The Web-based Medical Information retrieval System (WebMIRS) allows Internet access to databases containing 17,000 digitized x-ray spine images and associated text data from National Health and Nutrition Examinations ...
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The Web-based Medical Information retrieval System (WebMIRS) allows Internet access to databases containing 17,000 digitized x-ray spine images and associated text data from National Health and Nutrition Examinations 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 imageretrieval 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.
video-on-Demand systems have received a good deal of attention recently. Few studies, however, have addressed the problem of locating a video of interest in a large video database. This paper describes the design and ...
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ISBN:
(纸本)0819414808
video-on-Demand systems have received a good deal of attention recently. Few studies, however, have addressed the problem of locating a video of interest in a large video database. This paper describes the design and implementation of a metadata database and query interface that attempts to solve this information retrieval problem.
The temporal and multi-modal nature of video increases the dimensionality of content based retrieval problem. This places new demands on the indexing and retrieval tools required. The virage video Engine (VVE) with th...
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ISBN:
(纸本)0819424331
The temporal and multi-modal nature of video increases the dimensionality of content based retrieval problem. This places new demands on the indexing and retrieval tools required. The virage video Engine (VVE) with the default set of primitives provide the necessary frame work and basic tools for video content based retrieval. The video engine is a flexible platform independent architecture which provides support for processing multiple synchronized data streams like image sequences, audio and closed captions. The architecture allows for multi-modal indexing and retrieval of video through the use of media specific primitives. This paper presents the use of the VVE framework for content based videoretrieval.
The detection of shot boundaries in video sequences is an important task for generating indexed videodatabases. This paper provides a comprehensive quantitative comparison of the metrics that have been applied to sho...
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The detection of shot boundaries in video sequences is an important task for generating indexed videodatabases. This paper provides a comprehensive quantitative comparison of the metrics that have been applied to shot boundary detection. In addition, several standardized statistical tests that have not been applied to this problem, and three new metrics, are considered. A mathematical framework for quantitatively comparing metrics is supplied. Experimental results based on a video database containing 39,000 frames are included.
In this paper, we present an approach to clustering video sequences and images for efficient retrieval using relative entropy as our cost criterion. In addition, our experiments indicate that relative entropy is a goo...
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In this paper, we present an approach to clustering video sequences and images for efficient retrieval using relative entropy as our cost criterion. In addition, our experiments indicate that relative entropy is a good similarity measure for content-based retrieval. In our clustering work, we treat images and video as probability density functions over the extracted features. This leads us to formulate a general algorithm for clustering densities. In this context, it can be seen that an euclidean distance between features and the Kullback-Liebler (KL) divergence give equivalent clustering. In addition, the asymmetry of the KL divergence leads to another clustering. Our experiments indicate that this clustering is more robust to noise and distortions compared with the one resulting from euclidean norm.
A new system, the so-called MUviS, is introduced for content-based indexing and retrieval for image database management systems. In addition to traditional indexing by key words, MUviS allows indexing of objects and i...
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A new system, the so-called MUviS, is introduced for content-based indexing and retrieval for image database management systems. In addition to traditional indexing by key words, MUviS allows indexing of objects and images based on color, texture, shape and objects layout inside them. Due to the use of large vector features, the pyramid trees are employed to create the index structure.
The development of increasingly complex multimedia applications calls for new methodologies for the organization and retrieval of still images and video sequences. Query and retrieval methods based on image content pr...
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ISBN:
(纸本)0819424331
The development of increasingly complex multimedia applications calls for new methodologies for the organization and retrieval of still images and video sequences. Query and retrieval methods based on image content promise good results, are currently widely investigated and, to some extent, already commercially available. Yet a large number of issues remain unsolved. In this paper we describe some results of a study on similarity evaluation in imageretrieval using color, object orientation and relative position as content features. A simple prototype system is also introduced that computes the feature descriptors and performs queries. Although not trivial, the features extraction process is completely automated and requires no user intervention. The system is admittedly not a general purpose tool, but is oriented to thematic image repositories where the semantics of stored images are limited to a specific domain.
Histograms are the most prevalently used representation for the color content of images and video. An elaborate representation of the histograms requires specifying the color centers of the histogram bins and the coun...
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ISBN:
(纸本)0819435902
Histograms are the most prevalently used representation for the color content of images and video. An elaborate representation of the histograms requires specifying the color centers of the histogram bins and the count of the number of image pixels with that color. Such an elaborate representation, though expressive, may not be necessary for some tasks in image search, filtering and retrieval. A qualitative representation of the histogram is sufficient for many applications. Such a representation will be compact and greatly simplify the storage and transmission of the image representation. It will also reduce the computational complexity of search and filtering algorithms without adversely affecting the quality. We present such a compact binary descriptor for color representation. This descriptor is the quantized Haar transform coefficients of the color histograms. We show the use of this descriptor for fast retrieval of similar images and search for similar video segments from a large database. We also show the use of this descriptor for browsing large imagedatabases without the need for computationally expensive clustering algorithms. The compact nature of the descriptor and the associated simple similarity measure allows searching over a database of about four hours of video in less than 5-6 seconds without the use of any sophisticated indexing scheme.
A different approach to content-based retrieval and a novel framework for classification of visual information are proposed. The visual Apprentice which is an implementation of the framework for still images and video...
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A different approach to content-based retrieval and a novel framework for classification of visual information are proposed. The visual Apprentice which is an implementation of the framework for still images and video that uses a combination of lazy-learning, decision trees, and evolution programs for classification and grouping is introduced. Examples and results are given to demonstrate the applicability of the proposed approach to perform visual classification and detection.
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