risk and HIV+ MISM should be considered. Leading health agencies should review whether their web information is retrievable, credible and useful to those most at risk. PMID:18401701

Retrieval of Spatio-temporal Evaporation by Integrating Landsat OLI Optical and Thermal Data

NASA Astrophysics Data System (ADS)

Wandera, L. N.; Tol, C. V. D.; Mallick, K.; Bayat, B.; Verbeiren, B.; van Griensven, A.; Verhoef, W.; Suliga, J.; Barrios, J. M.; Chormański, J.; Kleniewska, M.

2017-12-01

Soil-Vegetation-Atmosphere (SVAT) Transfer Models are capable of providing continuous predictions of evapotranspiration (ET). However, providing these models with reliable spatio-temporal information of vegetation and soil properties remains challenging. Thus, combining optical and thermal satellite information might assists to overcome this challenge when using SVAT models. In this study, using a radiative transfer model of solar and sky radiation (RTMo), we simulate Landsat 8 reflectance bands (2-7). We then apply a numerical optimization approach to invert the model and retrieve the corresponding canopy attributes leaf chlorophyll content (Cab), leaf water content (Cw), leaf dry matter content (Cdm), leaf brown material (Cs), Leaf Area Index (LAI) and the leaf angle distribution function in the canopy at overpass time. The retrievals are then directly used as inputs into our SVAT model of choice, Soil Canopy Observations of Photochemistry and Energy Fluxes (SCOPE). Using a model for transfer of thermal radiation emitted by vegetation and soil (RTMt), we proceed to simulate Landsat radiance for the corresponding reflectance data using a lookup table (LUT). These variables were then used to develop a crop factor (Kc) map. A reference ET was generated and applied to the Kc map to obtain actual ET. We proceeded to interpolate the ET between the image acquisition dates to have a complete time series. The retrieval maps for the specific variables captured seasonal variability patterns for the respective variables. The generated KC map showed similar trend with the LAI maps. There was an underestimation of actual ET when the simulation was not constrained to the thermal information. The interpolation of ET between acquisition image dates reflected the seasonal trends. Key Word: SVAT, optical, thermal, remote sensing, evapotranspiration

Medial Temporal Lobe Contributions to Cued Retrieval of Items and Contexts

PubMed Central

Hannula, Deborah E.; Libby, Laura A.; Yonelinas, Andrew P.; Ranganath, Charan

2013-01-01

Several models have proposed that different regions of the medial temporal lobes contribute to different aspects of episodic memory. For instance, according to one view, the perirhinal cortex represents specific items, parahippocampal cortex represents information regarding the context in which these items were encountered, and the hippocampus represents item-context bindings. Here, we used event-related functional magnetic resonance imaging (fMRI) to test a specific prediction of this model – namely, that successful retrieval of items from context cues will elicit perirhinal recruitment and that successful retrieval of contexts from item cues will elicit parahippocampal cortex recruitment. Retrieval of the bound representation in either case was expected to elicit hippocampal engagement. To test these predictions, we had participants study several item-context pairs (i.e., pictures of objects and scenes, respectively), and then had them attempt to recall items from associated context cues and contexts from associated item cues during a scanned retrieval session. Results based on both univariate and multivariate analyses confirmed a role for hippocampus in content-general relational memory retrieval, and a role for parahippocampal cortex in successful retrieval of contexts from item cues. However, we also found that activity differences in perirhinal cortex were correlated with successful cued recall for both items and contexts. These findings provide partial support for the above predictions and are discussed with respect to several models of medial temporal lobe function. PMID:23466350

[Vegetation index estimation by chlorophyll content of grassland based on spectral analysis].

PubMed

Xiao, Han; Chen, Xiu-Wan; Yang, Zhen-Yu; Li, Huai-Yu; Zhu, Han

2014-11-01

Comparing the methods of existing remote sensing research on the estimation of chlorophyll content, the present paper confirms that the vegetation index is one of the most practical and popular research methods. In recent years, the increasingly serious problem of grassland degradation. This paper, firstly, analyzes the measured reflectance spectral curve and its first derivative curve in the grasslands of Songpan, Sichuan and Gongger, Inner Mongolia, conducts correlation analysis between these two spectral curves and chlorophyll content, and finds out the regulation between REP (red edge position) and grassland chlorophyll content, that is, the higher the chlorophyll content is, the higher the REIP (red-edge inflection point) value would be. Then, this paper constructs GCI (grassland chlorophyll index) and selects the most suitable band for retrieval. Finally, this paper calculates the GCI by the use of satellite hyperspectral image, conducts the verification and accuracy analysis of the calculation results compared with chlorophyll content data collected from field of twice experiments. The result shows that for grassland chlorophyll content, GCI has stronger sensitivity than other indices of chlorophyll, and has higher estimation accuracy. GCI is the first proposed to estimate the grassland chlorophyll content, and has wide application potential for the remote sensing retrieval of grassland chlorophyll content. In addition, the grassland chlorophyll content estimation method based on remote sensing retrieval in this paper provides new research ideas for other vegetation biochemical parameters' estimation, vegetation growth status' evaluation and grassland ecological environment change's monitoring.

Construction of In-house Databases in a Corporation

NASA Astrophysics Data System (ADS)

Koga, Kunitoshi; Kijima, Seiichi

The rapid technological innovation and alternation of the economical society have increased the importance of obtaining the accurate information promptly in order to make a timely decision. Therefore, UBE Industries Ltd. has developed “UMATIS”, an information manegement system, since July in 1985, where not only the technical but also manegerial information is accumulated and one can obtain it quickly as occasion demands. In addition to the brief introduction of an outline and the progress of the system, this gives the detailed description of the contents of the registered items, the indexing method, the utilization of retrieval, the manegerial method and so on in the main technical information system. The personal relation information system and the inorgnanic material data base system are also explained briefly.

A New Methodology for Simultaneous Multi-layer Retrievals of Ice and Liquid Water Cloud Properties

NASA Astrophysics Data System (ADS)

Sourdeval, O.; Labonnote, L.; Baran, A. J.; Brogniez, G.

2014-12-01

It is widely recognized that the study of clouds has nowadays become one of the major concern of the climate research community. Consequently, a multitude of retrieval methodologies have been developed during the last decades in order to obtain accurate retrievals of cloud properties that can be supplied to climate models. Most of the current methodologies have proven to be satisfactory for separately retrieving ice or liquid cloud properties, but very few of them have attempted simultaneous retrievals of these two cloud types. Recent studies nevertheless show that the omission of one of these layers can have strong consequences on the retrievals and their accuracy. In this study, a new methodology that simultaneously retrieves the properties of ice and liquid clouds is presented. The optical thickness and the effective radius of up to two liquid cloud layers and the ice water path of one ice cloud layer are simultaneously retrieved, along with an accurate estimation of their uncertainties. Radiometric measurements ranging from the visible to the thermal infrared are used for performing the retrievals. In order to quantify the capabilities and limitations of our methodology, the results of a theoretical information content analysis are first presented. This analysis allows obtaining an a priori understanding of how much information should be expected on each of the retrieval parameters in different atmospheric conditions, and which set of channels is likely to provide this information. After such theoretical considerations, global retrievals corresponding to several months of A-Train data are presented. Comparisons of our retrievals with operational products from active and passive instruments are effectuated and show good global agreements. These comparisons are useful for validating our retrievals but also for testing how operational products can be influenced by multi-layer configurations.

Sagace: A web-based search engine for biomedical databases in Japan

PubMed Central

2012-01-01

Background In the big data era, biomedical research continues to generate a large amount of data, and the generated information is often stored in a database and made publicly available. Although combining data from multiple databases should accelerate further studies, the current number of life sciences databases is too large to grasp features and contents of each database. Findings We have developed Sagace, a web-based search engine that enables users to retrieve information from a range of biological databases (such as gene expression profiles and proteomics data) and biological resource banks (such as mouse models of disease and cell lines). With Sagace, users can search more than 300 databases in Japan. Sagace offers features tailored to biomedical research, including manually tuned ranking, a faceted navigation to refine search results, and rich snippets constructed with retrieved metadata for each database entry. Conclusions Sagace will be valuable for experts who are involved in biomedical research and drug development in both academia and industry. Sagace is freely available at http://sagace.nibio.go.jp/en/. PMID:23110816

On combining image-based and ontological semantic dissimilarities for medical image retrieval applications

PubMed Central

Kurtz, Camille; Depeursinge, Adrien; Napel, Sandy; Beaulieu, Christopher F.; Rubin, Daniel L.

2014-01-01

Computer-assisted image retrieval applications can assist radiologists by identifying similar images in archives as a means to providing decision support. In the classical case, images are described using low-level features extracted from their contents, and an appropriate distance is used to find the best matches in the feature space. However, using low-level image features to fully capture the visual appearance of diseases is challenging and the semantic gap between these features and the high-level visual concepts in radiology may impair the system performance. To deal with this issue, the use of semantic terms to provide high-level descriptions of radiological image contents has recently been advocated. Nevertheless, most of the existing semantic image retrieval strategies are limited by two factors: they require manual annotation of the images using semantic terms and they ignore the intrinsic visual and semantic relationships between these annotations during the comparison of the images. Based on these considerations, we propose an image retrieval framework based on semantic features that relies on two main strategies: (1) automatic “soft” prediction of ontological terms that describe the image contents from multi-scale Riesz wavelets and (2) retrieval of similar images by evaluating the similarity between their annotations using a new term dissimilarity measure, which takes into account both image-based and ontological term relations. The combination of these strategies provides a means of accurately retrieving similar images in databases based on image annotations and can be considered as a potential solution to the semantic gap problem. We validated this approach in the context of the retrieval of liver lesions from computed tomographic (CT) images and annotated with semantic terms of the RadLex ontology. The relevance of the retrieval results was assessed using two protocols: evaluation relative to a dissimilarity reference standard defined for pairs of images on a 25-images dataset, and evaluation relative to the diagnoses of the retrieved images on a 72-images dataset. A normalized discounted cumulative gain (NDCG) score of more than 0.92 was obtained with the first protocol, while AUC scores of more than 0.77 were obtained with the second protocol. This automatical approach could provide real-time decision support to radiologists by showing them similar images with associated diagnoses and, where available, responses to therapies. PMID:25036769

Finding Information on the World Wide Web: The Retrieval Effectiveness of Search Engines.

ERIC Educational Resources Information Center

Pathak, Praveen; Gordon, Michael

1999-01-01

Describes a study that examined the effectiveness of eight search engines for the World Wide Web. Calculated traditional information-retrieval measures of recall and precision at varying numbers of retrieved documents to use as the bases for statistical comparisons of retrieval effectiveness. Also examined the overlap between search engines.…

Indexing the medical open access literature for textual and content-based visual retrieval.

PubMed

Eggel, Ivan; Müller, Henning

2010-01-01

Over the past few years an increasing amount of scientific journals have been created in an open access format. Particularly in the medical field the number of openly accessible journals is enormous making a wide body of knowledge available for analysis and retrieval. Part of the trend towards open access publications can be linked to funding bodies such as the NIH¹ (National Institutes of Health) and the Swiss National Science Foundation (SNF²) requiring funded projects to make all articles of funded research available publicly. This article describes an approach to make part of the knowledge of open access journals available for retrieval including the textual information but also the images contained in the articles. For this goal all articles of 24 journals related to medical informatics and medical imaging were crawled from the web pages of BioMed Central. Text and images of the PDF (Portable Document Format) files were indexed separately and a web-based retrieval interface allows for searching via keyword queries or by visual similarity queries. Starting point for a visual similarity query can be an image on the local hard disk that is uploaded or any image found via the textual search. Search for similar documents is also possible.

Plain English in Owner Manuals and User Guides.

ERIC Educational Resources Information Center

Gray, Lee L.

1987-01-01

The author makes some specific suggestions for writers of manuals and user guides in the need for (1) an overview, (2) an informative table of contents, and (3) an opening summary for each chapter. Task-oriented informative subheads facilitate the retrieval of information. The kind of use and the user's needs affect the organization and…

intelligentCAPTURE 1.0 Adds Tables of Content to Library Catalogues and Improves Retrieval.

ERIC Educational Resources Information Center

Hauer, Manfred; Simedy, Walton

2002-01-01

Describes an online library catalog that was developed for an Austrian scientific library that includes table of contents in addition to the standard bibliographic information in order to increase relevance for searchers. Discusses the technology involved, including OCR (Optical Character Recognition) and automatic indexing techniques; weighted…

Content-based image retrieval with ontological ranking

NASA Astrophysics Data System (ADS)

Tsai, Shen-Fu; Tsai, Min-Hsuan; Huang, Thomas S.

2010-02-01

Images are a much more powerful medium of expression than text, as the adage says: "One picture is worth a thousand words." It is because compared with text consisting of an array of words, an image has more degrees of freedom and therefore a more complicated structure. However, the less limited structure of images presents researchers in the computer vision community a tough task of teaching machines to understand and organize images, especially when a limit number of learning examples and background knowledge are given. The advance of internet and web technology in the past decade has changed the way human gain knowledge. People, hence, can exchange knowledge with others by discussing and contributing information on the web. As a result, the web pages in the internet have become a living and growing source of information. One is therefore tempted to wonder whether machines can learn from the web knowledge base as well. Indeed, it is possible to make computer learn from the internet and provide human with more meaningful knowledge. In this work, we explore this novel possibility on image understanding applied to semantic image search. We exploit web resources to obtain links from images to keywords and a semantic ontology constituting human's general knowledge. The former maps visual content to related text in contrast to the traditional way of associating images with surrounding text; the latter provides relations between concepts for machines to understand to what extent and in what sense an image is close to the image search query. With the aid of these two tools, the resulting image search system is thus content-based and moreover, organized. The returned images are ranked and organized such that semantically similar images are grouped together and given a rank based on the semantic closeness to the input query. The novelty of the system is twofold: first, images are retrieved not only based on text cues but their actual contents as well; second, the grouping is different from pure visual similarity clustering. More specifically, the inferred concepts of each image in the group are examined in the context of a huge concept ontology to determine their true relations with what people have in mind when doing image search.

Information-rich spectral channels for simulated retrievals of partial column-averaged methane

NASA Astrophysics Data System (ADS)

Su, Zhan; Xi, Xi; Natraj, Vijay; Li, King-Fai; Shia, Run-Lie; Miller, Charles E.; Yung, Yuk L.

2016-01-01

Space-based remote sensing of the column-averaged methane dry air mole fraction (XCH4) has greatly increased our understanding of the spatiotemporal patterns in the global methane cycle. The potential to retrieve multiple pieces of vertical profile information would further improve the quantification of CH4 across space-time scales. We conduct information analysis for channel selection and evaluate the prospects of retrieving multiple pieces of information as well as total column CH4 from both ground-based and space-based near-infrared remote sensing spectra. We analyze the degrees of freedom of signal (DOF) in the CH4 absorption bands near 2.3 μm and 1.6 μm and select ˜1% of the channels that contain >95% of the information about the CH4 profile. The DOF is around 4 for fine ground-based spectra (resolution = 0.01 cm-1) and 3 for coarse space-based spectra (resolution = 0.20 cm-1) based on channel selection and a signal-to-noise ratio (SNR) of 300. The DOF varies from 2.2 to 3.2 when SNR is between 100 and 300, and spectral resolution is 0.20 cm-1. Simulated retrieval tests in clear-sky conditions using the selected channels reveal that the retrieved partial column-averaged CH4 values are not sensitive to the a priori profiles and can reflect local enhancements of CH4 in different partial air columns. Both the total and partial column-averaged retrieval errors in all tests are within 1% of the true state. These simulated tests highlight the possibility to retrieve up to three to four pieces of information about the vertical distribution of CH4 in reality.

A Hybrid Approach to Finding Relevant Social Media Content for Complex Domain Specific Information Needs.

PubMed

Cameron, Delroy; Sheth, Amit P; Jaykumar, Nishita; Thirunarayan, Krishnaprasad; Anand, Gaurish; Smith, Gary A

2014-12-01

While contemporary semantic search systems offer to improve classical keyword-based search, they are not always adequate for complex domain specific information needs. The domain of prescription drug abuse, for example, requires knowledge of both ontological concepts and "intelligible constructs" not typically modeled in ontologies. These intelligible constructs convey essential information that include notions of intensity, frequency, interval, dosage and sentiments, which could be important to the holistic needs of the information seeker. In this paper, we present a hybrid approach to domain specific information retrieval that integrates ontology-driven query interpretation with synonym-based query expansion and domain specific rules, to facilitate search in social media on prescription drug abuse. Our framework is based on a context-free grammar (CFG) that defines the query language of constructs interpretable by the search system. The grammar provides two levels of semantic interpretation: 1) a top-level CFG that facilitates retrieval of diverse textual patterns, which belong to broad templates and 2) a low-level CFG that enables interpretation of specific expressions belonging to such textual patterns. These low-level expressions occur as concepts from four different categories of data: 1) ontological concepts, 2) concepts in lexicons (such as emotions and sentiments), 3) concepts in lexicons with only partial ontology representation, called lexico-ontology concepts (such as side effects and routes of administration (ROA)), and 4) domain specific expressions (such as date, time, interval, frequency and dosage) derived solely through rules. Our approach is embodied in a novel Semantic Web platform called PREDOSE, which provides search support for complex domain specific information needs in prescription drug abuse epidemiology. When applied to a corpus of over 1 million drug abuse-related web forum posts, our search framework proved effective in retrieving relevant documents when compared with three existing search systems.

A Hybrid Approach to Finding Relevant Social Media Content for Complex Domain Specific Information Needs

PubMed Central

Cameron, Delroy; Sheth, Amit P.; Jaykumar, Nishita; Thirunarayan, Krishnaprasad; Anand, Gaurish; Smith, Gary A.

2015-01-01

While contemporary semantic search systems offer to improve classical keyword-based search, they are not always adequate for complex domain specific information needs. The domain of prescription drug abuse, for example, requires knowledge of both ontological concepts and “intelligible constructs” not typically modeled in ontologies. These intelligible constructs convey essential information that include notions of intensity, frequency, interval, dosage and sentiments, which could be important to the holistic needs of the information seeker. In this paper, we present a hybrid approach to domain specific information retrieval that integrates ontology-driven query interpretation with synonym-based query expansion and domain specific rules, to facilitate search in social media on prescription drug abuse. Our framework is based on a context-free grammar (CFG) that defines the query language of constructs interpretable by the search system. The grammar provides two levels of semantic interpretation: 1) a top-level CFG that facilitates retrieval of diverse textual patterns, which belong to broad templates and 2) a low-level CFG that enables interpretation of specific expressions belonging to such textual patterns. These low-level expressions occur as concepts from four different categories of data: 1) ontological concepts, 2) concepts in lexicons (such as emotions and sentiments), 3) concepts in lexicons with only partial ontology representation, called lexico-ontology concepts (such as side effects and routes of administration (ROA)), and 4) domain specific expressions (such as date, time, interval, frequency and dosage) derived solely through rules. Our approach is embodied in a novel Semantic Web platform called PREDOSE, which provides search support for complex domain specific information needs in prescription drug abuse epidemiology. When applied to a corpus of over 1 million drug abuse-related web forum posts, our search framework proved effective in retrieving relevant documents when compared with three existing search systems. PMID:25814917

An automatic method for retrieving and indexing catalogues of biomedical courses.

PubMed

Maojo, Victor; de la Calle, Guillermo; García-Remesal, Miguel; Bankauskaite, Vaida; Crespo, Jose

2008-11-06

Although there is wide information about Biomedical Informatics education and courses in different Websites, information is usually not exhaustive and difficult to update. We propose a new methodology based on information retrieval techniques for extracting, indexing and retrieving automatically information about educational offers. A web application has been developed to make available such information in an inventory of courses and educational offers.

Multimodal medical information retrieval with unsupervised rank fusion.

PubMed

Mourão, André; Martins, Flávio; Magalhães, João

2015-01-01

Modern medical information retrieval systems are paramount to manage the insurmountable quantities of clinical data. These systems empower health care experts in the diagnosis of patients and play an important role in the clinical decision process. However, the ever-growing heterogeneous information generated in medical environments poses several challenges for retrieval systems. We propose a medical information retrieval system with support for multimodal medical case-based retrieval. The system supports medical information discovery by providing multimodal search, through a novel data fusion algorithm, and term suggestions from a medical thesaurus. Our search system compared favorably to other systems in 2013 ImageCLEFMedical. Copyright © 2014 Elsevier Ltd. All rights reserved.

Design of Content Based Image Retrieval Scheme for Diabetic Retinopathy Images using Harmony Search Algorithm.

PubMed

Sivakamasundari, J; Natarajan, V

2015-01-01

Diabetic Retinopathy (DR) is a disorder that affects the structure of retinal blood vessels due to long-standing diabetes mellitus. Automated segmentation of blood vessel is vital for periodic screening and timely diagnosis. An attempt has been made to generate continuous retinal vasculature for the design of Content Based Image Retrieval (CBIR) application. The typical normal and abnormal retinal images are preprocessed to improve the vessel contrast. The blood vessels are segmented using evolutionary based Harmony Search Algorithm (HSA) combined with Otsu Multilevel Thresholding (MLT) method by best objective functions. The segmentation results are validated with corresponding ground truth images using binary similarity measures. The statistical, textural and structural features are obtained from the segmented images of normal and DR affected retina and are analyzed. CBIR in medical image retrieval applications are used to assist physicians in clinical decision-support techniques and research fields. A CBIR system is developed using HSA based Otsu MLT segmentation technique and the features obtained from the segmented images. Similarity matching is carried out between the features of query and database images using Euclidean Distance measure. Similar images are ranked and retrieved. The retrieval performance of CBIR system is evaluated in terms of precision and recall. The CBIR systems developed using HSA based Otsu MLT and conventional Otsu MLT methods are compared. The retrieval performance such as precision and recall are found to be 96% and 58% for CBIR system using HSA based Otsu MLT segmentation. This automated CBIR system could be recommended for use in computer assisted diagnosis for diabetic retinopathy screening.

A new visual navigation system for exploring biomedical Open Educational Resource (OER) videos

PubMed Central

Zhao, Baoquan; Xu, Songhua; Lin, Shujin; Luo, Xiaonan; Duan, Lian

2016-01-01

Objective Biomedical videos as open educational resources (OERs) are increasingly proliferating on the Internet. Unfortunately, seeking personally valuable content from among the vast corpus of quality yet diverse OER videos is nontrivial due to limitations of today’s keyword- and content-based video retrieval techniques. To address this need, this study introduces a novel visual navigation system that facilitates users’ information seeking from biomedical OER videos in mass quantity by interactively offering visual and textual navigational clues that are both semantically revealing and user-friendly. Materials and Methods The authors collected and processed around 25 000 YouTube videos, which collectively last for a total length of about 4000 h, in the broad field of biomedical sciences for our experiment. For each video, its semantic clues are first extracted automatically through computationally analyzing audio and visual signals, as well as text either accompanying or embedded in the video. These extracted clues are subsequently stored in a metadata database and indexed by a high-performance text search engine. During the online retrieval stage, the system renders video search results as dynamic web pages using a JavaScript library that allows users to interactively and intuitively explore video content both efficiently and effectively. Results The authors produced a prototype implementation of the proposed system, which is publicly accessible at https://patentq.njit.edu/oer. To examine the overall advantage of the proposed system for exploring biomedical OER videos, the authors further conducted a user study of a modest scale. The study results encouragingly demonstrate the functional effectiveness and user-friendliness of the new system for facilitating information seeking from and content exploration among massive biomedical OER videos. Conclusion Using the proposed tool, users can efficiently and effectively find videos of interest, precisely locate video segments delivering personally valuable information, as well as intuitively and conveniently preview essential content of a single or a collection of videos. PMID:26335986

Strong Similarity Measures for Ordered Sets of Documents in Information Retrieval.

ERIC Educational Resources Information Center

Egghe, L.; Michel, Christine

2002-01-01

Presents a general method to construct ordered similarity measures in information retrieval based on classical similarity measures for ordinary sets. Describes a test of some of these measures in an information retrieval system that extracted ranked document sets and discuses the practical usability of the ordered similarity measures. (Author/LRW)

Linear information retrieval method in X-ray grating-based phase contrast imaging and its interchangeability with tomographic reconstruction

NASA Astrophysics Data System (ADS)

Wu, Z.; Gao, K.; Wang, Z. L.; Shao, Q. G.; Hu, R. F.; Wei, C. X.; Zan, G. B.; Wali, F.; Luo, R. H.; Zhu, P. P.; Tian, Y. C.

2017-06-01

In X-ray grating-based phase contrast imaging, information retrieval is necessary for quantitative research, especially for phase tomography. However, numerous and repetitive processes have to be performed for tomographic reconstruction. In this paper, we report a novel information retrieval method, which enables retrieving phase and absorption information by means of a linear combination of two mutually conjugate images. Thanks to the distributive law of the multiplication as well as the commutative law and associative law of the addition, the information retrieval can be performed after tomographic reconstruction, thus simplifying the information retrieval procedure dramatically. The theoretical model of this method is established in both parallel beam geometry for Talbot interferometer and fan beam geometry for Talbot-Lau interferometer. Numerical experiments are also performed to confirm the feasibility and validity of the proposed method. In addition, we discuss its possibility in cone beam geometry and its advantages compared with other methods. Moreover, this method can also be employed in other differential phase contrast imaging methods, such as diffraction enhanced imaging, non-interferometric imaging, and edge illumination.

Supervised learning of tools for content-based search of image databases

NASA Astrophysics Data System (ADS)

Delanoy, Richard L.

1996-03-01

A computer environment, called the Toolkit for Image Mining (TIM), is being developed with the goal of enabling users with diverse interests and varied computer skills to create search tools for content-based image retrieval and other pattern matching tasks. Search tools are generated using a simple paradigm of supervised learning that is based on the user pointing at mistakes of classification made by the current search tool. As mistakes are identified, a learning algorithm uses the identified mistakes to build up a model of the user's intentions, construct a new search tool, apply the search tool to a test image, display the match results as feedback to the user, and accept new inputs from the user. Search tools are constructed in the form of functional templates, which are generalized matched filters capable of knowledge- based image processing. The ability of this system to learn the user's intentions from experience contrasts with other existing approaches to content-based image retrieval that base searches on the characteristics of a single input example or on a predefined and semantically- constrained textual query. Currently, TIM is capable of learning spectral and textural patterns, but should be adaptable to the learning of shapes, as well. Possible applications of TIM include not only content-based image retrieval, but also quantitative image analysis, the generation of metadata for annotating images, data prioritization or data reduction in bandwidth-limited situations, and the construction of components for larger, more complex computer vision algorithms.

Document Indexing for Image-Based Optical Information Systems.

ERIC Educational Resources Information Center

Thiel, Thomas J.; And Others

1991-01-01

Discussion of image-based information retrieval systems focuses on indexing. Highlights include computerized information retrieval; multimedia optical systems; optical mass storage and personal computers; and a case study that describes an optical disk system which was developed to preserve, access, and disseminate military documents. (19…

A new retrieval method for the ice water content of cirrus using data from the CloudSat and CALIPSO

NASA Astrophysics Data System (ADS)

Pan, Honglin; Bu, Lingbing; Kumar, K. Raghavendra; Gao, Haiyang; Huang, Xingyou; Zhang, Wentao

2017-08-01

The CloudSat and CALIPSO (Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations) are the members of satellite observation system of A-train to achieve the quasi-synchronization observation on the same orbit. With the help of active (CALIOP and CPR) and passive payloads from these two satellites, respectively, unprecedented detailed information of microphysical properties of ice cloud can be retrieved. The ice water content (IWC) is regarded as one of the most important microphysical characteristics of cirrus for its prominent role in cloud radiative forcing. In this paper, we proposed a new joint (Combination) retrieval method using the full advantages of different well established retrieval methods, namely the LIDAR method (for the region Lidar-only), the MWCR method (for the region Radar-only), and Wang method (for the region Lidar-Radar) proposed by Wang et al. (2002). In retrieval of cirrus IWC, empirical formulas of the exponential type were used for both thinner cirrus (detected by Lidar-only), thicker cirrus (detected by radar-only), and the part of cirrus detected by both, respectively. In the present study, the comparison of various methods verified that our proposed new joint method is more comprehensive, rational and reliable. Further, the retrieval information of cirrus is complete and accurate for the region that Lidar cannot penetrate and Radar is insensitive. On the whole, the retrieval results of IWC showed certain differences retrieved from the joint method, Ca&Cl, and ICARE which can be interpreted from the different hypothesis of microphysical characteristics and parameters used in the retrieval method. In addition, our joint method only uses the extinction coefficient and the radar reflectivity factor to calculate the IWC, which is simpler and reduces to some extent the accumulative error. In future studies, we will not only compare the value of IWC but also explore the detailed macrophysical and microphysical characteristics of cirrus.

Impact of a priori information on IASI ozone retrievals and trends

NASA Astrophysics Data System (ADS)

Barret, B.; Peiro, H.; Emili, E.; Le Flocgmoën, E.

2017-12-01

The IASI sensor documents atmospheric water vapor, temperature and composition since 2007. The Software for a Fast Retrieval of IASI Data (SOFRID) has been developped to retrieve O3 and CO profiles from IASI in near-real time on a global scale. Information content analyses have shown that IASI enables the quantification of O3 independently in the troposphere, the UTLS and the stratosphere. Validation studies have demonstrated that the daily to seasonal variability of tropospheric and UTLS O3 was well captured by IASI especially in the tropics. IASI-SOFRID retrievals have also been used to document the tropospheric composition during the Asian monsoon and participated to determine the O3 evolution during the 2008-2016 period in the framework of the TOAR project. Nevertheless, IASI-SOFRID O3 is biased high in the UTLS and in the tropical troposphere and the 8 years O3 trends from the different IASI products are significantly different from the O3 trends from UV-Vis satellite sensors (e.g. OMI)..SOFRID is based on the Optimal Estimation Method that requires a priori information to complete the information provided by the measured thermal infrared radiances. In SOFRID-O3 v1.5 used in TOAR the a priori consists of a single O3 profile and associated covariance matrix based on global O3 radiosoundings. Such a global a priori is characterized by a very large variabilty and does not represent our best kowledge of the O3 profile at a given time and location. Furthermore it is biased towards the northern hemisphere middle latitudes. We have therefore implemented the possibility to use dynamical a priori data in SOFRID and performed experiments using O3 climatological data and MLS O3 analyses. We will present O3 distributions and comparisons with O3 radiosoundings from the different SOFRID-O3 retrievals. We will in particular assess the impact of the use of different a priori data upon the O3 biases and trends during the IASI period.

Interactive content-based image retrieval (CBIR) computer-aided diagnosis (CADx) system for ultrasound breast masses using relevance feedback

NASA Astrophysics Data System (ADS)

Cho, Hyun-chong; Hadjiiski, Lubomir; Sahiner, Berkman; Chan, Heang-Ping; Paramagul, Chintana; Helvie, Mark; Nees, Alexis V.

2012-03-01

We designed a Content-Based Image Retrieval (CBIR) Computer-Aided Diagnosis (CADx) system to assist radiologists in characterizing masses on ultrasound images. The CADx system retrieves masses that are similar to a query mass from a reference library based on computer-extracted features that describe texture, width-to-height ratio, and posterior shadowing of a mass. Retrieval is performed with k nearest neighbor (k-NN) method using Euclidean distance similarity measure and Rocchio relevance feedback algorithm (RRF). In this study, we evaluated the similarity between the query and the retrieved masses with relevance feedback using our interactive CBIR CADx system. The similarity assessment and feedback were provided by experienced radiologists' visual judgment. For training the RRF parameters, similarities of 1891 image pairs obtained from 62 masses were rated by 3 MQSA radiologists using a 9-point scale (9=most similar). A leave-one-out method was used in training. For each query mass, 5 most similar masses were retrieved from the reference library using radiologists' similarity ratings, which were then used by RRF to retrieve another 5 masses for the same query. The best RRF parameters were chosen based on three simulated observer experiments, each of which used one of the radiologists' ratings for retrieval and relevance feedback. For testing, 100 independent query masses on 100 images and 121 reference masses on 230 images were collected. Three radiologists rated the similarity between the query and the computer-retrieved masses. Average similarity ratings without and with RRF were 5.39 and 5.64 on the training set and 5.78 and 6.02 on the test set, respectively. The average Az values without and with RRF were 0.86+/-0.03 and 0.87+/-0.03 on the training set and 0.91+/-0.03 and 0.90+/-0.03 on the test set, respectively. This study demonstrated that RRF improved the similarity of the retrieved masses.

Retrieval of radiology reports citing critical findings with disease-specific customization.

PubMed

Lacson, Ronilda; Sugarbaker, Nathanael; Prevedello, Luciano M; Ivan, Ip; Mar, Wendy; Andriole, Katherine P; Khorasani, Ramin

2012-01-01

Communication of critical results from diagnostic procedures between caregivers is a Joint Commission national patient safety goal. Evaluating critical result communication often requires manual analysis of voluminous data, especially when reviewing unstructured textual results of radiologic findings. Information retrieval (IR) tools can facilitate this process by enabling automated retrieval of radiology reports that cite critical imaging findings. However, IR tools that have been developed for one disease or imaging modality often need substantial reconfiguration before they can be utilized for another disease entity. THIS PAPER: 1) describes the process of customizing two Natural Language Processing (NLP) and Information Retrieval/Extraction applications - an open-source toolkit, A Nearly New Information Extraction system (ANNIE); and an application developed in-house, Information for Searching Content with an Ontology-Utilizing Toolkit (iSCOUT) - to illustrate the varying levels of customization required for different disease entities and; 2) evaluates each application's performance in identifying and retrieving radiology reports citing critical imaging findings for three distinct diseases, pulmonary nodule, pneumothorax, and pulmonary embolus. Both applications can be utilized for retrieval. iSCOUT and ANNIE had precision values between 0.90-0.98 and recall values between 0.79 and 0.94. ANNIE had consistently higher precision but required more customization. Understanding the customizations involved in utilizing NLP applications for various diseases will enable users to select the most suitable tool for specific tasks.

Retrieval of Radiology Reports Citing Critical Findings with Disease-Specific Customization

PubMed Central

Lacson, Ronilda; Sugarbaker, Nathanael; Prevedello, Luciano M; Ivan, IP; Mar, Wendy; Andriole, Katherine P; Khorasani, Ramin

2012-01-01

Background: Communication of critical results from diagnostic procedures between caregivers is a Joint Commission national patient safety goal. Evaluating critical result communication often requires manual analysis of voluminous data, especially when reviewing unstructured textual results of radiologic findings. Information retrieval (IR) tools can facilitate this process by enabling automated retrieval of radiology reports that cite critical imaging findings. However, IR tools that have been developed for one disease or imaging modality often need substantial reconfiguration before they can be utilized for another disease entity. Purpose: This paper: 1) describes the process of customizing two Natural Language Processing (NLP) and Information Retrieval/Extraction applications – an open-source toolkit, A Nearly New Information Extraction system (ANNIE); and an application developed in-house, Information for Searching Content with an Ontology-Utilizing Toolkit (iSCOUT) – to illustrate the varying levels of customization required for different disease entities and; 2) evaluates each application’s performance in identifying and retrieving radiology reports citing critical imaging findings for three distinct diseases, pulmonary nodule, pneumothorax, and pulmonary embolus. Results: Both applications can be utilized for retrieval. iSCOUT and ANNIE had precision values between 0.90-0.98 and recall values between 0.79 and 0.94. ANNIE had consistently higher precision but required more customization. Conclusion: Understanding the customizations involved in utilizing NLP applications for various diseases will enable users to select the most suitable tool for specific tasks. PMID:22934127

Retrieval-Induced versus Context-Induced Forgetting: Does Retrieval-Induced Forgetting Depend on Context Shifts?

ERIC Educational Resources Information Center

Soares, Julia S.; Polack, Cody W.; Miller, Ralph R.

2016-01-01

Retrieval-induced forgetting (RIF) is the observation that retrieval of target information causes forgetting of related nontarget information. A number of accounts of this phenomenon have been proposed, including a context-shift-based account (Jonker, Seli, & Macleod, 2013). This account proposes that RIF occurs as a result of the context…

Hospital nurses' information retrieval behaviours in relation to evidence based nursing: a literature review.

PubMed

Alving, Berit Elisabeth; Christensen, Janne Buck; Thrysøe, Lars

2018-03-01

The purpose of this literature review is to provide an overview of the information retrieval behaviour of clinical nurses, in terms of the use of databases and other information resources and their frequency of use. Systematic searches carried out in five databases and handsearching were used to identify the studies from 2010 to 2016, with a populations, exposures and outcomes (PEO) search strategy, focusing on the question: In which databases or other information resources do hospital nurses search for evidence based information, and how often? Of 5272 titles retrieved based on the search strategy, only nine studies fulfilled the criteria for inclusion. The studies are from the United States, Canada, Taiwan and Nigeria. The results show that hospital nurses' primary choice of source for evidence based information is Google and peers, while bibliographic databases such as PubMed are secondary choices. Data on frequency are only included in four of the studies, and data are heterogenous. The reasons for choosing Google and peers are primarily lack of time; lack of information; lack of retrieval skills; or lack of training in database searching. Only a few studies are published on clinical nurses' retrieval behaviours, and more studies are needed from Europe and Australia. © 2018 Health Libraries Group.

Health data and data governance.

PubMed

Hovenga, Evelyn J S; Grain, Heather

2013-01-01

Health is a knowledge industry, based on data collected to support care, service planning, financing and knowledge advancement. Increasingly there is a need to collect, retrieve and use health record information in an electronic format to provide greater flexibility, as this enables retrieval and display of data in multiple locations and formats irrespective of where the data were collected. Electronically maintained records require greater structure and consistency to achieve this. The use of data held in records generated in real time in clinical systems also has the potential to reduce the time it takes to gain knowledge, as there is less need to collect research specific information, this is only possible if data governance principles are applied. Connected devices and information systems are now generating huge amounts of data, as never before seen. An ability to analyse and mine very large amounts of data, "Big Data", provides policy and decision makers with new insights into varied aspects of work and information flow and operational business patterns and trends, and drives greater efficiencies, and safer and more effective health care. This enables decision makers to apply rules and guidance that have been developed based upon knowledge from many individual patient records through recognition of triggers based upon that knowledge. In clinical decision support systems information about the individual is compared to rules based upon knowledge gained from accumulated information of many to provide guidance at appropriate times in the clinical process. To achieve this the data in the individual system, and the knowledge rules must be represented in a compatible and consistent manner. This chapter describes data attributes; explains the difference between data and information; outlines the requirements for quality data; shows the relevance of health data standards; and describes how data governance impacts representation of content in systems and the use of that information.

Understanding vaccination resistance: vaccine search term selection bias and the valence of retrieved information.

PubMed

Ruiz, Jeanette B; Bell, Robert A

2014-10-07

Dubious vaccination-related information on the Internet leads some parents to opt out of vaccinating their children. To determine if negative, neutral and positive search terms retrieve vaccination information that differs in valence and confirms searchers' assumptions about vaccination. A content analysis of first-page Google search results was conducted using three negative, three neutral, and three positive search terms for the concepts "vaccine," "vaccination," and "MMR"; 84 of the 90 websites retrieved met inclusion requirements. Two coders independently and reliably coded for the presence or absence of each of 15 myths about vaccination (e.g., "vaccines cause autism"), statements that countered these myths, and recommendations for or against vaccination. Data were analyzed using descriptive statistics. Across all websites, at least one myth was perpetuated on 16.7% of websites and at least one myth was countered on 64.3% of websites. The mean number of myths perpetuated on websites retrieved with negative, neutral, and positive search terms, respectively, was 1.93, 0.53, and 0.40. The mean number of myths countered on websites retrieved with negative, neutral, and positive search terms, respectively, was 3.0, 3.27, and 2.87. Explicit recommendations regarding vaccination were offered on 22.6% of websites. A recommendation against vaccination was more often made on websites retrieved with negative search terms (37.5% of recommendations) than on websites retrieved with neutral (12.5%) or positive (0%) search terms. The concerned parent who seeks information about the risks of childhood immunizations will find more websites that perpetuate vaccine myths and recommend against vaccination than the parent who seeks information about the benefits of vaccination. This suggests that search term valence can lead to online information that supports concerned parents' misconceptions about vaccines. Copyright © 2014 Elsevier Ltd. All rights reserved.

Top-down and bottom-up attention to memory: a hypothesis (AtoM) on the role of the posterior parietal cortex in memory retrieval.

PubMed

Ciaramelli, Elisa; Grady, Cheryl L; Moscovitch, Morris

2008-01-01

Recent neuroimaging studies have implicated the posterior parietal cortex in episodic memory retrieval, but there is uncertainty about its specific role. Research in the attentional domain has shown that superior parietal lobe (SPL) regions along the intraparietal sulcus are implicated in the voluntary orienting of attention to relevant aspects of the environment, whereas inferior parietal lobe (IPL) regions at the temporo-parietal junction mediate the automatic allocation of attention to task-relevant information. Here we propose that the SPL and the IPL play conceptually similar roles in episodic memory retrieval. We hypothesize that the SPL allocates top-down attention to memory retrieval, whereas the IPL mediates the automatic, bottom-up attentional capture by retrieved memory contents. By reviewing the existing fMRI literature, we show that the posterior intraparietal sulcus of SPL is consistently active when the need for top-down assistance to memory retrieval is supposedly maximal, e.g., for memories retrieved with low vs. high confidence, for familiar vs. recollected memories, for recognition of high vs. low frequency words. On the other hand, the supramarginal gyrus of IPL is consistently active when the attentional capture by memory contents is supposedly maximal, i.e., for strong vs. weak memories, for vividly recollected vs. familiar memories, for memories retrieved with high vs. low confidence. We introduce a model of episodic memory retrieval that characterizes contributions of posterior parietal cortex.

Report of the International Ozone Trends Panel 1988, volume 1

NASA Technical Reports Server (NTRS)

1989-01-01

Chapters on the following topics are presented: spacecraft instrument calibration and stability; information content of ozone retrieval algorithms; trends in total column ozone measurements; and trends in ozone profile measurement.

Teasing apart retrieval and encoding interference in the processing of anaphors

PubMed Central

Jäger, Lena A.; Benz, Lena; Roeser, Jens; Dillon, Brian W.; Vasishth, Shravan

2015-01-01

Two classes of account have been proposed to explain the memory processes subserving the processing of reflexive-antecedent dependencies. Structure-based accounts assume that the retrieval of the antecedent is guided by syntactic tree-configurational information without considering other kinds of information such as gender marking in the case of English reflexives. By contrast, unconstrained cue-based retrieval assumes that all available information is used for retrieving the antecedent. Similarity-based interference effects from structurally illicit distractors which match a non-structural retrieval cue have been interpreted as evidence favoring the unconstrained cue-based retrieval account since cue-based retrieval interference from structurally illicit distractors is incompatible with the structure-based account. However, it has been argued that the observed effects do not necessarily reflect interference occurring at the moment of retrieval but might equally well be accounted for by interference occurring already at the stage of encoding or maintaining the antecedent in memory, in which case they cannot be taken as evidence against the structure-based account. We present three experiments (self-paced reading and eye-tracking) on German reflexives and Swedish reflexive and pronominal possessives in which we pit the predictions of encoding interference and cue-based retrieval interference against each other. We could not find any indication that encoding interference affects the processing ease of the reflexive-antecedent dependency formation. Thus, there is no evidence that encoding interference might be the explanation for the interference effects observed in previous work. We therefore conclude that invoking encoding interference may not be a plausible way to reconcile interference effects with a structure-based account of reflexive processing. PMID:26106337

Information Content of Bistatic Lidar Observations of Aerosols from Space

NASA Technical Reports Server (NTRS)

Alexandrov, Mikhail D.; Mishchenko, Michael I.

2017-01-01

We present, for the first time, a quantitative retrieval error-propagation study for a bistatic high spectral resolution lidar (HSRL) system intended for detailed quasi-global monitoring of aerosol properties from space. Our results demonstrate that supplementing a conventional monostatic HSRL with an additional receiver flown in formation at a scattering angle close to 165 degrees dramatically increases the information content of the measurements and allows for a sufficiently accurate characterization of tropospheric aerosols. We conclude that a bistatic HSRL system would far exceed the capabilities of currently flown or planned orbital instruments in monitoring global aerosol effects on the environment and on the Earth's climate. We also demonstrate how the commonly used a priori 'regularization' methodology can artificially reduce the propagated uncertainties and can thereby be misleading as to the real retrieval capabilities of a measurement system.

LandEx - Fast, FOSS-Based Application for Query and Retrieval of Land Cover Patterns

NASA Astrophysics Data System (ADS)

Netzel, P.; Stepinski, T.

2012-12-01

The amount of satellite-based spatial data is continuously increasing making a development of efficient data search tools a priority. The bulk of existing research on searching satellite-gathered data concentrates on images and is based on the concept of Content-Based Image Retrieval (CBIR); however, available solutions are not efficient and robust enough to be put to use as deployable web-based search tools. Here we report on development of a practical, deployable tool that searches classified, rather than raw image. LandEx (Landscape Explorer) is a GeoWeb-based tool for Content-Based Pattern Retrieval (CBPR) contained within the National Land Cover Dataset 2006 (NLCD2006). The USGS-developed NLCD2006 is derived from Landsat multispectral images; it covers the entire conterminous U.S. with the resolution of 30 meters/pixel and it depicts 16 land cover classes. The size of NLCD2006 is about 10 Gpixels (161,000 x 100,000 pixels). LandEx is a multi-tier GeoWeb application based on Open Source Software. Main components are: GeoExt/OpenLayers (user interface), GeoServer (OGC WMS, WCS and WPS server), and GRASS (calculation engine). LandEx performs search using query-by-example approach: user selects a reference scene (exhibiting a chosen pattern of land cover classes) and the tool produces, in real time, a map indicating a degree of similarity between the reference pattern and all local patterns across the U.S. Scene pattern is encapsulated by a 2D histogram of classes and sizes of single-class clumps. Pattern similarity is based on the notion of mutual information. The resultant similarity map can be viewed and navigated in a web browser, or it can download as a GeoTiff file for more in-depth analysis. The LandEx is available at http://sil.uc.edu

Cervical cancer, human papillomavirus and vaccines: assessment of the information retrieved from general knowledge websites in Chile.

PubMed

Lopez, C S; Krauskopf, E; Villota, C E; Burzio, L O; Villegas, J E

2017-07-01

Cervical cancer is the most common gynaecologic malignancy worldwide and is the sixth cause of cancer death in Chile. Human papillomavirus (HPV) is responsible for most cervical cancers. Individuals seeking basic information about HPV frequently turn to health information websites. We hypothesized that some of their data may be inaccurate. Comparative analysis of information. We analyze the content of highly accessed websites such as the Spanish version of Wikipedia and Yahoo Answers through the application of a questionnaire, as well as a website managed by the Chilean Ministry of Health (Minsal). The accuracy of each answer was confirmed by comparison with information retrieved from articles published by indexed journals. The information provided by the Spanish version of Wikipedia was accurate; nevertheless a few omissions were detected. The quality of the information provided by the Spanish version of Yahoo Answers was inaccurate and confusing. The Minsal website lacked important information on several topics about HPV even though it is managed and endorsed by the government. We suggest periodical content reviews to increase the completeness, transparency and correctness of the website. Copyright © 2017 The Royal Society for Public Health. Published by Elsevier Ltd. All rights reserved.

Image information content and patient exposure.

PubMed

Motz, J W; Danos, M

1978-01-01

Presently, patient exposure and x-ray tube kilovoltage are determined by image visibility requirements on x-ray film. With the employment of image-processing techniques, image visibility may be manipulated and the exposure may be determined only by the desired information content, i.e., by the required degree of tissue-density descrimination and spatial resolution. This work gives quantitative relationships between the image information content and the patient exposure, give estimates of the minimum exposures required for the detection of image signals associated with particular radiological exams. Also, for subject thickness larger than approximately 5 cm, the results show that the maximum information content may be obtained at a single kilovoltage and filtration with the simultaneous employment of image-enhancement and antiscatter techniques. This optimization may be used either to reduce the patient exposure or to increase the retrieved information.

MPEG-7 audio-visual indexing test-bed for video retrieval

NASA Astrophysics Data System (ADS)

Gagnon, Langis; Foucher, Samuel; Gouaillier, Valerie; Brun, Christelle; Brousseau, Julie; Boulianne, Gilles; Osterrath, Frederic; Chapdelaine, Claude; Dutrisac, Julie; St-Onge, Francis; Champagne, Benoit; Lu, Xiaojian

2003-12-01

This paper reports on the development status of a Multimedia Asset Management (MAM) test-bed for content-based indexing and retrieval of audio-visual documents within the MPEG-7 standard. The project, called "MPEG-7 Audio-Visual Document Indexing System" (MADIS), specifically targets the indexing and retrieval of video shots and key frames from documentary film archives, based on audio-visual content like face recognition, motion activity, speech recognition and semantic clustering. The MPEG-7/XML encoding of the film database is done off-line. The description decomposition is based on a temporal decomposition into visual segments (shots), key frames and audio/speech sub-segments. The visible outcome will be a web site that allows video retrieval using a proprietary XQuery-based search engine and accessible to members at the Canadian National Film Board (NFB) Cineroute site. For example, end-user will be able to ask to point on movie shots in the database that have been produced in a specific year, that contain the face of a specific actor who tells a specific word and in which there is no motion activity. Video streaming is performed over the high bandwidth CA*net network deployed by CANARIE, a public Canadian Internet development organization.

Junior high school students' cognitive process in solving the developed algebraic problems based on information processing taxonomy model

NASA Astrophysics Data System (ADS)

Purwoko, Saad, Noor Shah; Tajudin, Nor'ain Mohd

2017-05-01

This study aims to: i) develop problem solving questions of Linear Equations System of Two Variables (LESTV) based on levels of IPT Model, ii) explain the level of students' skill of information processing in solving LESTV problems; iii) explain students' skill in information processing in solving LESTV problems; and iv) explain students' cognitive process in solving LESTV problems. This study involves three phases: i) development of LESTV problem questions based on Tessmer Model; ii) quantitative survey method on analyzing students' skill level of information processing; and iii) qualitative case study method on analyzing students' cognitive process. The population of the study was 545 eighth grade students represented by a sample of 170 students of five Junior High Schools in Hilir Barat Zone, Palembang (Indonesia) that were chosen using cluster sampling. Fifteen students among them were drawn as a sample for the interview session with saturated information obtained. The data were collected using the LESTV problem solving test and the interview protocol. The quantitative data were analyzed using descriptive statistics, while the qualitative data were analyzed using the content analysis. The finding of this study indicated that students' cognitive process was just at the step of indentifying external source and doing algorithm in short-term memory fluently. Only 15.29% students could retrieve type A information and 5.88% students could retrieve type B information from long-term memory. The implication was the development problems of LESTV had validated IPT Model in modelling students' assessment by different level of hierarchy.

Discriminative Multi-View Interactive Image Re-Ranking.

PubMed

Li, Jun; Xu, Chang; Yang, Wankou; Sun, Changyin; Tao, Dacheng

2017-07-01

Given an unreliable visual patterns and insufficient query information, content-based image retrieval is often suboptimal and requires image re-ranking using auxiliary information. In this paper, we propose a discriminative multi-view interactive image re-ranking (DMINTIR), which integrates user relevance feedback capturing users' intentions and multiple features that sufficiently describe the images. In DMINTIR, heterogeneous property features are incorporated in the multi-view learning scheme to exploit their complementarities. In addition, a discriminatively learned weight vector is obtained to reassign updated scores and target images for re-ranking. Compared with other multi-view learning techniques, our scheme not only generates a compact representation in the latent space from the redundant multi-view features but also maximally preserves the discriminative information in feature encoding by the large-margin principle. Furthermore, the generalization error bound of the proposed algorithm is theoretically analyzed and shown to be improved by the interactions between the latent space and discriminant function learning. Experimental results on two benchmark data sets demonstrate that our approach boosts baseline retrieval quality and is competitive with the other state-of-the-art re-ranking strategies.

Health Information Retrieval Tool (HIRT)

PubMed Central

Nyun, Mra Thinzar; Ogunyemi, Omolola; Zeng, Qing

2002-01-01

The World Wide Web (WWW) is a powerful way to deliver on-line health information, but one major problem limits its value to consumers: content is highly distributed, while relevant and high quality information is often difficult to find. To address this issue, we experimented with an approach that utilizes three-dimensional anatomic models in conjunction with free-text search.

GOSAT CO2 retrieval results using TANSO-CAI aerosol information over East Asia

NASA Astrophysics Data System (ADS)

KIM, M.; Kim, W.; Jung, Y.; Lee, S.; Kim, J.; Lee, H.; Boesch, H.; Goo, T. Y.

2015-12-01

In the satellite remote sensing of CO2, incorrect aerosol information could induce large errors as previous studies suggested. Many factors, such as, aerosol type, wavelength dependency of AOD, aerosol polarization effect and etc. have been main error sources. Due to these aerosol effects, large number of data retrieved are screened out in quality control, or retrieval errors tend to increase if not screened out, especially in East Asia where aerosol concentrations are fairly high. To reduce these aerosol induced errors, a CO2 retrieval algorithm using the simultaneous TANSO-CAI aerosol information is developed. This algorithm adopts AOD and aerosol type information as a priori information from the CAI aerosol retrieval algorithm. The CO2 retrieval algorithm based on optimal estimation method and VLIDORT, a vector discrete ordinate radiative transfer model. The CO2 algorithm, developed with various state vectors to find accurate CO2 concentration, shows reasonable results when compared with other dataset. This study concentrates on the validation of retrieved results with the ground-based TCCON measurements in East Asia and the comparison with the previous retrieval from ACOS, NIES, and UoL. Although, the retrieved CO2 concentration is lower than previous results by ppm's, it shows similar trend and high correlation with previous results. Retrieved data and TCCON measurements data are compared at three stations of Tsukuba, Saga, Anmyeondo in East Asia, with the collocation criteria of ±2°in latitude/longitude and ±1 hours of GOSAT passing time. Compared results also show similar trend with good correlation. Based on the TCCON comparison results, bias correction equation is calculated and applied to the East Asia data.

Multiple-Feature Extracting Modules Based Leak Mining System Design

PubMed Central

Cho, Ying-Chiang; Pan, Jen-Yi

2013-01-01

Over the years, human dependence on the Internet has increased dramatically. A large amount of information is placed on the Internet and retrieved from it daily, which makes web security in terms of online information a major concern. In recent years, the most problematic issues in web security have been e-mail address leakage and SQL injection attacks. There are many possible causes of information leakage, such as inadequate precautions during the programming process, which lead to the leakage of e-mail addresses entered online or insufficient protection of database information, a loophole that enables malicious users to steal online content. In this paper, we implement a crawler mining system that is equipped with SQL injection vulnerability detection, by means of an algorithm developed for the web crawler. In addition, we analyze portal sites of the governments of various countries or regions in order to investigate the information leaking status of each site. Subsequently, we analyze the database structure and content of each site, using the data collected. Thus, we make use of practical verification in order to focus on information security and privacy through black-box testing. PMID:24453892

Multiple-feature extracting modules based leak mining system design.

PubMed

Cho, Ying-Chiang; Pan, Jen-Yi

2013-01-01

Over the years, human dependence on the Internet has increased dramatically. A large amount of information is placed on the Internet and retrieved from it daily, which makes web security in terms of online information a major concern. In recent years, the most problematic issues in web security have been e-mail address leakage and SQL injection attacks. There are many possible causes of information leakage, such as inadequate precautions during the programming process, which lead to the leakage of e-mail addresses entered online or insufficient protection of database information, a loophole that enables malicious users to steal online content. In this paper, we implement a crawler mining system that is equipped with SQL injection vulnerability detection, by means of an algorithm developed for the web crawler. In addition, we analyze portal sites of the governments of various countries or regions in order to investigate the information leaking status of each site. Subsequently, we analyze the database structure and content of each site, using the data collected. Thus, we make use of practical verification in order to focus on information security and privacy through black-box testing.

Term-Weighting Approaches in Automatic Text Retrieval.

ERIC Educational Resources Information Center

Salton, Gerard; Buckley, Christopher

1988-01-01

Summarizes the experimental evidence that indicates that text indexing systems based on the assignment of appropriately weighted single terms produce retrieval results superior to those obtained with more elaborate text representations, and provides baseline single term indexing models with which more elaborate content analysis procedures can be…

Video and image retrieval beyond the cognitive level: the needs and possibilities

NASA Astrophysics Data System (ADS)

Hanjalic, Alan

2000-12-01

The worldwide research efforts in the are of image and video retrieval have concentrated so far on increasing the efficiency and reliability of extracting the elements of image and video semantics and so on improving the search and retrieval performance at the cognitive level of content abstraction. At this abstraction level, the user is searching for 'factual' or 'objective' content such as image showing a panorama of San Francisco, an outdoor or an indoor image, a broadcast news report on a defined topic, a movie dialog between the actors A and B or the parts of a basketball game showing fast breaks, steals and scores. These efforts, however, do not address the retrieval applications at the so-called affective level of content abstraction where the 'ground truth' is not strictly defined. Such applications are, for instance, those where subjectivity of the user plays the major role, e.g. the task of retrieving all images that the user 'likes most', and those that are based on 'recognizing emotions' in audiovisual data. Typical examples are searching for all images that 'radiate happiness', identifying all 'sad' movie fragments and looking for the 'romantic landscapes', 'sentimental' movie segments, 'movie highlights' or 'most exciting' moments of a sport event. This paper discusses the needs and possibilities for widening the current scope of research in the area of image and video search and retrieval in order to enable applications at the affective level of content abstraction.

Video and image retrieval beyond the cognitive level: the needs and possibilities

NASA Astrophysics Data System (ADS)

Hanjalic, Alan

2001-01-01

The worldwide research efforts in the are of image and video retrieval have concentrated so far on increasing the efficiency and reliability of extracting the elements of image and video semantics and so on improving the search and retrieval performance at the cognitive level of content abstraction. At this abstraction level, the user is searching for 'factual' or 'objective' content such as image showing a panorama of San Francisco, an outdoor or an indoor image, a broadcast news report on a defined topic, a movie dialog between the actors A and B or the parts of a basketball game showing fast breaks, steals and scores. These efforts, however, do not address the retrieval applications at the so-called affective level of content abstraction where the 'ground truth' is not strictly defined. Such applications are, for instance, those where subjectivity of the user plays the major role, e.g. the task of retrieving all images that the user 'likes most', and those that are based on 'recognizing emotions' in audiovisual data. Typical examples are searching for all images that 'radiate happiness', identifying all 'sad' movie fragments and looking for the 'romantic landscapes', 'sentimental' movie segments, 'movie highlights' or 'most exciting' moments of a sport event. This paper discusses the needs and possibilities for widening the current scope of research in the area of image and video search and retrieval in order to enable applications at the affective level of content abstraction.

The effect of cloud screening on MAX-DOAS aerosol retrievals.

NASA Astrophysics Data System (ADS)

Gielen, Clio; Van Roozendael, Michel; Hendrik, Francois; Fayt, Caroline; Hermans, Christian; Pinardi, Gaia; De Backer, Hugo; De Bock, Veerle; Laffineur, Quentin; Vlemmix, Tim

2014-05-01

In recent years, ground-based multi-axis differential absorption spectroscopy (MAX-DOAS) has shown to be ideally suited for the retrieval of tropospheric trace gases and deriving information on the aerosol properties. These measurements are invaluable to our understanding of the physics and chemistry of the atmospheric system, and the impact on the Earth's climate. Unfortunately, MAX-DOAS measurements are often performed under strong non-clear-sky conditions, causing strong data quality degradation and uncertainties on the retrievals. Here we present the result of our cloud-screening method, using the colour index (CI), on aerosol retrievals from MAX-DOAS measurements (AOD and vertical profiles). We focus on two large data sets, from the Brussels and Beijing area. Using the CI we define 3 different sky conditions: bad (=full thick cloud cover/extreme aerosols), mediocre (=thin clouds/aerosols) and good (=clear sky). We also flag the presence of broken/scattered clouds. We further compare our cloud-screening method with results from cloud-cover fractions derived from thermic infrared measurements. In general, our method shows good results to qualify the sky and cloud conditions of MAX-DOAS measurements, without the need for other external cloud-detection systems. Removing data under bad-sky and broken-cloud conditions results in a strongly improved agreement, in both correlation and slope, between the MAX-DOAS aerosol retrievals and data from other instruments (e.g. AERONET, Brewer). With the improved AOD retrievals, the seasonal and diurnal variations of the aerosol content and vertical distribution at both sites can be investigated in further detail. By combining with additional information derived by other instruments (Brewer, lidar, ...) operated at the stations, we will further study the observed aerosol characteristics, and their influence on and by meteorological conditions such as clouds and/or the boundary layer height.

A multi-tiered architecture for content retrieval in mobile peer-to-peer networks.

DOT National Transportation Integrated Search

2012-01-01

In this paper, we address content retrieval in Mobile Peer-to-Peer (P2P) Networks. We design a multi-tiered architecture for content : retrieval, where at Tier 1, we design a protocol for content similarity governed by a parameter that trades accu...

Structuring Legacy Pathology Reports by openEHR Archetypes to Enable Semantic Querying.

PubMed

Kropf, Stefan; Krücken, Peter; Mueller, Wolf; Denecke, Kerstin

2017-05-18

Clinical information is often stored as free text, e.g. in discharge summaries or pathology reports. These documents are semi-structured using section headers, numbered lists, items and classification strings. However, it is still challenging to retrieve relevant documents since keyword searches applied on complete unstructured documents result in many false positive retrieval results. We are concentrating on the processing of pathology reports as an example for unstructured clinical documents. The objective is to transform reports semi-automatically into an information structure that enables an improved access and retrieval of relevant data. The data is expected to be stored in a standardized, structured way to make it accessible for queries that are applied to specific sections of a document (section-sensitive queries) and for information reuse. Our processing pipeline comprises information modelling, section boundary detection and section-sensitive queries. For enabling a focused search in unstructured data, documents are automatically structured and transformed into a patient information model specified through openEHR archetypes. The resulting XML-based pathology electronic health records (PEHRs) are queried by XQuery and visualized by XSLT in HTML. Pathology reports (PRs) can be reliably structured into sections by a keyword-based approach. The information modelling using openEHR allows saving time in the modelling process since many archetypes can be reused. The resulting standardized, structured PEHRs allow accessing relevant data by retrieving data matching user queries. Mapping unstructured reports into a standardized information model is a practical solution for a better access to data. Archetype-based XML enables section-sensitive retrieval and visualisation by well-established XML techniques. Focussing the retrieval to particular sections has the potential of saving retrieval time and improving the accuracy of the retrieval.

Information content of ozone retrieval algorithms

NASA Technical Reports Server (NTRS)

Rodgers, C.; Bhartia, P. K.; Chu, W. P.; Curran, R.; Deluisi, J.; Gille, J. C.; Hudson, R.; Mateer, C.; Rusch, D.; Thomas, R. J.

1989-01-01

The algorithms are characterized that were used for production processing by the major suppliers of ozone data to show quantitatively: how the retrieved profile is related to the actual profile (This characterizes the altitude range and vertical resolution of the data); the nature of systematic errors in the retrieved profiles, including their vertical structure and relation to uncertain instrumental parameters; how trends in the real ozone are reflected in trends in the retrieved ozone profile; and how trends in other quantities (both instrumental and atmospheric) might appear as trends in the ozone profile. No serious deficiencies were found in the algorithms used in generating the major available ozone data sets. As the measurements are all indirect in someway, and the retrieved profiles have different characteristics, data from different instruments are not directly comparable.

Cognitive Process as a Basis for Intelligent Retrieval Systems Design.

ERIC Educational Resources Information Center

Chen, Hsinchun; Dhar, Vasant

1991-01-01

Two studies of the cognitive processes involved in online document-based information retrieval were conducted. These studies led to the development of five computational models of online document retrieval which were incorporated into the design of an "intelligent" document-based retrieval system. Both the system and the broader implications of…

Use of ebRIM-based CSW with sensor observation services for registry and discovery of remote-sensing observations

NASA Astrophysics Data System (ADS)

Chen, Nengcheng; Di, Liping; Yu, Genong; Gong, Jianya; Wei, Yaxing

2009-02-01

Recent advances in Sensor Web geospatial data capture, such as high-resolution in satellite imagery and Web-ready data processing and modeling technologies, have led to the generation of large numbers of datasets from real-time or near real-time observations and measurements. Finding which sensor or data complies with criteria such as specific times, locations, and scales has become a bottleneck for Sensor Web-based applications, especially remote-sensing observations. In this paper, an architecture for use of the integration Sensor Observation Service (SOS) with the Open Geospatial Consortium (OGC) Catalogue Service-Web profile (CSW) is put forward. The architecture consists of a distributed geospatial sensor observation service, a geospatial catalogue service based on the ebXML Registry Information Model (ebRIM), SOS search and registry middleware, and a geospatial sensor portal. The SOS search and registry middleware finds the potential SOS, generating data granule information and inserting the records into CSW. The contents and sequence of the services, the available observations, and the metadata of the observations registry are described. A prototype system is designed and implemented using the service middleware technology and a standard interface and protocol. The feasibility and the response time of registry and retrieval of observations are evaluated using a realistic Earth Observing-1 (EO-1) SOS scenario. Extracting information from SOS requires the same execution time as record generation for CSW. The average data retrieval response time in SOS+CSW mode is 17.6% of that of the SOS-alone mode. The proposed architecture has the more advantages of SOS search and observation data retrieval than the existing sensor Web enabled systems.

Hepatic CT image query using Gabor features

NASA Astrophysics Data System (ADS)

Zhao, Chenguang; Cheng, Hongyan; Zhuang, Tiange

2004-07-01

A retrieval scheme for liver computerize tomography (CT) images based on Gabor texture is presented. For each hepatic CT image, we manually delineate abnormal regions within liver area. Then, a continuous Gabor transform is utilized to analyze the texture of the pathology bearing region and extract the corresponding feature vectors. For a given sample image, we compare its feature vector with those of other images. Similar images with the highest rank are retrieved. In experiments, 45 liver CT images are collected, and the effectiveness of Gabor texture for content based retrieval is verified.

Combination of image descriptors for the exploration of cultural photographic collections

NASA Astrophysics Data System (ADS)

Bhowmik, Neelanjan; Gouet-Brunet, Valérie; Bloch, Gabriel; Besson, Sylvain

2017-01-01

The rapid growth of image digitization and collections in recent years makes it challenging and burdensome to organize, categorize, and retrieve similar images from voluminous collections. Content-based image retrieval (CBIR) is immensely convenient in this context. A considerable number of local feature detectors and descriptors are present in the literature of CBIR. We propose a model to anticipate the best feature combinations for image retrieval-related applications. Several spatial complementarity criteria of local feature detectors are analyzed and then engaged in a regression framework to find the optimal combination of detectors for a given dataset and are better adapted for each given image; the proposed model is also useful to optimally fix some other parameters, such as the k in k-nearest neighbor retrieval. Three public datasets of various contents and sizes are employed to evaluate the proposal, which is legitimized by improving the quality of retrieval notably facing classical approaches. Finally, the proposed image search engine is applied to the cultural photographic collections of a French museum, where it demonstrates its added value for the exploration and promotion of these contents at different levels from their archiving up to their exhibition in or ex situ.

Corrugated Waveguide Mode Content Analysis Using Irradiance Moments

PubMed Central

Jawla, Sudheer K.; Shapiro, Michael A.; Idei, Hiroshi; Temkin, Richard J.

2015-01-01

We present a novel, relatively simple method for determining the mode content of the linearly polarized modes of a corrugated waveguide using the moments of the intensity pattern of the field radiated from the end of the waveguide. This irradiance moment method is based on calculating the low-order irradiance moments, using measured intensity profiles only, of the radiated field from the waveguide aperture. Unlike the phase retrieval method, this method does not use or determine the phase distribution at the waveguide aperture. The new method was benchmarked numerically by comparison with sample mode mixtures. The results predict less than ±0.7% error bar in the retrieval of the mode content. The method was also tested using high-resolution experimental data from beams radiated from 63.5 mm and 19 mm corrugated waveguides at 170 and 250 GHz, respectively. The results showed a very good agreement of the mode content retrieved using the irradiance moment method versus the phase retrieval technique. The irradiance moment method is most suitable for cases where the modal power is primarily in the fundamental HE11 mode, with <8% of the power in high-order modes. PMID:25821260

Creating a classification of image types in the medical literature for visual categorization

NASA Astrophysics Data System (ADS)

Müller, Henning; Kalpathy-Cramer, Jayashree; Demner-Fushman, Dina; Antani, Sameer

2012-02-01

Content-based image retrieval (CBIR) from specialized collections has often been proposed for use in such areas as diagnostic aid, clinical decision support, and teaching. The visual retrieval from broad image collections such as teaching files, the medical literature or web images, by contrast, has not yet reached a high maturity level compared to textual information retrieval. Visual image classification into a relatively small number of classes (20-100) on the other hand, has shown to deliver good results in several benchmarks. It is, however, currently underused as a basic technology for retrieval tasks, for example, to limit the search space. Most classification schemes for medical images are focused on specific areas and consider mainly the medical image types (modalities), imaged anatomy, and view, and merge them into a single descriptor or classification hierarchy. Furthermore, they often ignore other important image types such as biological images, statistical figures, flowcharts, and diagrams that frequently occur in the biomedical literature. Most of the current classifications have also been created for radiology images, which are not the only types to be taken into account. With Open Access becoming increasingly widespread particularly in medicine, images from the biomedical literature are more easily available for use. Visual information from these images and knowledge that an image is of a specific type or medical modality could enrich retrieval. This enrichment is hampered by the lack of a commonly agreed image classification scheme. This paper presents a hierarchy for classification of biomedical illustrations with the goal of using it for visual classification and thus as a basis for retrieval. The proposed hierarchy is based on relevant parts of existing terminologies, such as the IRMA-code (Image Retrieval in Medical Applications), ad hoc classifications and hierarchies used in imageCLEF (Image retrieval task at the Cross-Language Evaluation Forum) and NLM's (National Library of Medicine) OpenI. Furtheron, mappings to NLM's MeSH (Medical Subject Headings), RSNA's RadLex (Radiological Society of North America, Radiology Lexicon), and the IRMA code are also attempted for relevant image types. Advantages derived from such hierarchical classification for medical image retrieval are being evaluated through benchmarks such as imageCLEF, and R&D systems such as NLM's OpenI. The goal is to extend this hierarchy progressively and (through adding image types occurring in the biomedical literature) to have a terminology for visual image classification based on image types distinguishable by visual means and occurring in the medical open access literature.

Improve Biomedical Information Retrieval using Modified Learning to Rank Methods.

PubMed

Xu, Bo; Lin, Hongfei; Lin, Yuan; Ma, Yunlong; Yang, Liang; Wang, Jian; Yang, Zhihao

2016-06-14

In these years, the number of biomedical articles has increased exponentially, which becomes a problem for biologists to capture all the needed information manually. Information retrieval technologies, as the core of search engines, can deal with the problem automatically, providing users with the needed information. However, it is a great challenge to apply these technologies directly for biomedical retrieval, because of the abundance of domain specific terminologies. To enhance biomedical retrieval, we propose a novel framework based on learning to rank. Learning to rank is a series of state-of-the-art information retrieval techniques, and has been proved effective in many information retrieval tasks. In the proposed framework, we attempt to tackle the problem of the abundance of terminologies by constructing ranking models, which focus on not only retrieving the most relevant documents, but also diversifying the searching results to increase the completeness of the resulting list for a given query. In the model training, we propose two novel document labeling strategies, and combine several traditional retrieval models as learning features. Besides, we also investigate the usefulness of different learning to rank approaches in our framework. Experimental results on TREC Genomics datasets demonstrate the effectiveness of our framework for biomedical information retrieval.

Content Management Middleware for the Support of Distributed Teaching

ERIC Educational Resources Information Center

Tsalapatas, Hariklia; Stav, John B.; Kalantzis, Christos

2004-01-01

eCMS is a web-based federated content management system for the support of distributed teaching based on an open, distributed middleware architecture for the publication, discovery, retrieval, and integration of educational material. The infrastructure supports the management of both standalone material and structured courses, as well as the…

Medial temporal lobe contributions to cued retrieval of items and contexts.

PubMed

Hannula, Deborah E; Libby, Laura A; Yonelinas, Andrew P; Ranganath, Charan

2013-10-01

Several models have proposed that different regions of the medial temporal lobes contribute to different aspects of episodic memory. For instance, according to one view, the perirhinal cortex represents specific items, parahippocampal cortex represents information regarding the context in which these items were encountered, and the hippocampus represents item-context bindings. Here, we used event-related functional magnetic resonance imaging (fMRI) to test a specific prediction of this model-namely, that successful retrieval of items from context cues will elicit perirhinal recruitment and that successful retrieval of contexts from item cues will elicit parahippocampal cortex recruitment. Retrieval of the bound representation in either case was expected to elicit hippocampal engagement. To test these predictions, we had participants study several item-context pairs (i.e., pictures of objects and scenes, respectively), and then had them attempt to recall items from associated context cues and contexts from associated item cues during a scanned retrieval session. Results based on both univariate and multivariate analyses confirmed a role for hippocampus in content-general relational memory retrieval, and a role for parahippocampal cortex in successful retrieval of contexts from item cues. However, we also found that activity differences in perirhinal cortex were correlated with successful cued recall for both items and contexts. These findings provide partial support for the above predictions and are discussed with respect to several models of medial temporal lobe function. Copyright © 2013 Elsevier Ltd. All rights reserved.

A Knowledge-Based Approach to Retrieving Teaching Materials for Context-Aware Learning

ERIC Educational Resources Information Center

Shih, Wen-Chung; Tseng, Shian-Shyong

2009-01-01

With the rapid development of wireless communication and sensor technologies, ubiquitous learning has become a promising solution to educational problems. In context-aware ubiquitous learning environments, it is required that learning content is retrieved according to environmental contexts, such as learners' location. Also, a learning content…

Facilitating medical information search using Google Glass connected to a content-based medical image retrieval system.

PubMed

Widmer, Antoine; Schaer, Roger; Markonis, Dimitrios; Muller, Henning

2014-01-01

Wearable computing devices are starting to change the way users interact with computers and the Internet. Among them, Google Glass includes a small screen located in front of the right eye, a camera filming in front of the user and a small computing unit. Google Glass has the advantage to provide online services while allowing the user to perform tasks with his/her hands. These augmented glasses uncover many useful applications, also in the medical domain. For example, Google Glass can easily provide video conference between medical doctors to discuss a live case. Using these glasses can also facilitate medical information search by allowing the access of a large amount of annotated medical cases during a consultation in a non-disruptive fashion for medical staff. In this paper, we developed a Google Glass application able to take a photo and send it to a medical image retrieval system along with keywords in order to retrieve similar cases. As a preliminary assessment of the usability of the application, we tested the application under three conditions (images of the skin; printed CT scans and MRI images; and CT and MRI images acquired directly from an LCD screen) to explore whether using Google Glass affects the accuracy of the results returned by the medical image retrieval system. The preliminary results show that despite minor problems due to the relative stability of the Google Glass, images can be sent to and processed by the medical image retrieval system and similar images are returned to the user, potentially helping in the decision making process.

Content-based analysis of news video

NASA Astrophysics Data System (ADS)

Yu, Junqing; Zhou, Dongru; Liu, Huayong; Cai, Bo

2001-09-01

In this paper, we present a schema for content-based analysis of broadcast news video. First, we separate commercials from news using audiovisual features. Then, we automatically organize news programs into a content hierarchy at various levels of abstraction via effective integration of video, audio, and text data available from the news programs. Based on these news video structure and content analysis technologies, a TV news video Library is generated, from which users can retrieve definite news story according to their demands.

Blurry-frame detection and shot segmentation in colonoscopy videos

NASA Astrophysics Data System (ADS)

Oh, JungHwan; Hwang, Sae; Tavanapong, Wallapak; de Groen, Piet C.; Wong, Johnny

2003-12-01

Colonoscopy is an important screening procedure for colorectal cancer. During this procedure, the endoscopist visually inspects the colon. Human inspection, however, is not without error. We hypothesize that colonoscopy videos may contain additional valuable information missed by the endoscopist. Video segmentation is the first necessary step for the content-based video analysis and retrieval to provide efficient access to the important images and video segments from a large colonoscopy video database. Based on the unique characteristics of colonoscopy videos, we introduce a new scheme to detect and remove blurry frames, and segment the videos into shots based on the contents. Our experimental results show that the average precision and recall of the proposed scheme are over 90% for the detection of non-blurry images. The proposed method of blurry frame detection and shot segmentation is extensible to the videos captured from other endoscopic procedures such as upper gastrointestinal endoscopy, enteroscopy, cystoscopy, and laparoscopy.

Available web-based teaching resources for health care professionals on screening for oral cancer

PubMed Central

Varela-Centelles, Pablo; Insua, Angel; Warnakulasuriya, Saman; Rapidis, Alexander; Diz, Pedro; Seoane, Juan

2015-01-01

Objectives: To identify websites with adequate information on oral cancer screening for healthcare professionals (HCPs) and to assess both their quality and contents. Study Design: Websites were identified using Google and HON medical professional search engines using the terms “screening for oral cancer”. The first 100 sites retrieved by each engine were analysed using the DISCERN questionnaire (reliability), the V instrument (contents on oral cancer) and further by the Flesch-Kinkaid Reading Grade Level and the Flesch Reading Ease (readability). Results: The overall rating showed minimal shortcomings in the quality of the information in the websites. The coverage and correctness of information on “visual examination” was rated as fair/good, whereas updating of contents resulted very variable (eg: 81% for visual examination and 18.2% for molecular biomarkers). These results permitted to rank the websites housing relevant information for oral cancer. Top ranking websites were affiliated to the Oral Cancer Foundation (USA), WHO Collaborating Centre for oral cancer (UK) whose webpage is entitled “Oral Cancer Education and Research”, and the Clinical Guidelines maintained by the British Columbia Cancer Agency (Canada) and the British Dental Association (UK) respectively. Conclusions: There are web-based, HCP-addressed, resources on screening for oral cancer housing heterogeneous information both in quality and contents. The use of specific evaluation tools permits the selection of reliable websites on this topic with a potential to improve the existing educational gaps among HCPs. Key words:Oral cancer, early diagnosis, screening, secondary prevention, internet, teaching resources, continuous education. PMID:25475775

A comparison of Boolean-based retrieval to the WAIS system for retrieval of aeronautical information

NASA Technical Reports Server (NTRS)

Marchionini, Gary; Barlow, Diane

1994-01-01

An evaluation of an information retrieval system using a Boolean-based retrieval engine and inverted file architecture and WAIS, which uses a vector-based engine, was conducted. Four research questions in aeronautical engineering were used to retrieve sets of citations from the NASA Aerospace Database which was mounted on a WAIS server and available through Dialog File 108 which served as the Boolean-based system (BBS). High recall and high precision searches were done in the BBS and terse and verbose queries were used in the WAIS condition. Precision values for the WAIS searches were consistently above the precision values for high recall BBS searches and consistently below the precision values for high precision BBS searches. Terse WAIS queries gave somewhat better precision performance than verbose WAIS queries. In every case, a small number of relevant documents retrieved by one system were not retrieved by the other, indicating the incomplete nature of the results from either retrieval system. Relevant documents in the WAIS searches were found to be randomly distributed in the retrieved sets rather than distributed by ranks. Advantages and limitations of both types of systems are discussed.

Exoplanets with JWST: degeneracy, systematics and how to avoid them

NASA Astrophysics Data System (ADS)

Barstow, Joanna K.; Irwin, Patrick G. J.; Kendrew, Sarah; Aigrain, Suzanne

2016-07-01

The high sensitivity and broad wavelength coverage of the James Webb Space Telescope will transform the field of exoplanet transit spectroscopy. Transit spectra are inferred from minute, wavelength-dependent variations in the depth of a transit or eclipse as the planet passes in front of or is obscured by its star, and the spectra contain information about the composition, structure and cloudiness of exoplanet atmospheres. Atmospheric retrieval is the preferred technique for extracting information from these spectra, but the process can be confused by astrophysical and instrumental systematic noise. We present results of retrieval tests based on synthetic, noisy JWST spectra, for clear and cloudy planets and active and inactive stars. We find that the ability to correct for stellar activity is likely to be a limiting factor for cloudy planets, as the effects of unocculted star spots may mimic the presence of a scattering slope due to clouds. We discuss the pros and cons of the available JWST instrument combinations for transit spectroscopy, and consider the effect of clouds and aerosols on the spectra. Aerosol high in a planet's atmosphere obscures molecular absorption features in transmission, reducing the information content of spectra in wavelength regions where the cloud is optically thick. We discuss the usefulness of particular wavelength regions for identifying the presence of cloud, and suggest strategies for solving the highly-degenerate retrieval problem for these objects.

What do patients know about their low back pain? An analysis of the quality of information available on the Internet.

PubMed

Galbusera, Fabio; Brayda-Bruno, Marco; Freutel, Maren; Seitz, Andreas; Steiner, Malte; Wehrle, Esther; Wilke, Hans-Joachim

2012-01-01

Previous surveys showed a poor quality of the web sites providing health information about low back pain. However, the rapid and continuous evolution of the Internet content may question the current validity of those investigations. The present study is aimed to quantitatively assess the quality of the Internet information about low back pain retrieved with the most commonly employed search engines. An Internet search with the keywords "low back pain" has been performed with Google, Yahoo!® and Bing™ in the English language. The top 30 hits obtained with each search engine were evaluated by five independent raters and averaged following criteria derived from previous works. All search results were categorized as declaring compliant to a quality standard for health information (e.g. HONCode) or not and based on the web site type (Institutional, Free informative, Commercial, News, Social Network, Unknown). The quality of the hits retrieved by the three search engines was extremely similar. The web sites had a clear purpose, were easy to navigate, and mostly lacked in validity and quality of the provided links. The conformity to a quality standard was correlated with a marked greater quality of the web sites in all respects. Institutional web sites had the best validity and ease of use. Free informative web sites had good quality but a markedly lower validity compared to Institutional websites. Commercial web sites provided more biased information. News web sites were well designed and easy to use, but lacked in validity. The average quality of the hits retrieved by the most commonly employed search engines could be defined as satisfactory and favorably comparable with previous investigations. Awareness of the user about checking the quality of the information remains of concern.

Passage-Based Bibliographic Coupling: An Inter-Article Similarity Measure for Biomedical Articles

PubMed Central

Liu, Rey-Long

2015-01-01

Biomedical literature is an essential source of biomedical evidence. To translate the evidence for biomedicine study, researchers often need to carefully read multiple articles about specific biomedical issues. These articles thus need to be highly related to each other. They should share similar core contents, including research goals, methods, and findings. However, given an article r, it is challenging for search engines to retrieve highly related articles for r. In this paper, we present a technique PBC (Passage-based Bibliographic Coupling) that estimates inter-article similarity by seamlessly integrating bibliographic coupling with the information collected from context passages around important out-link citations (references) in each article. Empirical evaluation shows that PBC can significantly improve the retrieval of those articles that biomedical experts believe to be highly related to specific articles about gene-disease associations. PBC can thus be used to improve search engines in retrieving the highly related articles for any given article r, even when r is cited by very few (or even no) articles. The contribution is essential for those researchers and text mining systems that aim at cross-validating the evidence about specific gene-disease associations. PMID:26440794

Annotating image ROIs with text descriptions for multimodal biomedical document retrieval

NASA Astrophysics Data System (ADS)

You, Daekeun; Simpson, Matthew; Antani, Sameer; Demner-Fushman, Dina; Thoma, George R.

2013-01-01

Regions of interest (ROIs) that are pointed to by overlaid markers (arrows, asterisks, etc.) in biomedical images are expected to contain more important and relevant information than other regions for biomedical article indexing and retrieval. We have developed several algorithms that localize and extract the ROIs by recognizing markers on images. Cropped ROIs then need to be annotated with contents describing them best. In most cases accurate textual descriptions of the ROIs can be found from figure captions, and these need to be combined with image ROIs for annotation. The annotated ROIs can then be used to, for example, train classifiers that separate ROIs into known categories (medical concepts), or to build visual ontologies, for indexing and retrieval of biomedical articles. We propose an algorithm that pairs visual and textual ROIs that are extracted from images and figure captions, respectively. This algorithm based on dynamic time warping (DTW) clusters recognized pointers into groups, each of which contains pointers with identical visual properties (shape, size, color, etc.). Then a rule-based matching algorithm finds the best matching group for each textual ROI mention. Our method yields a precision and recall of 96% and 79%, respectively, when ground truth textual ROI data is used.

Passage-Based Bibliographic Coupling: An Inter-Article Similarity Measure for Biomedical Articles.

PubMed

Liu, Rey-Long

2015-01-01

Biomedical literature is an essential source of biomedical evidence. To translate the evidence for biomedicine study, researchers often need to carefully read multiple articles about specific biomedical issues. These articles thus need to be highly related to each other. They should share similar core contents, including research goals, methods, and findings. However, given an article r, it is challenging for search engines to retrieve highly related articles for r. In this paper, we present a technique PBC (Passage-based Bibliographic Coupling) that estimates inter-article similarity by seamlessly integrating bibliographic coupling with the information collected from context passages around important out-link citations (references) in each article. Empirical evaluation shows that PBC can significantly improve the retrieval of those articles that biomedical experts believe to be highly related to specific articles about gene-disease associations. PBC can thus be used to improve search engines in retrieving the highly related articles for any given article r, even when r is cited by very few (or even no) articles. The contribution is essential for those researchers and text mining systems that aim at cross-validating the evidence about specific gene-disease associations.

The potential of the internet.

PubMed

Coleman, Jamie J; McDowell, Sarah E

2012-06-01

The internet and the World Wide Web have changed the ways that we function. As technologies grow and adapt, there is a huge potential for the internet to affect drug research and development, as well as many other aspects of clinical pharmacology. We review some of the areas of interest to date and discuss some of the potential areas in which internet-based technology can be exploited. Information retrieval from the web by health-care professionals is common, and bringing evidence-based medicine to the bedside affects the care of patients. As a primary research tool the web can provide a vast array of information in generating new ideas or exploring previous research findings. This has facilitated systematic reviewing, for example. The content of the web has become a subject of research in its own right. The web is also widely used as a research facilitator, including enhancement of communication between collaborators, provision of online research tools (such as questionnaires, management of large scale multicentre trials, registration of clinical trials) and distribution of information. Problems include information overload, ignorance of early data that are not indexed in databases, difficulties in keeping web sites up to date and assessing the validity of information retrieved. Some web-based activities are viewed with suspicion, including analysis by pharmaceutical companies of drug information to facilitate direct-to-consumer advertising of novel pharmaceuticals. Use of these technologies will continue to expand in often unexpected ways. Clinical pharmacologists must embrace internet technology and include it as a key priority in their research agenda. © 2012 The Authors. British Journal of Clinical Pharmacology © 2012 The British Pharmacological Society.

Enhancements in medicine by integrating content based image retrieval in computer-aided diagnosis

NASA Astrophysics Data System (ADS)

Aggarwal, Preeti; Sardana, H. K.

2010-02-01

Computer-aided diagnosis (CAD) has become one of the major research subjects in medical imaging and diagnostic radiology. With cad, radiologists use the computer output as a "second opinion" and make the final decisions. Retrieving images is a useful tool to help radiologist to check medical image and diagnosis. The impact of contentbased access to medical images is frequently reported but existing systems are designed for only a particular context of diagnosis. The challenge in medical informatics is to develop tools for analyzing the content of medical images and to represent them in a way that can be efficiently searched and compared by the physicians. CAD is a concept established by taking into account equally the roles of physicians and computers. To build a successful computer aided diagnostic system, all the relevant technologies, especially retrieval need to be integrated in such a manner that should provide effective and efficient pre-diagnosed cases with proven pathology for the current case at the right time. In this paper, it is suggested that integration of content-based image retrieval (CBIR) in cad can bring enormous results in medicine especially in diagnosis. This approach is also compared with other approaches by highlighting its advantages over those approaches.

Physical retrieval of precipitation water contents from Special Sensor Microwave/Imager (SSM/I) data. Part 1: A cloud ensemble/radiative parameterization for sensor response (report version)

NASA Technical Reports Server (NTRS)

Olson, William S.; Raymond, William H.

1990-01-01

The physical retrieval of geophysical parameters based upon remotely sensed data requires a sensor response model which relates the upwelling radiances that the sensor observes to the parameters to be retrieved. In the retrieval of precipitation water contents from satellite passive microwave observations, the sensor response model has two basic components. First, a description of the radiative transfer of microwaves through a precipitating atmosphere must be considered, because it is necessary to establish the physical relationship between precipitation water content and upwelling microwave brightness temperature. Also the spatial response of the satellite microwave sensor (or antenna pattern) must be included in the description of sensor response, since precipitation and the associated brightness temperature field can vary over a typical microwave sensor resolution footprint. A 'population' of convective cells, as well as stratiform clouds, are simulated using a computationally-efficient multi-cylinder cloud model. Ensembles of clouds selected at random from the population, distributed over a 25 km x 25 km model domain, serve as the basis for radiative transfer calculations of upwelling brightness temperatures at the SSM/I frequencies. Sensor spatial response is treated explicitly by convolving the upwelling brightness temperature by the domain-integrated SSM/I antenna patterns. The sensor response model is utilized in precipitation water content retrievals.

Set-relevance determines the impact of distractors on episodic memory retrieval.

PubMed

Kwok, Sze Chai; Shallice, Tim; Macaluso, Emiliano

2014-09-01

We investigated the interplay between stimulus-driven attention and memory retrieval with a novel interference paradigm that engaged both systems concurrently on each trial. Participants encoded a 45-min movie on Day 1 and, on Day 2, performed a temporal order judgment task during fMRI. Each retrieval trial comprised three images presented sequentially, and the task required participants to judge the temporal order of the first and the last images ("memory probes") while ignoring the second image, which was task irrelevant ("attention distractor"). We manipulated the content relatedness and the temporal proximity between the distractor and the memory probes, as well as the temporal distance between two probes. Behaviorally, short temporal distances between the probes led to reduced retrieval performance. Distractors that at encoding were temporally close to the first probe image reduced these costs, specifically when the distractor was content unrelated to the memory probes. The imaging results associated the distractor probe temporal proximity with activation of the right ventral attention network. By contrast, the precuneus was activated for high-content relatedness between distractors and probes and in trials including a short distance between the two memory probes. The engagement of the right ventral attention network by specific types of distractors suggests a link between stimulus-driven attention control and episodic memory retrieval, whereas the activation pattern of the precuneus implicates this region in memory search within knowledge/content-based hierarchies.

[A retrieval method of drug molecules based on graph collapsing].

PubMed

Qu, J W; Lv, X Q; Liu, Z M; Liao, Y; Sun, P H; Wang, B; Tang, Z

2018-04-18

To establish a compact and efficient hypergraph representation and a graph-similarity-based retrieval method of molecules to achieve effective and efficient medicine information retrieval. Chemical structural formula (CSF) was a primary search target as a unique and precise identifier for each compound at the molecular level in the research field of medicine information retrieval. To retrieve medicine information effectively and efficiently, a complete workflow of the graph-based CSF retrieval system was introduced. This system accepted the photos taken from smartphones and the sketches drawn on tablet personal computers as CSF inputs, and formalized the CSFs with the corresponding graphs. Then this paper proposed a compact and efficient hypergraph representation for molecules on the basis of analyzing factors that directly affected the efficiency of graph matching. According to the characteristics of CSFs, a hierarchical collapsing method combining graph isomorphism and frequent subgraph mining was adopted. There was yet a fundamental challenge, subgraph overlapping during the collapsing procedure, which hindered the method from establishing the correct compact hypergraph of an original CSF graph. Therefore, a graph-isomorphism-based algorithm was proposed to select dominant acyclic subgraphs on the basis of overlapping analysis. Finally, the spatial similarity among graphical CSFs was evaluated by multi-dimensional measures of similarity. To evaluate the performance of the proposed method, the proposed system was firstly compared with Wikipedia Chemical Structure Explorer (WCSE), the state-of-the-art system that allowed CSF similarity searching within Wikipedia molecules dataset, on retrieval accuracy. The system achieved higher values on mean average precision, discounted cumulative gain, rank-biased precision, and expected reciprocal rank than WCSE from the top-2 to the top-10 retrieved results. Specifically, the system achieved 10%, 1.41, 6.42%, and 1.32% higher than WCSE on these metrics for top-10 retrieval results, respectively. Moreover, several retrieval cases were presented to intuitively compare with WCSE. The results of the above comparative study demonstrated that the proposed method outperformed the existing method with regard to accuracy and effectiveness. This paper proposes a graph-similarity-based retrieval approach for medicine information. To obtain satisfactory retrieval results, an isomorphism-based algorithm is proposed for dominant subgraph selection based on the subgraph overlapping analysis, as well as an effective and efficient hypergraph representation of molecules. Experiment results demonstrate the effectiveness of the proposed approach.

Compact binary hashing for music retrieval

NASA Astrophysics Data System (ADS)

Seo, Jin S.

2014-03-01

With the huge volume of music clips available for protection, browsing, and indexing, there is an increased attention to retrieve the information contents of the music archives. Music-similarity computation is an essential building block for browsing, retrieval, and indexing of digital music archives. In practice, as the number of songs available for searching and indexing is increased, so the storage cost in retrieval systems is becoming a serious problem. This paper deals with the storage problem by extending the supervector concept with the binary hashing. We utilize the similarity-preserving binary embedding in generating a hash code from the supervector of each music clip. Especially we compare the performance of the various binary hashing methods for music retrieval tasks on the widely-used genre dataset and the in-house singer dataset. Through the evaluation, we find an effective way of generating hash codes for music similarity estimation which improves the retrieval performance.

Retrieval and characterization of ozone profiles from solar infrared spectra at the Jungfraujoch

NASA Astrophysics Data System (ADS)

Barret, B.; de MazièRe, M.; Demoulin, P.

2002-12-01

Vertical distributions of ozone from June 1996 to November 2000 have been retrieved from high-resolution Fourier transform infrared (FTIR) solar absorption spectra recorded at the primary Network for Detection of Stratospheric Change station of the Jungfraujoch in the Swiss Alps (46.5°N, 8°E, 3580 m above sea level (asl). The retrievals were performed using the Optimal Estimation Method (OEM), both in a narrow spectral interval (1002.567-1003.2 cm-1) and in a broad spectral interval (1000.0-1005.0 cm-1) in the O3 9.6-μm band. A thorough characterization of the retrievals has been performed following the lines of OEM, including an information content analysis, a study of the correlations between retrieved instrumental parameters and retrieved ozone concentrations, and an evaluation of the O3 profile error budget. It is demonstrated that the information content is significantly higher for spectra in the broad microwindow, resulting in higher vertical resolutions, on the order of 8 km, of the retrieved profiles extending up to 40 km, and less correlations between retrieved parameters. An independent statistical verification of the retrieval results and their characterization has been performed by comparison of the FTIR ozone profiles with independent measurements. These are the ozone profile measurements from balloon soundings at Payerne, from the microwave radiometer at Bern and the lidar at Observatoire de Haute-Provence (OHP), and the total column data from the Dobson spectrophotometer at Arosa. Applying the optimum retrieval procedure in the broad spectral interval, an excellent agreement has been found between the FTIR O3 profile data and the correlative data. The largest offset of the FTIR data in comparison with the correlative data is found with respect to the lidar data in the 24- to 40-km layer, and is on the order of 5%. No systematic biases have been found in the troposphere, neither in the upper troposphere-lower stratosphere (UTLS) up to 18 km. The dispersion of the relative differences between the data sets, if any, is never larger than half of the natural ozone variability.

Effects of Information Access Cost and Accountability on Medical Residents' Information Retrieval Strategy and Performance During Prehandover Preparation: Evidence From Interview and Simulation Study.

PubMed

Yang, X Jessie; Wickens, Christopher D; Park, Taezoon; Fong, Liesel; Siah, Kewin T H

2015-12-01

We aimed to examine the effects of information access cost and accountability on medical residents' information retrieval strategy and performance during prehandover preparation. Prior studies observing doctors' prehandover practices witnessed the use of memory-intensive strategies when retrieving patient information. These strategies impose potential threats to patient safety as human memory is prone to errors. Of interest in this work are the underlying determinants of information retrieval strategy and the potential impacts on medical residents' information preparation performance. A two-step research approach was adopted, consisting of semistructured interviews with 21 medical residents and a simulation-based experiment with 32 medical residents. The semistructured interviews revealed that a substantial portion of medical residents (38%) relied largely on memory for preparing handover information. The simulation-based experiment showed that higher information access cost reduced information access attempts and access duration on patient documents and harmed information preparation performance. Higher accountability led to marginally longer access to patient documents. It is important to understand the underlying determinants of medical residents' information retrieval strategy and performance during prehandover preparation. We noted the criticality of easy access to patient documents in prehandover preparation. In addition, accountability marginally influenced medical residents' information retrieval strategy. Findings from this research suggested that the cost of accessing information sources should be minimized in developing handover preparation tools. © 2015, Human Factors and Ergonomics Society.

A Model of Knowledge Based Information Retrieval with Hierarchical Concept Graph.

ERIC Educational Resources Information Center

Kim, Young Whan; Kim, Jin H.

1990-01-01

Proposes a model of knowledge-based information retrieval (KBIR) that is based on a hierarchical concept graph (HCG) which shows relationships between index terms and constitutes a hierarchical thesaurus as a knowledge base. Conceptual distance between a query and an object is discussed and the use of Boolean operators is described. (25…

An efficient approach for video information retrieval

NASA Astrophysics Data System (ADS)

Dong, Daoguo; Xue, Xiangyang

2005-01-01

Today, more and more video information can be accessed through internet, satellite, etc.. Retrieving specific video information from large-scale video database has become an important and challenging research topic in the area of multimedia information retrieval. In this paper, we introduce a new and efficient index structure OVA-File, which is a variant of VA-File. In OVA-File, the approximations close to each other in data space are stored in close positions of the approximation file. The benefit is that only a part of approximations close to the query vector need to be visited to get the query result. Both shot query algorithm and video clip algorithm are proposed to support video information retrieval efficiently. The experimental results showed that the queries based on OVA-File were much faster than that based on VA-File with small loss of result quality.

Using the Weighted Keyword Model to Improve Information Retrieval for Answering Biomedical Questions

PubMed Central

Yu, Hong; Cao, Yong-gang

2009-01-01

Physicians ask many complex questions during the patient encounter. Information retrieval systems that can provide immediate and relevant answers to these questions can be invaluable aids to the practice of evidence-based medicine. In this study, we first automatically identify topic keywords from ad hoc clinical questions with a Condition Random Field model that is trained over thousands of manually annotated clinical questions. We then report on a linear model that assigns query weights based on their automatically identified semantic roles: topic keywords, domain specific terms, and their synonyms. Our evaluation shows that this weighted keyword model improves information retrieval from the Text Retrieval Conference Genomics track data. PMID:21347188

Using the weighted keyword model to improve information retrieval for answering biomedical questions.

PubMed

Yu, Hong; Cao, Yong-Gang

2009-03-01

Physicians ask many complex questions during the patient encounter. Information retrieval systems that can provide immediate and relevant answers to these questions can be invaluable aids to the practice of evidence-based medicine. In this study, we first automatically identify topic keywords from ad hoc clinical questions with a Condition Random Field model that is trained over thousands of manually annotated clinical questions. We then report on a linear model that assigns query weights based on their automatically identified semantic roles: topic keywords, domain specific terms, and their synonyms. Our evaluation shows that this weighted keyword model improves information retrieval from the Text Retrieval Conference Genomics track data.

An effective content-based image retrieval technique for image visuals representation based on the bag-of-visual-words model

PubMed Central

Jabeen, Safia; Mehmood, Zahid; Mahmood, Toqeer; Saba, Tanzila; Rehman, Amjad; Mahmood, Muhammad Tariq

2018-01-01

For the last three decades, content-based image retrieval (CBIR) has been an active research area, representing a viable solution for retrieving similar images from an image repository. In this article, we propose a novel CBIR technique based on the visual words fusion of speeded-up robust features (SURF) and fast retina keypoint (FREAK) feature descriptors. SURF is a sparse descriptor whereas FREAK is a dense descriptor. Moreover, SURF is a scale and rotation-invariant descriptor that performs better in the case of repeatability, distinctiveness, and robustness. It is robust to noise, detection errors, geometric, and photometric deformations. It also performs better at low illumination within an image as compared to the FREAK descriptor. In contrast, FREAK is a retina-inspired speedy descriptor that performs better for classification-based problems as compared to the SURF descriptor. Experimental results show that the proposed technique based on the visual words fusion of SURF-FREAK descriptors combines the features of both descriptors and resolves the aforementioned issues. The qualitative and quantitative analysis performed on three image collections, namely Corel-1000, Corel-1500, and Caltech-256, shows that proposed technique based on visual words fusion significantly improved the performance of the CBIR as compared to the feature fusion of both descriptors and state-of-the-art image retrieval techniques. PMID:29694429

An effective content-based image retrieval technique for image visuals representation based on the bag-of-visual-words model.

PubMed

Jabeen, Safia; Mehmood, Zahid; Mahmood, Toqeer; Saba, Tanzila; Rehman, Amjad; Mahmood, Muhammad Tariq

2018-01-01

For the last three decades, content-based image retrieval (CBIR) has been an active research area, representing a viable solution for retrieving similar images from an image repository. In this article, we propose a novel CBIR technique based on the visual words fusion of speeded-up robust features (SURF) and fast retina keypoint (FREAK) feature descriptors. SURF is a sparse descriptor whereas FREAK is a dense descriptor. Moreover, SURF is a scale and rotation-invariant descriptor that performs better in the case of repeatability, distinctiveness, and robustness. It is robust to noise, detection errors, geometric, and photometric deformations. It also performs better at low illumination within an image as compared to the FREAK descriptor. In contrast, FREAK is a retina-inspired speedy descriptor that performs better for classification-based problems as compared to the SURF descriptor. Experimental results show that the proposed technique based on the visual words fusion of SURF-FREAK descriptors combines the features of both descriptors and resolves the aforementioned issues. The qualitative and quantitative analysis performed on three image collections, namely Corel-1000, Corel-1500, and Caltech-256, shows that proposed technique based on visual words fusion significantly improved the performance of the CBIR as compared to the feature fusion of both descriptors and state-of-the-art image retrieval techniques.

Comparing the quality of pro- and anti-vaccination online information: a content analysis of vaccination-related webpages.

PubMed

Sak, Gabriele; Diviani, Nicola; Allam, Ahmed; Schulz, Peter J

2016-01-15

The exponential increase in health-related online platforms has made the Internet one of the main sources of health information globally. The quality of health contents disseminated on the Internet has been a central focus for many researchers. To date, however, few comparative content analyses of pro- and anti-vaccination websites have been conducted, and none of them compared the quality of information. The main objective of this study was therefore to bring new evidence on this aspect by comparing the quality of pro- and anti-vaccination online sources. Based on past literature and health information quality evaluation initiatives, a 40-categories assessment tool (Online Vaccination Information Quality Codebook) was developed and used to code a sample of 1093 webpages retrieved via Google and two filtered versions of the same search engine. The categories investigated were grouped into four main quality dimensions: web-related design quality criteria (10 categories), health-specific design quality criteria (3 categories), health related content attributes (12 categories) and vaccination-specific content attributes (15 categories). Data analysis comprised frequency counts, cross tabulations, Pearson's chi-square, and other inferential indicators. The final sample included 514 webpages in favor of vaccination, 471 against, and 108 neutral. Generally, webpages holding a favorable view toward vaccination presented more quality indicators compared to both neutral and anti-vaccination pages. However, some notable exceptions to this rule were observed. In particular, no differences were found between pro- and anti-vaccination webpages as regards vaccination-specific content attributes. Our analyses showed that the overall quality of pro-vaccination webpages is superior to anti-vaccination online sources. The developed coding scheme was proven to be a helpful and reliable tool to judge the quality of vaccination-related webpages. Based on the results, we advance recommendations for online health information providers as well as directions for future research in this field.

Pinterest as a Resource for Health Information on Chronic Obstructive Pulmonary Disease (COPD): A Social Media Content Analysis

ERIC Educational Resources Information Center

Paige, Samantha R.; Stellefson, Michael; Chaney, Beth H.; Alber, Julia M.

2015-01-01

Purpose: The purpose of this study was to explore how Pinterest group pinboards are used to communicate health information on chronic obstructive pulmonary disease (COPD). Method A nonprobability census sampling method retrieved 399 pins from the 10 most followed COPD group pinboards. Pins were coded according to COPD information categories,…

[Automation of medical literature--and information services].

PubMed

Bakker, S

1997-01-04

It is important for clinical practice to be able to find (or retrieve) relevant literature and to keep informed of the state of medical science. The fact that the contents of articles in journals are now accessible via computers is the result of integration of bibliographic techniques, medical knowledge and computer technology. Articles published in some 5000 medical journals can nowadays be retrieved electronically via Medline and Embase together (but medical literature in Dutch is underrepresented). Computerised insertion of publications into Internet dose not make information traceable or accessible, let alone reliable and readable. It cannot be predicted if electronic versions of scientific periodicals will replace the printed editions completely. However, valuable, reliable information will always have its price, even on Internet. It is unlikely that electronic information published privately (internet) will replace scientific publishers soon, for readers will still want selection and monitoring of contents and language. Good layout, professional typography and suitable illustrations to enhance reading comfort and cognitive processes, will even become more important. The problems arising from the immensity of scientific knowledge are not (any longer) of a technological nature-what is needed is a cultural about-turn of the information infrastructure in medical-scientific associations, organizations and institutions.

Simutaneous Variational Retrievals of Temperature, Humidity, Surface and Cloud Properties from Satellite and Airborne Hyperspectral Infrared Sounder Data using the Havemann-Taylor Fast Radiative Transfer Code (HT-FRTC) as the Forward Model Operator

NASA Astrophysics Data System (ADS)

Havemann, S.; Thelen, J. C.; Harlow, R. C.

2016-12-01

Full scattering radiative transfer simulations for hyperspectral infrared and shortwave sounders are essential in order to be able to extract the maximal information content from these instruments for cloudy scenes and those with significant aerosol loading, but have been rarely done because of the high computational demands. The Havemann-Taylor Fast Radiative Transfer Code works in Principal Component space, reducing the computational demand by orders of magnitude thereby making fast simultaneous retrievals of vertical profiles of temperature and humidity, surface temperature and emissivity as well as cloud and aerosol properties feasible. Results of successful retrievals using IASI sounder data as well as data taken during flights of the Airborne Research Interferometer Evaluation System (ARIES) on board the FAAM Bae 146 aircraft will be presented. These will demonstrate that the use of all the instrument channels in PC space can provide valuable information both on temperature and humidity profiles relevant for NWP and on the cirrus cloud properties at the same time. There is very significant information on the humidity profile below semi-transparent cirrus to be gained from IR sounder data. The retrieved ice water content is in good agreement with airborne in-situ measurements during Lagrangian spiral descents. In addition to the full scattering calculations, the HT-FRTC has also been trained with a fast approximation to the scattering problem which reduces it to a clear-sky calculation but with a modified extinction (Chou scaling). Chou scaling is a reasonable approximation in the infrared but is very poor where the solar contribution becomes significant. The comparison of the retrieval performance with the full scattering solution and the Chou scaling solution in the forward model operator for infrared sounders shows that temperature and humidity profiles are only marginally degraded by the use of the Chou scaling approximation. Retrievals of the specific cloud parameters (ice water content, cirrus cloud thickness and cirrus cloud horizontal fraction) are however strongly negatively affected under the Chou scaling approximation. The aim is also to use HT-FRTC to run clear and cloudy simulations for the atmospheric state test set which has been prepared by the NASA/JPL/AIRS project.

Medical image retrieval system using multiple features from 3D ROIs

NASA Astrophysics Data System (ADS)

Lu, Hongbing; Wang, Weiwei; Liao, Qimei; Zhang, Guopeng; Zhou, Zhiming

2012-02-01

Compared to a retrieval using global image features, features extracted from regions of interest (ROIs) that reflect distribution patterns of abnormalities would benefit more for content-based medical image retrieval (CBMIR) systems. Currently, most CBMIR systems have been designed for 2D ROIs, which cannot reflect 3D anatomical features and region distribution of lesions comprehensively. To further improve the accuracy of image retrieval, we proposed a retrieval method with 3D features including both geometric features such as Shape Index (SI) and Curvedness (CV) and texture features derived from 3D Gray Level Co-occurrence Matrix, which were extracted from 3D ROIs, based on our previous 2D medical images retrieval system. The system was evaluated with 20 volume CT datasets for colon polyp detection. Preliminary experiments indicated that the integration of morphological features with texture features could improve retrieval performance greatly. The retrieval result using features extracted from 3D ROIs accorded better with the diagnosis from optical colonoscopy than that based on features from 2D ROIs. With the test database of images, the average accuracy rate for 3D retrieval method was 76.6%, indicating its potential value in clinical application.

A Sieving ANN for Emotion-Based Movie Clip Classification

NASA Astrophysics Data System (ADS)

Watanapa, Saowaluk C.; Thipakorn, Bundit; Charoenkitkarn, Nipon

Effective classification and analysis of semantic contents are very important for the content-based indexing and retrieval of video database. Our research attempts to classify movie clips into three groups of commonly elicited emotions, namely excitement, joy and sadness, based on a set of abstract-level semantic features extracted from the film sequence. In particular, these features consist of six visual and audio measures grounded on the artistic film theories. A unique sieving-structured neural network is proposed to be the classifying model due to its robustness. The performance of the proposed model is tested with 101 movie clips excerpted from 24 award-winning and well-known Hollywood feature films. The experimental result of 97.8% correct classification rate, measured against the collected human-judges, indicates the great potential of using abstract-level semantic features as an engineered tool for the application of video-content retrieval/indexing.

Exploring Hardware-Based Primitives to Enhance Parallel Security Monitoring in a Novel Computing Architecture

DTIC Science & Technology

2007-03-01

software level retrieve state information that can inherently contain more contextual information . As a result, such mechanisms can be applied in more...ease by which state information can be gathered for monitoring purposes. For example, we consider soft security to allow for easier state retrieval ...files are to be checked and what parameters are to be verified. The independent auditor periodically retrieves information pertaining to the files in

Evaluation of email alerts in practice: Part 2. Validation of the information assessment method.

PubMed

Pluye, Pierre; Grad, Roland M; Johnson-Lafleur, Janique; Bambrick, Tara; Burnand, Bernard; Mercer, Jay; Marlow, Bernard; Campbell, Craig

2010-12-01

The information assessment method (IAM) permits health professionals to systematically document the relevance, cognitive impact, use and health outcomes of information objects delivered by or retrieved from electronic knowledge resources. The companion review paper (Part 1) critically examined the literature, and proposed a 'Push-Pull-Acquisition-Cognition-Application' evaluation framework, which is operationalized by IAM. The purpose of the present paper (Part 2) is to examine the content validity of the IAM cognitive checklist when linked to email alerts. A qualitative component of a mixed methods study was conducted with 46 doctors reading and rating research-based synopses sent on email. The unit of analysis was a doctor's explanation of a rating of one item regarding one synopsis. Interviews with participants provided 253 units that were analysed to assess concordance with item definitions. The content relevance of seven items was supported. For three items, revisions were needed. Interviews suggested one new item. This study has yielded a 2008 version of IAM. © 2010 Blackwell Publishing Ltd.

Probabilistic and machine learning-based retrieval approaches for biomedical dataset retrieval

PubMed Central

Karisani, Payam; Qin, Zhaohui S; Agichtein, Eugene

2018-01-01

Abstract The bioCADDIE dataset retrieval challenge brought together different approaches to retrieval of biomedical datasets relevant to a user’s query, expressed as a text description of a needed dataset. We describe experiments in applying a data-driven, machine learning-based approach to biomedical dataset retrieval as part of this challenge. We report on a series of experiments carried out to evaluate the performance of both probabilistic and machine learning-driven techniques from information retrieval, as applied to this challenge. Our experiments with probabilistic information retrieval methods, such as query term weight optimization, automatic query expansion and simulated user relevance feedback, demonstrate that automatically boosting the weights of important keywords in a verbose query is more effective than other methods. We also show that although there is a rich space of potential representations and features available in this domain, machine learning-based re-ranking models are not able to improve on probabilistic information retrieval techniques with the currently available training data. The models and algorithms presented in this paper can serve as a viable implementation of a search engine to provide access to biomedical datasets. The retrieval performance is expected to be further improved by using additional training data that is created by expert annotation, or gathered through usage logs, clicks and other processes during natural operation of the system. Database URL: https://github.com/emory-irlab/biocaddie PMID:29688379

SIFT Meets CNN: A Decade Survey of Instance Retrieval.

PubMed

Zheng, Liang; Yang, Yi; Tian, Qi

2018-05-01

In the early days, content-based image retrieval (CBIR) was studied with global features. Since 2003, image retrieval based on local descriptors (de facto SIFT) has been extensively studied for over a decade due to the advantage of SIFT in dealing with image transformations. Recently, image representations based on the convolutional neural network (CNN) have attracted increasing interest in the community and demonstrated impressive performance. Given this time of rapid evolution, this article provides a comprehensive survey of instance retrieval over the last decade. Two broad categories, SIFT-based and CNN-based methods, are presented. For the former, according to the codebook size, we organize the literature into using large/medium-sized/small codebooks. For the latter, we discuss three lines of methods, i.e., using pre-trained or fine-tuned CNN models, and hybrid methods. The first two perform a single-pass of an image to the network, while the last category employs a patch-based feature extraction scheme. This survey presents milestones in modern instance retrieval, reviews a broad selection of previous works in different categories, and provides insights on the connection between SIFT and CNN-based methods. After analyzing and comparing retrieval performance of different categories on several datasets, we discuss promising directions towards generic and specialized instance retrieval.

AEROMETRIC INFORMATION RETRIEVAL SYSTEM (AIRS) - GRAPHICS

EPA Science Inventory

Aerometric Information Retrieval System (AIRS) is a computer-based repository of information about airborne pollution in the United States and various World Health Organization (WHO) member countries. AIRS is administered by the U.S. Environmental Protection Agency, and runs on t...

JANE, A new information retrieval system for the Radiation Shielding Information Center

DOE Office of Scientific and Technical Information (OSTI.GOV)

Trubey, D.K.

A new information storage and retrieval system has been developed for the Radiation Shielding Information Center (RSIC) at Oak Ridge National Laboratory to replace mainframe systems that have become obsolete. The database contains citations and abstracts of literature which were selected by RSIC analysts and indexed with terms from a controlled vocabulary. The database, begun in 1963, has been maintained continuously since that time. The new system, called JANE, incorporates automatic indexing techniques and on-line retrieval using the RSIC Data General Eclipse MV/4000 minicomputer, Automatic indexing and retrieval techniques based on fuzzy-set theory allow the presentation of results in ordermore » of Retrieval Status Value. The fuzzy-set membership function depends on term frequency in the titles and abstracts and on Term Discrimination Values which indicate the resolving power of the individual terms. These values are determined by the Cover Coefficient method. The use of a commercial database base to store and retrieve the indexing information permits rapid retrieval of the stored documents. Comparisons of the new and presently-used systems for actual searches of the literature indicate that it is practical to replace the mainframe systems with a minicomputer system similar to the present version of JANE. 18 refs., 10 figs.« less

A multilingual assessment of melanoma information quality on the Internet.

PubMed

Bari, Lilla; Kemeny, Lajos; Bari, Ferenc

2014-06-01

This study aims to assess and compare melanoma information quality in Hungarian, Czech, and German languages on the Internet. We used country-specific Google search engines to retrieve the first 25 uniform resource locators (URLs) by searching the word "melanoma" in the given language. Using the automated toolbar of Health On the Net Foundation (HON), we assessed each Web site for HON certification based on the Health On the Net Foundation Code of Conduct (HONcode). Information quality was determined using a 35-point checklist created by Bichakjian et al. (J Clin Oncol 20:134-141, 2002), with the NCCN melanoma guideline as control. After excluding duplicate and link-only pages, a total of 24 Hungarian, 18 Czech, and 21 German melanoma Web sites were evaluated and rated. The amount of HON certified Web sites was the highest among the German Web pages (19%). One of the retrieved Hungarian and none of the Czech Web sites were HON certified. We found the highest number of Web sites containing comprehensive, correct melanoma information in German language, followed by Czech and Hungarian pages. Although the majority of the Web sites lacked data about incidence, risk factors, prevention, treatment, work-up, and follow-up, at least one comprehensive, high-quality Web site was found in each language. Several Web sites contained incorrect information in each language. While a small amount of comprehensive, quality melanoma-related Web sites was found, most of the retrieved Web content lacked basic disease information, such as risk factors, prevention, and treatment. A significant number of Web sites contained malinformation. In case of melanoma, primary and secondary preventions are of especially high importance; therefore, the improvement of disease information quality available on the Internet is necessary.

Medical Image Databases

PubMed Central

Tagare, Hemant D.; Jaffe, C. Carl; Duncan, James

1997-01-01

Abstract Information contained in medical images differs considerably from that residing in alphanumeric format. The difference can be attributed to four characteristics: (1) the semantics of medical knowledge extractable from images is imprecise; (2) image information contains form and spatial data, which are not expressible in conventional language; (3) a large part of image information is geometric; (4) diagnostic inferences derived from images rest on an incomplete, continuously evolving model of normality. This paper explores the differentiating characteristics of text versus images and their impact on design of a medical image database intended to allow content-based indexing and retrieval. One strategy for implementing medical image databases is presented, which employs object-oriented iconic queries, semantics by association with prototypes, and a generic schema. PMID:9147338

Chlorophyll content retrieval from hyperspectral remote sensing imagery.

PubMed

Yang, Xiguang; Yu, Ying; Fan, Wenyi

2015-07-01

Chlorophyll content is the essential parameter in the photosynthetic process determining leaf spectral variation in visible bands. Therefore, the accurate estimation of the forest canopy chlorophyll content is a significant foundation in assessing forest growth and stress affected by diseases. Hyperspectral remote sensing with high spatial resolution can be used for estimating chlorophyll content. In this study, the chlorophyll content was retrieved step by step using Hyperion imagery. Firstly, the spectral curve of the leaf was analyzed, 25 spectral characteristic parameters were identified through the correlation coefficient matrix, and a leaf chlorophyll content inversion model was established using a stepwise regression method. Secondly, the pixel reflectance was converted into leaf reflectance by a geometrical-optical model (4-scale). The three most important parameters of reflectance conversion, including the multiple scattering factor (M 0 ), and the probability of viewing the sunlit tree crown (P T ) and the background (P G ), were estimated by leaf area index (LAI), respectively. The results indicated that M 0 , P T , and P G could be described as a logarithmic function of LAI, with all R (2) values above 0.9. Finally, leaf chlorophyll content was retrieved with RMSE = 7.3574 μg/cm(2), and canopy chlorophyll content per unit ground surface area was estimated based on leaf chlorophyll content and LAI. Chlorophyll content mapping can be useful for the assessment of forest growth stage and diseases.

Elaborative Retrieval: Do Semantic Mediators Improve Memory?

ERIC Educational Resources Information Center

Lehman, Melissa; Karpicke, Jeffrey D.

2016-01-01

The elaborative retrieval account of retrieval-based learning proposes that retrieval enhances retention because the retrieval process produces the generation of semantic mediators that link cues to target information. We tested 2 assumptions that form the basis of this account: that semantic mediators are more likely to be generated during…

A review of EO image information mining

NASA Astrophysics Data System (ADS)

Quartulli, Marco; Olaizola, Igor G.

2013-01-01

We analyze the state of the art of content-based retrieval in Earth observation image archives focusing on complete systems showing promise for operational implementation. The different paradigms at the basis of the main system families are introduced. The approaches taken are considered, focusing in particular on the phases after primitive feature extraction. The solutions envisaged for the issues related to feature simplification and synthesis, indexing, semantic labeling are reviewed. The methodologies for query specification and execution are evaluated. Conclusions are drawn on the state of published research in Earth observation (EO) mining.

Content-Based Management of Image Databases in the Internet Age

ERIC Educational Resources Information Center

Kleban, James Theodore

2010-01-01

The Internet Age has seen the emergence of richly annotated image data collections numbering in the billions of items. This work makes contributions in three primary areas which aid the management of this data: image representation, efficient retrieval, and annotation based on content and metadata. The contributions are as follows. First,…

Collection Fusion Using Bayesian Estimation of a Linear Regression Model in Image Databases on the Web.

ERIC Educational Resources Information Center

Kim, Deok-Hwan; Chung, Chin-Wan

2003-01-01

Discusses the collection fusion problem of image databases, concerned with retrieving relevant images by content based retrieval from image databases distributed on the Web. Focuses on a metaserver which selects image databases supporting similarity measures and proposes a new algorithm which exploits a probabilistic technique using Bayesian…

Simulation of snow and soil water content as a basis for satellite retrievals

USDA-ARS?s Scientific Manuscript database

It is not yet possible to determine whether the snow has changed over time despite collection of passive microwave data for more than thirty years. Physically-based, but computationally simple snow and soil models have been coupled to form the basis of a data assimilation system for retrievals of sn...

Chinese Brush Calligraphy Character Retrieval and Learning

ERIC Educational Resources Information Center

Zhuang, Yueting; Zhang, Xiafen; Lu, Weiming; Wu, Fei

2007-01-01

Chinese brush calligraphy is a valuable civilization legacy and a high art of scholarship. It is still popular in Chinese banners, newspaper mastheads, university names, and celebration gifts. There are Web sites that try to help people enjoy and learn Chinese calligraphy. However, there lacks advanced services such as content-based retrieval or…

Depolarization Lidar Determination Of Cloud-Base Microphysical Properties

NASA Astrophysics Data System (ADS)

Donovan, D. P.; Klein Baltink, H.; Henzing, J. S.; de Roode, S.; Siebesma, A. P.

2016-06-01

The links between multiple-scattering induced depolarization and cloud microphysical properties (e.g. cloud particle number density, effective radius, water content) have long been recognised. Previous efforts to use depolarization information in a quantitative manner to retrieve cloud microphysical cloud properties have also been undertaken but with limited scope and, arguably, success. In this work we present a retrieval procedure applicable to liquid stratus clouds with (quasi-)linear LWC profiles and (quasi-)constant number density profiles in the cloud-base region. This set of assumptions allows us to employ a fast and robust inversion procedure based on a lookup-table approach applied to extensive lidar Monte-Carlo multiple-scattering calculations. An example validation case is presented where the results of the inversion procedure are compared with simultaneous cloud radar observations. In non-drizzling conditions it was found, in general, that the lidar- only inversion results can be used to predict the radar reflectivity within the radar calibration uncertainty (2-3 dBZ). Results of a comparison between ground-based aerosol number concentration and lidar-derived cloud base number considerations are also presented. The observed relationship between the two quantities is seen to be consistent with the results of previous studies based on aircraft-based in situ measurements.

DORS: DDC Online Retrieval System.

ERIC Educational Resources Information Center

Liu, Songqiao; Svenonius, Elaine

1991-01-01

Describes the Dewey Online Retrieval System (DORS), which was developed at the University of California, Los Angeles (UCLA), to experiment with classification-based search strategies in online catalogs. Classification structures in automated information retrieval are discussed; and specifications for a classification retrieval interface are…

Contextual Information Drives the Reconsolidation-Dependent Updating of Retrieved Fear Memories

PubMed Central

Jarome, Timothy J; Ferrara, Nicole C; Kwapis, Janine L; Helmstetter, Fred J

2015-01-01

Stored memories enter a temporary state of vulnerability following retrieval known as ‘reconsolidation', a process that can allow memories to be modified to incorporate new information. Although reconsolidation has become an attractive target for treatment of memories related to traumatic past experiences, we still do not know what new information triggers the updating of retrieved memories. Here, we used biochemical markers of synaptic plasticity in combination with a novel behavioral procedure to determine what was learned during memory reconsolidation under normal retrieval conditions. We eliminated new information during retrieval by manipulating animals' training experience and measured changes in proteasome activity and GluR2 expression in the amygdala, two established markers of fear memory lability and reconsolidation. We found that eliminating new contextual information during the retrieval of memories for predictable and unpredictable fear associations prevented changes in proteasome activity and glutamate receptor expression in the amygdala, indicating that this new information drives the reconsolidation of both predictable and unpredictable fear associations on retrieval. Consistent with this, eliminating new contextual information prior to retrieval prevented the memory-impairing effects of protein synthesis inhibitors following retrieval. These results indicate that under normal conditions, reconsolidation updates memories by incorporating new contextual information into the memory trace. Collectively, these results suggest that controlling contextual information present during retrieval may be a useful strategy for improving reconsolidation-based treatments of traumatic memories associated with anxiety disorders such as post-traumatic stress disorder. PMID:26062788

Geographic Information System (GIS) capabilities in traffic accident information management: a qualitative approach.

PubMed

Ahmadi, Maryam; Valinejadi, Ali; Goodarzi, Afshin; Safari, Ameneh; Hemmat, Morteza; Majdabadi, Hesamedin Askari; Mohammadi, Ali

2017-06-01

Traffic accidents are one of the more important national and international issues, and their consequences are important for the political, economical, and social level in a country. Management of traffic accident information requires information systems with analytical and accessibility capabilities to spatial and descriptive data. The aim of this study was to determine the capabilities of a Geographic Information System (GIS) in management of traffic accident information. This qualitative cross-sectional study was performed in 2016. In the first step, GIS capabilities were identified via literature retrieved from the Internet and based on the included criteria. Review of the literature was performed until data saturation was reached; a form was used to extract the capabilities. In the second step, study population were hospital managers, police, emergency, statisticians, and IT experts in trauma, emergency and police centers. Sampling was purposive. Data was collected using a questionnaire based on the first step data; validity and reliability were determined by content validity and Cronbach's alpha of 75%. Data was analyzed using the decision Delphi technique. GIS capabilities were identified in ten categories and 64 sub-categories. Import and process of spatial and descriptive data and so, analysis of this data were the most important capabilities of GIS in traffic accident information management. Storing and retrieving of descriptive and spatial data, providing statistical analysis in table, chart and zoning format, management of bad structure issues, determining the cost effectiveness of the decisions and prioritizing their implementation were the most important capabilities of GIS which can be efficient in the management of traffic accident information.

Accuracy Assessment of GAI Retrieval from SPOT5 Take According to Crop Type and Crop Development (BELCAM)

NASA Astrophysics Data System (ADS)

Delloye, C.; Weiss, M.; Baret, F.; Morin, D.; Defourny, P.

2016-08-01

The successful launch of Sentinel-2A equipped of the Multi Spectral Instrument is an exceptional opportunity to deliver regular information of high spatial and temporal resolution about the agricultural fields in Belgium. This research takes advantage of SPOT5 Take5 frequent acquisition over the Belgium in 2015 to realize an in-depth analysis of the Green Area Index (GAI) retrieval by inversion of a radiative transfer model at field scale over the whole Belgium for 2 crops: winter wheat and potato. The GAI is particularly relevant to derive the chlorophyll content at the canopy level (GAI × Cab) which is directly correlated to the Nitrogen content of the crops. This information is of crucial importance to advice farmers on the nitrogen fertilization genuinely required by the crops allowing the best yield and avoiding over fertilization and pollution of the groundwater table. The use of vegetation indexes seems promising to retrieve accurately the GAI (RRMSE =10.2%) during the period of the third Nitrogen application for the winter wheat. Further analyses have to be conducted for varieties of potato with a high level of biomass development (GAI > 4).

A Comparison of Three Online Information Retrieval Services.

ERIC Educational Resources Information Center

Zais, Harriet W.

Three firms which offer online information retrieval are compared. The firms are Lockheed Information Service, System Development Corporation and the Western Research Application Center. Comparison tables provide information such as hours accessible, coverage, file update, search elements and cost figures for 15 data bases. In addition, general…

Image query and indexing for digital x rays

NASA Astrophysics Data System (ADS)

Long, L. Rodney; Thoma, George R.

1998-12-01

The web-based medical information retrieval system (WebMIRS) allows interned access to databases containing 17,000 digitized x-ray spine images and associated text data from National Health and Nutrition Examination Surveys (NHANES). WebMIRS allows SQL query of the text, and viewing of the returned text records and images using a standard browser. We are now working (1) to determine utility of data directly derived from the images in our databases, and (2) to investigate the feasibility of computer-assisted or automated indexing of the images to support image retrieval of images of interest to biomedical researchers in the field of osteoarthritis. To build an initial database based on image data, we are manually segmenting a subset of the vertebrae, using techniques from vertebral morphometry. From this, we will derive and add to the database vertebral features. This image-derived data will enhance the user's data access capability by enabling the creation of combined SQL/image-content queries.

Presentation video retrieval using automatically recovered slide and spoken text

NASA Astrophysics Data System (ADS)

Cooper, Matthew

2013-03-01

Video is becoming a prevalent medium for e-learning. Lecture videos contain text information in both the presentation slides and lecturer's speech. This paper examines the relative utility of automatically recovered text from these sources for lecture video retrieval. To extract the visual information, we automatically detect slides within the videos and apply optical character recognition to obtain their text. Automatic speech recognition is used similarly to extract spoken text from the recorded audio. We perform controlled experiments with manually created ground truth for both the slide and spoken text from more than 60 hours of lecture video. We compare the automatically extracted slide and spoken text in terms of accuracy relative to ground truth, overlap with one another, and utility for video retrieval. Results reveal that automatically recovered slide text and spoken text contain different content with varying error profiles. Experiments demonstrate that automatically extracted slide text enables higher precision video retrieval than automatically recovered spoken text.

How teachers can help learners build storage and retrieval strength.

PubMed

Desy, Janeve; Busche, Kevin; Cusano, Ronald; Veale, Pamela; Coderre, Sylvain; McLaughlin, Kevin

2018-04-01

To be an effective teacher, content expertise is necessary but alone does not guarantee optimal learning outcomes for students. In this article, the authors discuss ways in which medical teachers can shape the learning of their students and enable them to become more efficient and effective learners. Using Bjork and Bjork's new theory of disuse as their framework, the authors discuss strategies to improve storage strength of to-be-learned information and strategies to improve retrieval strength of learned information. Strategies to improve storage strength include optimizing cognitive load, providing causal explanations, and giving effective feedback. Strategies to improve retrieval strength include situated cognition and various types of retrieval practice. Adopting these teaching strategies should hopefully help teachers improve the learning outcomes of their students, but there is still a need for further research into the science of learning and the science of instruction, including comparative effectiveness of different teaching strategies and how best to translate findings from the psychology literature into medical education.

Global Interior Robot Localisation by a Colour Content Image Retrieval System

NASA Astrophysics Data System (ADS)

Chaari, A.; Lelandais, S.; Montagne, C.; Ahmed, M. Ben

2007-12-01

We propose a new global localisation approach to determine a coarse position of a mobile robot in structured indoor space using colour-based image retrieval techniques. We use an original method of colour quantisation based on the baker's transformation to extract a two-dimensional colour pallet combining as well space and vicinity-related information as colourimetric aspect of the original image. We conceive several retrieving approaches bringing to a specific similarity measure [InlineEquation not available: see fulltext.] integrating the space organisation of colours in the pallet. The baker's transformation provides a quantisation of the image into a space where colours that are nearby in the original space are also nearby in the output space, thereby providing dimensionality reduction and invariance to minor changes in the image. Whereas the distance [InlineEquation not available: see fulltext.] provides for partial invariance to translation, sight point small changes, and scale factor. In addition to this study, we developed a hierarchical search module based on the logic classification of images following rooms. This hierarchical module reduces the searching indoor space and ensures an improvement of our system performances. Results are then compared with those brought by colour histograms provided with several similarity measures. In this paper, we focus on colour-based features to describe indoor images. A finalised system must obviously integrate other type of signature like shape and texture.

Towards case-based medical learning in radiological decision making using content-based image retrieval

PubMed Central

2011-01-01

Background Radiologists' training is based on intensive practice and can be improved with the use of diagnostic training systems. However, existing systems typically require laboriously prepared training cases and lack integration into the clinical environment with a proper learning scenario. Consequently, diagnostic training systems advancing decision-making skills are not well established in radiological education. Methods We investigated didactic concepts and appraised methods appropriate to the radiology domain, as follows: (i) Adult learning theories stress the importance of work-related practice gained in a team of problem-solvers; (ii) Case-based reasoning (CBR) parallels the human problem-solving process; (iii) Content-based image retrieval (CBIR) can be useful for computer-aided diagnosis (CAD). To overcome the known drawbacks of existing learning systems, we developed the concept of image-based case retrieval for radiological education (IBCR-RE). The IBCR-RE diagnostic training is embedded into a didactic framework based on the Seven Jump approach, which is well established in problem-based learning (PBL). In order to provide a learning environment that is as similar as possible to radiological practice, we have analysed the radiological workflow and environment. Results We mapped the IBCR-RE diagnostic training approach into the Image Retrieval in Medical Applications (IRMA) framework, resulting in the proposed concept of the IRMAdiag training application. IRMAdiag makes use of the modular structure of IRMA and comprises (i) the IRMA core, i.e., the IRMA CBIR engine; and (ii) the IRMAcon viewer. We propose embedding IRMAdiag into hospital information technology (IT) infrastructure using the standard protocols Digital Imaging and Communications in Medicine (DICOM) and Health Level Seven (HL7). Furthermore, we present a case description and a scheme of planned evaluations to comprehensively assess the system. Conclusions The IBCR-RE paradigm incorporates a novel combination of essential aspects of diagnostic learning in radiology: (i) Provision of work-relevant experiences in a training environment integrated into the radiologist's working context; (ii) Up-to-date training cases that do not require cumbersome preparation because they are provided by routinely generated electronic medical records; (iii) Support of the way adults learn while remaining suitable for the patient- and problem-oriented nature of medicine. Future work will address unanswered questions to complete the implementation of the IRMAdiag trainer. PMID:22032775

Towards case-based medical learning in radiological decision making using content-based image retrieval.

PubMed

Welter, Petra; Deserno, Thomas M; Fischer, Benedikt; Günther, Rolf W; Spreckelsen, Cord

2011-10-27

Radiologists' training is based on intensive practice and can be improved with the use of diagnostic training systems. However, existing systems typically require laboriously prepared training cases and lack integration into the clinical environment with a proper learning scenario. Consequently, diagnostic training systems advancing decision-making skills are not well established in radiological education. We investigated didactic concepts and appraised methods appropriate to the radiology domain, as follows: (i) Adult learning theories stress the importance of work-related practice gained in a team of problem-solvers; (ii) Case-based reasoning (CBR) parallels the human problem-solving process; (iii) Content-based image retrieval (CBIR) can be useful for computer-aided diagnosis (CAD). To overcome the known drawbacks of existing learning systems, we developed the concept of image-based case retrieval for radiological education (IBCR-RE). The IBCR-RE diagnostic training is embedded into a didactic framework based on the Seven Jump approach, which is well established in problem-based learning (PBL). In order to provide a learning environment that is as similar as possible to radiological practice, we have analysed the radiological workflow and environment. We mapped the IBCR-RE diagnostic training approach into the Image Retrieval in Medical Applications (IRMA) framework, resulting in the proposed concept of the IRMAdiag training application. IRMAdiag makes use of the modular structure of IRMA and comprises (i) the IRMA core, i.e., the IRMA CBIR engine; and (ii) the IRMAcon viewer. We propose embedding IRMAdiag into hospital information technology (IT) infrastructure using the standard protocols Digital Imaging and Communications in Medicine (DICOM) and Health Level Seven (HL7). Furthermore, we present a case description and a scheme of planned evaluations to comprehensively assess the system. The IBCR-RE paradigm incorporates a novel combination of essential aspects of diagnostic learning in radiology: (i) Provision of work-relevant experiences in a training environment integrated into the radiologist's working context; (ii) Up-to-date training cases that do not require cumbersome preparation because they are provided by routinely generated electronic medical records; (iii) Support of the way adults learn while remaining suitable for the patient- and problem-oriented nature of medicine. Future work will address unanswered questions to complete the implementation of the IRMAdiag trainer.

Experiments with a novel content-based image retrieval software: can we eliminate classification systems in adolescent idiopathic scoliosis?

PubMed

Menon, K Venugopal; Kumar, Dinesh; Thomas, Tessamma

2014-02-01

Study Design Preliminary evaluation of new tool. Objective To ascertain whether the newly developed content-based image retrieval (CBIR) software can be used successfully to retrieve images of similar cases of adolescent idiopathic scoliosis (AIS) from a database to help plan treatment without adhering to a classification scheme. Methods Sixty-two operated cases of AIS were entered into the newly developed CBIR database. Five new cases of different curve patterns were used as query images. The images were fed into the CBIR database that retrieved similar images from the existing cases. These were analyzed by a senior surgeon for conformity to the query image. Results Within the limits of variability set for the query system, all the resultant images conformed to the query image. One case had no similar match in the series. The other four retrieved several images that were matching with the query. No matching case was left out in the series. The postoperative images were then analyzed to check for surgical strategies. Broad guidelines for treatment could be derived from the results. More precise query settings, inclusion of bending films, and a larger database will enhance accurate retrieval and better decision making. Conclusion The CBIR system is an effective tool for accurate documentation and retrieval of scoliosis images. Broad guidelines for surgical strategies can be made from the postoperative images of the existing cases without adhering to any classification scheme.

Hippocampal activation during episodic and semantic memory retrieval: comparing category production and category cued recall.

PubMed

Ryan, Lee; Cox, Christine; Hayes, Scott M; Nadel, Lynn

2008-01-01

Whether or not the hippocampus participates in semantic memory retrieval has been the focus of much debate in the literature. However, few neuroimaging studies have directly compared hippocampal activation during semantic and episodic retrieval tasks that are well matched in all respects other than the source of the retrieved information. In Experiment 1, we compared hippocampal fMRI activation during a classic semantic memory task, category production, and an episodic version of the same task, category cued recall. Left hippocampal activation was observed in both episodic and semantic conditions, although other regions of the brain clearly distinguished the two tasks. Interestingly, participants reported using retrieval strategies during the semantic retrieval task that relied on autobiographical and spatial information; for example, visualizing themselves in their kitchen while producing items for the category kitchen utensils. In Experiment 2, we considered whether the use of these spatial and autobiographical retrieval strategies could have accounted for the hippocampal activation observed in Experiment 1. Categories were presented that elicited one of three retrieval strategy types, autobiographical and spatial, autobiographical and nonspatial, and neither autobiographical nor spatial. Once again, similar hippocampal activation was observed for all three category types, regardless of the inclusion of spatial or autobiographical content. We conclude that the distinction between semantic and episodic memory is more complex than classic memory models suggest.

Hippocampal Activation during Episodic and Semantic Memory Retrieval: Comparing Category Production and Category Cued Recall

PubMed Central

Ryan, Lee; Cox, Christine; Hayes, Scott M.; Nadel, Lynn

2008-01-01

Whether or not the hippocampus participates in semantic memory retrieval has been the focus of much debate in the literature. However, few neuroimaging studies have directly compared hippocampal activation during semantic and episodic retrieval tasks that are well matched in all respects other than the source of the retrieved information. In Experiment 1, we compared hippocampal fMRI activation during a classic semantic memory task, category production, and an episodic version of the same task, category cued recall. Left hippocampal activation was observed in both episodic and semantic conditions, although other regions of the brain clearly distinguished the two tasks. Interestingly, participants reported using retrieval strategies during the semantic retrieval task that relied on autobiographical and spatial information; for example, visualizing themselves in their kitchen while producing items for the category kitchen utensils. In Experiment 2, we considered whether the use of these spatial and autobiographical retrieval strategies could have accounted for the hippocampal activation observed in Experiment 1. Categories were presented that elicited one of three retrieval strategy types, autobiographical and spatial, autobiographical and nonspatial, and neither autobiographical nor spatial. Once again, similar hippocampal activation was observed for all three category types, regardless of the inclusion of spatial or autobiographical content. We conclude that the distinction between semantic and episodic memory is more complex than classic memory models suggest. PMID:18420234

The influence of sea fog inhomogeneity on its microphysical characteristics retrieval

NASA Astrophysics Data System (ADS)

Hao, Zengzhou; Pan, Delu; Gong, Fang; He, Xianqiang

2008-10-01

A study on the effect of sea fog inhomogeneity on its microphysical parameters retrieval is presented. On the condition that the average liquid water content is linear vertically and the power spectrum spectral index sets 2.0, we generate a 3D sea fog fields by controlling the total liquid water contents greater than 0.04g/m3 based on the iterative method for generating scaling log-normal random field with an energy spectrum and a fragmentized cloud algorithm. Based on the fog field, the radiance at the wavelengths of 0.67 and 1.64 μm are simulated with 3D radiative transfer model SHDOM, and then the fog optical thickness and effective particle radius are simultaneously retrieved using the generic look-up-table AVHRR cloud algorithm. By comparing those fog optical thickness and effective particle radius, the influence of sea fog inhomogeneity on its properties retrieval is discussed. It exhibits the system bias when inferring sea fog physical properties from satellite measurements based on the assumption of plane parallel homogeneous atmosphere. And the bias depends on the solar zenith angel. The optical thickness is overrated while the effective particle radius is under-estimated at two solar zenith angle 30° and 60°. Those results show that it is necessary for sea fog true characteristics retrieval to develop a new algorithm using the 3D radiative transfer.

Global Precipitation Measurement (GPM) Mission: Precipitation Processing System (PPS) GPM Mission Gridded Text Products Provide Surface Precipitation Retrievals

NASA Technical Reports Server (NTRS)

Stocker, Erich Franz; Kelley, O.; Kummerow, C.; Huffman, G.; Olson, W.; Kwiatkowski, J.

2015-01-01

In February 2015, the Global Precipitation Measurement (GPM) mission core satellite will complete its first year in space. The core satellite carries a conically scanning microwave imager called the GPM Microwave Imager (GMI), which also has 166 GHz and 183 GHz frequency channels. The GPM core satellite also carries a dual frequency radar (DPR) which operates at Ku frequency, similar to the Tropical Rainfall Measuring Mission (TRMM) Precipitation Radar, and a new Ka frequency. The precipitation processing system (PPS) is producing swath-based instantaneous precipitation retrievals from GMI, both radars including a dual-frequency product, and a combined GMIDPR precipitation retrieval. These level 2 products are written in the HDF5 format and have many additional parameters beyond surface precipitation that are organized into appropriate groups. While these retrieval algorithms were developed prior to launch and are not optimal, these algorithms are producing very creditable retrievals. It is appropriate for a wide group of users to have access to the GPM retrievals. However, for researchers requiring only surface precipitation, these L2 swath products can appear to be very intimidating and they certainly do contain many more variables than the average researcher needs. Some researchers desire only surface retrievals stored in a simple easily accessible format. In response, PPS has begun to produce gridded text based products that contain just the most widely used variables for each instrument (surface rainfall rate, fraction liquid, fraction convective) in a single line for each grid box that contains one or more observations.This paper will describe the gridded data products that are being produced and provide an overview of their content. Currently two types of gridded products are being produced: (1) surface precipitation retrievals from the core satellite instruments GMI, DPR, and combined GMIDPR (2) surface precipitation retrievals for the partner constellation satellites. Both of these gridded products are generated for a.25 degree x.25 degree hourly grid, which are packaged into daily ASCII (American Standard Code for Information Interchange) files that can downloaded from the PPS FTP (File Transfer Protocol) site. To reduce the download size, the files are compressed using the gzip utility.This paper will focus on presenting high-level details about the gridded text product being generated from the instruments on the GPM core satellite. But summary information will also be presented about the partner radiometer gridded product. All retrievals for the partner radiometer are done using the GPROF2014 algorithmusing as input the PPS generated inter-calibrated 1C product for the radiometer.

Information content in frequency-dependent, multi-offset GPR data for layered media reconstruction using full-wave inversion

NASA Astrophysics Data System (ADS)

De Coster, Albéric; Phuong Tran, Anh; Lambot, Sébastien

2014-05-01

Water lost through leaks can represent high percentages of the total production in water supply systems and constitutes an important issue. Leak detection can be tackled with various techniques such as the ground-penetrating radar (GPR). Based on this technology, various procedures have been elaborated to characterize a leak and its evolution. In this study, we focus on a new full-wave radar modelling approach for near-field conditions, which takes into account the antenna effects as well as the interactions between the antenna(s) and the medium through frequency-dependent global transmission and reflection coefficients. This approach is applied to layered media for which 3-D Green's functions can be calculated. The model allows for a quantitative estimation of the properties of multilayered media by using full-wave inversion. This method, however, proves to be limited to provide users with an on-demand assessment as it is generally computationally demanding and time consuming, depending on the medium configuration as well as the number of unknown parameters to retrieve. In that respect, we propose two leads in order to enhance the parameter retrieval step. The first one consists in analyzing the impact of the reduction of the number of frequencies on the information content. For both numerical and laboratory experiments, this operation has been achieved by investigating the response surface topography of objective functions arising from the comparison between measured and modelled data. The second one involves the numerical implementation of multistatic antenna configurations with constant and variable offsets in the model. These two kinds of analyses are then combined in numerical experiments to observe the conjugated effect of the number of frequencies and the offset configuration. To perform the numerical analyses, synthetic Green's functions were simulated for different multilayered medium configurations. The results show that an antenna offset increase leads to an improvement in the response surface topography, which is more or less marked according to the initial information content. It also highlights the theoretical possibility of significantly reducing the number of frequencies without degrading the information content. This last statement is confirmed with the laboratory experiment which incorporates measurements done with a Vivaldi antenna above a medium composed of one or more sand layers characterized by different water contents. As a conclusion, the offset and frequency analyses highlight the great potential of the model for improving the soil parameter retrieval while reducing the computation time for a given antenna(s) - medium configuration. Acknowledgments: This work benefited from networking activities carried out within the EU funded COST Action TU1208 "Civil Engineering Applications of Ground Penetrating Radar" and was supported by the Walloon Region through the "SENSPORT" project (Convention n°1217720) undertook in the framework of the WBGreen research program.

Strategic Help in User Interfaces for Information Retrieval.

ERIC Educational Resources Information Center

Brajnik, Giorgio; Mizzaro, Stefano; Tasso, Carlo; Venuti, Fabio

2002-01-01

Discussion of search strategy in information retrieval by end users focuses on the role played by strategic reasoning and design principles for user interfaces. Highlights include strategic help based on collaborative coaching; a conceptual model for strategic help; and a prototype knowledge-based system named FIRE. (Author/LRW)

A probabilistic Sperner's theorem, with applications to the problem of retrieving information from a data base

NASA Technical Reports Server (NTRS)

Baumert, L. D.; Mceliece, R. J.; Rodemich, E. R.; Rumsey, H., Jr.

1978-01-01

The design of an optimal merged keycode data base information retrieval system is detailed. A probability distribution of n-bit binary words that minimized false drops was developed for the case where the set of desired records was a subset of tagged records.

Information Retrieval Using UMLS-based Structured Queries

PubMed Central

Fagan, Lawrence M.; Berrios, Daniel C.; Chan, Albert; Cucina, Russell; Datta, Anupam; Shah, Maulik; Surendran, Sujith

2001-01-01

During the last three years, we have developed and described components of ELBook, a semantically based information-retrieval system [1-4]. Using these components, domain experts can specify a query model, indexers can use the query model to index documents, and end-users can search these documents for instances of indexed queries.

NLPIR: A Theoretical Framework for Applying Natural Language Processing to Information Retrieval.

ERIC Educational Resources Information Center

Zhou, Lina; Zhang, Dongsong

2003-01-01

Proposes a theoretical framework called NLPIR that integrates natural language processing (NLP) into information retrieval (IR) based on the assumption that there exists representation distance between queries and documents. Discusses problems in traditional keyword-based IR, including relevance, and describes some existing NLP techniques.…

Three methods to retrieve slant total electron content measurements from ground-based GPS receivers and performance assessment

NASA Astrophysics Data System (ADS)

Zhang, Baocheng

2016-07-01

The high sampling rate along with the global coverage of ground-based receivers makes Global Positioning System (GPS) data particularly ideal for sensing the Earth's ionosphere. Retrieval of slant total electron content measurements (TECMs) constitutes a key first step toward extracting various ionospheric parameters from GPS data. Within the ionospheric community, the interpretation of TECM is widely recognized as the slant total electron content along the satellite receiver line of sight, biased by satellite and receiver differential code biases (DCBs). The Carrier-to-Code Leveling (CCL) has long been used as a geometry-free method for retrieving TECM, mainly because of its simplicity and effectiveness. In fact, however, the CCL has proven inaccurate as it may give rise to TECM very susceptible to so-called leveling errors. With the goal of attaining more accurate TECM retrieval, we report in this contribution two other methods than the CCL, namely, the Precise Point Positioning (PPP) and the Array-aided PPP (A-PPP). The PPP further exploits the International GPS Service (IGS) orbit and clock products and turns out to be a geometry-based method. The A-PPP is designed to retrieve TECM from an array of colocated receivers, taking advantage of the broadcast orbit and clock products. Moreover, A-PPP also takes into account the fact that the ionospheric effects measured from one satellite to all colocated receivers ought to be the same, thus leading to the estimability of interreceiver DCB. We perform a comparative study of the formal precision and the empirical accuracy of the TECM that are retrieved, respectively, by three methods from the same set of GPS data. Results of such a study can be used to assess the actual performance of the three methods. In addition, we check the temporal stability in A-PPP-derived interreceiver DCB estimates over time periods ranging from 1 to 3 days.

Triple-frequency radar retrievals of snowfall properties from the OLYMPEX field campaign

NASA Astrophysics Data System (ADS)

Leinonen, J. S.; Lebsock, M. D.; Sy, O. O.; Tanelli, S.

2017-12-01

Retrieval of snowfall properties with radar is subject to significant errors arising from the uncertainties in the size and structure of snowflakes. Recent modeling and theoretical studies have shown that multi-frequency radars can potentially constrain the microphysical properties and thus reduce the uncertainties in the retrieved snow water content. So far, there have only been limited efforts to leverage the theoretical advances in actual snowfall retrievals. In this study, we have implemented an algorithm that retrieves the snowfall properties from triple-frequency radar data using the radar scattering properties from a combination of snowflake scattering databases, which were derived using numerical scattering methods. Snowflake number concentration, characteristic size and density are derived using a combination of optimal estimation and Kalman smoothing; the snow water content and other bulk properties are then derived from these. The retrieval framework is probabilistic and thus naturally provides error estimates for the retrieved quantities. We tested the retrieval algorithm using data from the APR3 airborne radar flown onboard the NASA DC-8 aircraft during the Olympic Mountain Experiment (OLYMPEX) in late 2015. We demonstrated consistent retrieval of snow properties and smooth transition from single- and dual-frequency retrievals to using all three frequencies simultaneously. The error analysis shows that the retrieval accuracy is improved when additional frequencies are introduced. We also compare the findings to in situ measurements of snow properties as well as measurements by polarimetric ground-based radar.

A hierarchical knowledge-based approach for retrieving similar medical images described with semantic annotations

PubMed Central

Kurtz, Camille; Beaulieu, Christopher F.; Napel, Sandy; Rubin, Daniel L.

2014-01-01

Computer-assisted image retrieval applications could assist radiologist interpretations by identifying similar images in large archives as a means to providing decision support. However, the semantic gap between low-level image features and their high level semantics may impair the system performances. Indeed, it can be challenging to comprehensively characterize the images using low-level imaging features to fully capture the visual appearance of diseases on images, and recently the use of semantic terms has been advocated to provide semantic descriptions of the visual contents of images. However, most of the existing image retrieval strategies do not consider the intrinsic properties of these terms during the comparison of the images beyond treating them as simple binary (presence/absence) features. We propose a new framework that includes semantic features in images and that enables retrieval of similar images in large databases based on their semantic relations. It is based on two main steps: (1) annotation of the images with semantic terms extracted from an ontology, and (2) evaluation of the similarity of image pairs by computing the similarity between the terms using the Hierarchical Semantic-Based Distance (HSBD) coupled to an ontological measure. The combination of these two steps provides a means of capturing the semantic correlations among the terms used to characterize the images that can be considered as a potential solution to deal with the semantic gap problem. We validate this approach in the context of the retrieval and the classification of 2D regions of interest (ROIs) extracted from computed tomographic (CT) images of the liver. Under this framework, retrieval accuracy of more than 0.96 was obtained on a 30-images dataset using the Normalized Discounted Cumulative Gain (NDCG) index that is a standard technique used to measure the effectiveness of information retrieval algorithms when a separate reference standard is available. Classification results of more than 95% were obtained on a 77-images dataset. For comparison purpose, the use of the Earth Mover's Distance (EMD), which is an alternative distance metric that considers all the existing relations among the terms, led to results retrieval accuracy of 0.95 and classification results of 93% with a higher computational cost. The results provided by the presented framework are competitive with the state-of-the-art and emphasize the usefulness of the proposed methodology for radiology image retrieval and classification. PMID:24632078

A controlled nursing vocabulary for indexing and information retrieval.

PubMed

Pekkala, Eila; Saranto, Kaija; Tallberg, Marianne; Ensio, Anneli; Junttila, Kristiina

2006-01-01

The lack of a nursing thesaurus in Finnish has emerged among nursing professionals searching nursing knowledge and librarians when indexing literature to databases. The Finnish Nursing Education Society launched a project focusing on the development of a nursing vocabulary and the compilation of a thesaurus. The content of a vocabulary was created by six experts using Delphi-technique. The validity of the vocabulary was twice tested for indexing nursing research and has afterwards been revised. The vocabulary can be used for indexing and information retrieval purposes. The main challenge is that nurses easily can find national as well as international nursing research from databases and enhance research utilization.

INFORMATION RETRIEVAL EXPERIMENT. FINAL REPORT.

ERIC Educational Resources Information Center

SELYE, HANS

THIS REPORT IS A BRIEF REVIEW OF RESULTS OF AN EXPERIMENT TO DETERMINE THE INFORMATION RETRIEVAL EFFICIENCY OF A MANUAL SPECIALIZED INFORMATION SYSTEM BASED ON 700,000 DOCUMENTS IN THE FIELDS OF ENDOCRINOLOGY, STRESS, MAST CELLS, AND ANAPHYLACTOID REACTIONS. THE SYSTEM RECEIVES 30,000 PUBLICATIONS ANNUALLY. DETAILED INFORMATION IS REPRESENTED BY…

Hierarchical content-based image retrieval by dynamic indexing and guided search

NASA Astrophysics Data System (ADS)

You, Jane; Cheung, King H.; Liu, James; Guo, Linong

2003-12-01

This paper presents a new approach to content-based image retrieval by using dynamic indexing and guided search in a hierarchical structure, and extending data mining and data warehousing techniques. The proposed algorithms include: a wavelet-based scheme for multiple image feature extraction, the extension of a conventional data warehouse and an image database to an image data warehouse for dynamic image indexing, an image data schema for hierarchical image representation and dynamic image indexing, a statistically based feature selection scheme to achieve flexible similarity measures, and a feature component code to facilitate query processing and guide the search for the best matching. A series of case studies are reported, which include a wavelet-based image color hierarchy, classification of satellite images, tropical cyclone pattern recognition, and personal identification using multi-level palmprint and face features.

Where Is the Real Cheese? Young Children's Ability to Discriminate between Real and Pretend Acts

ERIC Educational Resources Information Center

Ma, Lili; Lillard, Angeline S.

2006-01-01

This study examined 2- to 3-year-olds' ability to make a pretend-real distinction in the absence of content cues. Children watched two actors side by side. One was really eating, and the other was pretending to eat, but in neither case was information about content available. Following the displays, children were asked to retrieve the real food…

Using the memory activation capture (MAC) procedure to investigate the temporal dynamics of hypothesis generation.

PubMed

Lange, Nicholas D; Buttaccio, Daniel R; Davelaar, Eddy J; Thomas, Rick P

2014-02-01

Research investigating top-down capture has demonstrated a coupling of working memory content with attention and eye movements. By capitalizing on this relationship, we have developed a novel methodology, called the memory activation capture (MAC) procedure, for measuring the dynamics of working memory content supporting complex cognitive tasks (e.g., decision making, problem solving). The MAC procedure employs briefly presented visual arrays containing task-relevant information at critical points in a task. By observing which items are preferentially fixated, we gain a measure of working memory content as the task evolves through time. The efficacy of the MAC procedure was demonstrated in a dynamic hypothesis generation task in which some of its advantages over existing methods for measuring changes in the contents of working memory over time are highlighted. In two experiments, the MAC procedure was able to detect the hypothesis that was retrieved and placed into working memory. Moreover, the results from Experiment 2 suggest a two-stage process following hypothesis retrieval, whereby the hypothesis undergoes a brief period of heightened activation before entering a lower activation state in which it is maintained for output. The results of both experiments are of additional general interest, as they represent the first demonstrations of top-down capture driven by participant-established WM content retrieved from long-term memory.

Image retrieval by information fusion based on scalable vocabulary tree and robust Hausdorff distance

NASA Astrophysics Data System (ADS)

Che, Chang; Yu, Xiaoyang; Sun, Xiaoming; Yu, Boyang

2017-12-01

In recent years, Scalable Vocabulary Tree (SVT) has been shown to be effective in image retrieval. However, for general images where the foreground is the object to be recognized while the background is cluttered, the performance of the current SVT framework is restricted. In this paper, a new image retrieval framework that incorporates a robust distance metric and information fusion is proposed, which improves the retrieval performance relative to the baseline SVT approach. First, the visual words that represent the background are diminished by using a robust Hausdorff distance between different images. Second, image matching results based on three image signature representations are fused, which enhances the retrieval precision. We conducted intensive experiments on small-scale to large-scale image datasets: Corel-9, Corel-48, and PKU-198, where the proposed Hausdorff metric and information fusion outperforms the state-of-the-art methods by about 13, 15, and 15%, respectively.

Learning semantic and visual similarity for endomicroscopy video retrieval.

PubMed

Andre, Barbara; Vercauteren, Tom; Buchner, Anna M; Wallace, Michael B; Ayache, Nicholas

2012-06-01

Content-based image retrieval (CBIR) is a valuable computer vision technique which is increasingly being applied in the medical community for diagnosis support. However, traditional CBIR systems only deliver visual outputs, i.e., images having a similar appearance to the query, which is not directly interpretable by the physicians. Our objective is to provide a system for endomicroscopy video retrieval which delivers both visual and semantic outputs that are consistent with each other. In a previous study, we developed an adapted bag-of-visual-words method for endomicroscopy retrieval, called "Dense-Sift," that computes a visual signature for each video. In this paper, we present a novel approach to complement visual similarity learning with semantic knowledge extraction, in the field of in vivo endomicroscopy. We first leverage a semantic ground truth based on eight binary concepts, in order to transform these visual signatures into semantic signatures that reflect how much the presence of each semantic concept is expressed by the visual words describing the videos. Using cross-validation, we demonstrate that, in terms of semantic detection, our intuitive Fisher-based method transforming visual-word histograms into semantic estimations outperforms support vector machine (SVM) methods with statistical significance. In a second step, we propose to improve retrieval relevance by learning an adjusted similarity distance from a perceived similarity ground truth. As a result, our distance learning method allows to statistically improve the correlation with the perceived similarity. We also demonstrate that, in terms of perceived similarity, the recall performance of the semantic signatures is close to that of visual signatures and significantly better than those of several state-of-the-art CBIR methods. The semantic signatures are thus able to communicate high-level medical knowledge while being consistent with the low-level visual signatures and much shorter than them. In our resulting retrieval system, we decide to use visual signatures for perceived similarity learning and retrieval, and semantic signatures for the output of an additional information, expressed in the endoscopist own language, which provides a relevant semantic translation of the visual retrieval outputs.

Directed Interaction Between Monkey Premotor and Posterior Parietal Cortex During Motor-Goal Retrieval from Working Memory

PubMed Central

Martínez-Vázquez, Pablo; Gail, Alexander

2018-01-01

Abstract Goal-directed behavior requires cognitive control of action, putatively by means of frontal-lobe impact on posterior brain areas. We investigated frontoparietal directed interaction (DI) in monkeys during memory-guided rule-based reaches, to test if DI supports motor-goal selection or working memory (WM) processes. We computed DI between the parietal reach region (PRR) and dorsal premotor cortex (PMd) with a Granger-causality measure of intracortical local field potentials (LFP). LFP mostly in the beta (12–32 Hz) and low-frequency (f≤10Hz) ranges contributed to DI. During movement withholding, beta-band activity in PRR had a Granger-causal effect on PMd independent of WM content. Complementary, low-frequency PMd activity had a transient Granger-causing effect on PRR specifically during WM retrieval of spatial motor goals, while no DI was associated with preliminary motor-goal selection. Our results support the idea that premotor and posterior parietal cortices interact functionally to achieve cognitive control during goal-directed behavior, in particular, that frontal-to-parietal interaction occurs during retrieval of motor-goal information from spatial WM. PMID:29481586

Directed Interaction Between Monkey Premotor and Posterior Parietal Cortex During Motor-Goal Retrieval from Working Memory.

PubMed

Martínez-Vázquez, Pablo; Gail, Alexander

2018-05-01

Goal-directed behavior requires cognitive control of action, putatively by means of frontal-lobe impact on posterior brain areas. We investigated frontoparietal directed interaction (DI) in monkeys during memory-guided rule-based reaches, to test if DI supports motor-goal selection or working memory (WM) processes. We computed DI between the parietal reach region (PRR) and dorsal premotor cortex (PMd) with a Granger-causality measure of intracortical local field potentials (LFP). LFP mostly in the beta (12-32 Hz) and low-frequency (f≤10Hz) ranges contributed to DI. During movement withholding, beta-band activity in PRR had a Granger-causal effect on PMd independent of WM content. Complementary, low-frequency PMd activity had a transient Granger-causing effect on PRR specifically during WM retrieval of spatial motor goals, while no DI was associated with preliminary motor-goal selection. Our results support the idea that premotor and posterior parietal cortices interact functionally to achieve cognitive control during goal-directed behavior, in particular, that frontal-to-parietal interaction occurs during retrieval of motor-goal information from spatial WM.

Private content identification based on soft fingerprinting

NASA Astrophysics Data System (ADS)

Voloshynovskiy, Sviatoslav; Holotyak, Taras; Koval, Oleksiy; Beekhof, Fokko; Farhadzadeh, Farzad

2011-02-01

In many problems such as biometrics, multimedia search, retrieval, recommendation systems requiring privacypreserving similarity computations and identification, some binary features are stored in the public domain or outsourced to third parties that might raise certain privacy concerns about the original data. To avoid this privacy leak, privacy protection is used. In most cases, privacy protection is uniformly applied to all binary features resulting in data degradation and corresponding loss of performance. To avoid this undesirable effect we propose a new privacy amplification technique that is based on data hiding principles and benefits from side information about bit reliability a.k.a. soft fingerprinting. In this paper, we investigate the identification-rate vs privacy-leak trade-off. The analysis is performed for the case of a perfect match between side information shared between the encoder and decoder as well as for the case of partial side information.

Preserved semantic access in global amnesia and hippocampal damage.

PubMed

Giovagnoli, A R; Erbetta, A; Bugiani, O

2001-12-01

C.B., a right-handed 33-year-old man, presented with anterograde amnesia after acute heart block. Cognitive abilities were normal except for serious impairment of long-term episodic memory. The access to semantic information was fully preserved. Magnetic resonance showed high signal intensity and marked volume loss in the hippocampus bilaterally; the left and right parahippocampal gyrus, lateral occipito-temporal gyrus, inferior temporal gyrus, and lateral temporal cortex were normal. This case underlines that global amnesia associated with hippocampal damage does not affect semantic memory. Although the hippocampus is important in retrieving context-linked information, its role is not so crucial in retrieving semantic contents. Cortical areas surrounding the hippocampus and lateral temporal areas might guide the recall of semantic information.

Mutual information based feature selection for medical image retrieval

NASA Astrophysics Data System (ADS)

Zhi, Lijia; Zhang, Shaomin; Li, Yan

2018-04-01

In this paper, authors propose a mutual information based method for lung CT image retrieval. This method is designed to adapt to different datasets and different retrieval task. For practical applying consideration, this method avoids using a large amount of training data. Instead, with a well-designed training process and robust fundamental features and measurements, the method in this paper can get promising performance and maintain economic training computation. Experimental results show that the method has potential practical values for clinical routine application.

Strategic retrieval, confabulations, and delusions: theory and data.

PubMed

Gilboa, Asaf

2010-01-01

Based on Moscovitch and Winocur's "working with memory" framework, confabulation is described as a deficit in strategic retrieval processes. The present paper suggests that only a confluence of deficits on multiple memory-related processes leads to confabulation. These are divided into three categories. Core processes that are unique to confabulation and required for its evolution include: (1) an intuitive, rapid, preconscious "feeling of rightness" monitoring, (2) an elaborate conscious "editor" monitoring, and (3) control processes that mediate the decision whether to act upon a retrieved memory. The second category is deficits on constitutional processes which are required for confabulation to occur but are not unique to it. These include the formation of erroneous memory representation, (temporal) context confusion, and deficits in retrieval cue generation. Finally, associated Features of confabulations determine the content "flavour" and frequency of confabulation but are not required for their evolution. Some associated features are magnification of normal reconstructive memory processes such as reliance on generic/schematic representations, and positivity biases in memory, whereas others are abnormal such as perseveration or source memory deficits. Data on deficits in core processes in confabulation are presented. Next, the apparent correspondences between confabulation and delusion are discussed. Considering confabulation within a strategic memory framework may help elucidate both the commonalities and differences between the two symptoms. Delusions are affected by a convergence of abnormal perception and encoding of information, associated with aberrant cognitive schema structure and disordered belief monitoring. Whereas confabulation is primarily a disorder of retrieval, mnemonic aspects of delusions can be described as primarily a disorder of input and integration of information. It is suggested that delusions might share some of the associated features of confabulation but not its core and constitutional processes. Preliminary data in support of this view are presented.

AEROMETRIC INFORMATION RETRIEVAL SYSTEM (AIRS) -GEOGRAPHIC, COMMON, AND MAINTENANCE SUBSYSTEM (GCS)

EPA Science Inventory

Aerometric Information Retrieval System (AIRS) is a computer-based repository of information about airborne pollution in the United States and various World Health Organization (WHO) member countries. AIRS is administered by the U.S. Environmental Protection Agency, and runs on t...

Semantics-driven modelling of user preferences for information retrieval in the biomedical domain.

PubMed

Gladun, Anatoly; Rogushina, Julia; Valencia-García, Rafael; Béjar, Rodrigo Martínez

2013-03-01

A large amount of biomedical and genomic data are currently available on the Internet. However, data are distributed into heterogeneous biological information sources, with little or even no organization. Semantic technologies provide a consistent and reliable basis with which to confront the challenges involved in the organization, manipulation and visualization of data and knowledge. One of the knowledge representation techniques used in semantic processing is the ontology, which is commonly defined as a formal and explicit specification of a shared conceptualization of a domain of interest. The work presented here introduces a set of interoperable algorithms that can use domain and ontological information to improve information-retrieval processes. This work presents an ontology-based information-retrieval system for the biomedical domain. This system, with which some experiments have been carried out that are described in this paper, is based on the use of domain ontologies for the creation and normalization of lightweight ontologies that represent user preferences in a determined domain in order to improve information-retrieval processes.

Remote Sensing of Vegetation Nitrogen Content for Spatially Explicit Carbon and Water Cycle Estimation

NASA Astrophysics Data System (ADS)

Zhang, Y. L.; Miller, J. R.; Chen, J. M.

2009-05-01

Foliage nitrogen concentration is a determinant of photosynthetic capacity of leaves, thereby an important input to ecological models for estimating terrestrial carbon and water budgets. Recently, spectrally continuous airborne hyperspectral remote sensing imagery has proven to be useful for retrieving an important related parameter, total chlorophyll content at both leaf and canopy scales. Thus remote sensing of vegetation biochemical parameters has promising potential for improving the prediction of global carbon and water balance patterns. In this research, we explored the feasibility of estimating leaf nitrogen content using hyperspectral remote sensing data for spatially explicit estimation of carbon and water budgets. Multi-year measurements of leaf biochemical contents of seven major boreal forest species were carried out in northeastern Ontario, Canada. The variation of leaf chlorophyll and nitrogen content in response to various growth conditions, and the relationship between them,were investigated. Despite differences in plant type (deciduous and evergreen), leaf age, stand growth conditions and developmental stages, leaf nitrogen content was strongly correlated with leaf chlorophyll content on a mass basis during the active growing season (r2=0.78). With this general correlation, leaf nitrogen content was estimated from leaf chlorophyll content at an accuracy of RMSE=2.2 mg/g, equivalent to 20.5% of the average measured leaf nitrogen content. Based on this correlation and a hyperspectral remote sensing algorithm for leaf chlorophyll content retrieval, the spatial variation of leaf nitrogen content was inferred from the airborne hyperspectral remote sensing imagery acquired by Compact Airborne Spectrographic Imager (CASI). A process-based ecological model Boreal Ecosystem Productivity Simulator (BEPS) was used for estimating terrestrial carbon and water budgets. In contrast to the scenario with leaf nitrogen content assigned as a constant value without differentiation between and within vegetation types for calculating the photosynthesis rate, we incorporated the spatial distribution of leaf nitrogen content in the model to estimate net primary productivity and evaportranspiration of boreal ecosystem. These regional estimates of carbon and water budgets with and without N mapping are compared, and the importance of this leaf biochemistry information derived from hyperspectral remote sensing in regional mapping of carbon and water fluxes is quantitatively assessed. Keywords: Remote Sensing, Leaf Nitrogen Content, Spatial Distribution, Carbon and Water Budgets, Estimation

Vegetation Water Content Mapping in a Diverse Agricultural Landscape: National Airborne Field Experiment 2006

NASA Technical Reports Server (NTRS)

Cosh, Michael H.; Jing Tao; Jackson, Thomas J.; McKee, Lynn; O'Neill, Peggy

2011-01-01

Mapping land cover and vegetation characteristics on a regional scale is critical to soil moisture retrieval using microwave remote sensing. In aircraft-based experiments such as the National Airborne Field Experiment 2006 (NAFE 06), it is challenging to provide accurate high resolution vegetation information, especially on a daily basis. A technique proposed in previous studies was adapted here to the heterogenous conditions encountered in NAFE 06, which included a hydrologically complex landscape consisting of both irrigated and dryland agriculture. Using field vegetation sampling and ground-based reflectance measurements, the knowledge base for relating the Normalized Difference Water Index (NDWI) and the vegetation water content was extended to a greater diversity of agricultural crops, which included dryland and irrigated wheat, alfalfa, and canola. Critical to the generation of vegetation water content maps, the land cover for this region was determined from satellite visible/infrared imagery and ground surveys with an accuracy of 95.5% and a kappa coefficient of 0.95. The vegetation water content was estimated with a root mean square error of 0.33 kg/sq m. The results of this investigation contribute to a more robust database of global vegetation water content observations and demonstrate that the approach can be applied with high accuracy. Keywords: Vegetation, field experimentation, thematic mapper, NDWI, agriculture.

Research of image retrieval technology based on color feature

NASA Astrophysics Data System (ADS)

Fu, Yanjun; Jiang, Guangyu; Chen, Fengying

2009-10-01

Recently, with the development of the communication and the computer technology and the improvement of the storage technology and the capability of the digital image equipment, more and more image resources are given to us than ever. And thus the solution of how to locate the proper image quickly and accurately is wanted.The early method is to set up a key word for searching in the database, but now the method has become very difficult when we search much more picture that we need. In order to overcome the limitation of the traditional searching method, content based image retrieval technology was aroused. Now, it is a hot research subject.Color image retrieval is the important part of it. Color is the most important feature for color image retrieval. Three key questions on how to make use of the color characteristic are discussed in the paper: the expression of color, the abstraction of color characteristic and the measurement of likeness based on color. On the basis, the extraction technology of the color histogram characteristic is especially discussed. Considering the advantages and disadvantages of the overall histogram and the partition histogram, a new method based the partition-overall histogram is proposed. The basic thought of it is to divide the image space according to a certain strategy, and then calculate color histogram of each block as the color feature of this block. Users choose the blocks that contain important space information, confirming the right value. The system calculates the distance between the corresponding blocks that users choosed. Other blocks merge into part overall histograms again, and the distance should be calculated. Then accumulate all the distance as the real distance between two pictures. The partition-overall histogram comprehensive utilizes advantages of two methods above, by choosing blocks makes the feature contain more spatial information which can improve performance; the distances between partition-overall histogram make rotating and translation does not change. The HSV color space is used to show color characteristic of image, which is suitable to the visual characteristic of human. Taking advance of human's feeling to color, it quantifies color sector with unequal interval, and get characteristic vector. Finally, it matches the similarity of image with the algorithm of the histogram intersection and the partition-overall histogram. Users can choose a demonstration image to show inquired vision require, and also can adjust several right value through the relevance-feedback method to obtain the best result of search.An image retrieval system based on these approaches is presented. The result of the experiments shows that the image retrieval based on partition-overall histogram can keep the space distribution information while abstracting color feature efficiently, and it is superior to the normal color histograms in precision rate while researching. The query precision rate is more than 95%. In addition, the efficient block expression will lower the complicate degree of the images to be searched, and thus the searching efficiency will be increased. The image retrieval algorithms based on the partition-overall histogram proposed in the paper is efficient and effective.

Towards an Intelligent Possibilistic Web Information Retrieval Using Multiagent System

ERIC Educational Resources Information Center

Elayeb, Bilel; Evrard, Fabrice; Zaghdoud, Montaceur; Ahmed, Mohamed Ben

2009-01-01

Purpose: The purpose of this paper is to make a scientific contribution to web information retrieval (IR). Design/methodology/approach: A multiagent system for web IR is proposed based on new technologies: Hierarchical Small-Worlds (HSW) and Possibilistic Networks (PN). This system is based on a possibilistic qualitative approach which extends the…

Information Retrieval System Design Issues in a Microcomputer-Based Relational DBMS Environment.

ERIC Educational Resources Information Center

Wolfram, Dietmar

1992-01-01

Outlines the file structure requirements for a microcomputer-based information retrieval system using FoxPro, a relational database management system (DBMS). Issues relating to the design and implementation of such systems are discussed, and two possible designs are examined in terms of space economy and practicality of implementation. (15…