Visually Searching the Web for Content
IEEE MultiMedia
Content-Based Similarity Assessment in Multi-segmented Medical Image Data Bases
MLDM '01 Proceedings of the Second International Workshop on Machine Learning and Data Mining in Pattern Recognition
Finding related functional neuroimaging volumes
Artificial Intelligence in Medicine
Extract and rank web communities
Proceedings of the 3rd International Conference on Web Intelligence, Mining and Semantics
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Abstract (white paper presented at the NSF Workshop on Visual Information Management, MIT, June 1995) We propose the development of a world wide web image search engine that crawls the web collecting information about the images it finds, computes the appropriate image decompositions and indices, and stores this extracted information for searches based on image content. Indexing and searching images need not require solving the image understanding problem. Instead, the general approach should be to provide an arsenal of image decompositions and discriminants that can be precomputed for images. At search time, users can select a weighted subset of these decompositions to be used for computing image similarity measurements. While this approach avoids the search-time-dependent problem of labeling what is important in images, it still holds several important problems that require further research in the area of query by image content. We briefly explore some of these problems as they pertain to shape.