MUSE: A Content-Based Image Search and Retrieval System Using Relevance Feedback


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Abstract

The field of Content–Based Visual Information Retrieval (CBVIR) has experienced tremendous growth in the recent years and many research groups are currently working on solutions to the problem of finding a desired image or video clip in a huge archive without resorting to metadata. This paper describes the ongoing development of a CBVIR system for image search and retrieval with relevance feedback capabilities. It supports browsing, query–by–example, and two different relevance feedback modes that allow users to refine their queries by indicating which images are good or bad at each iteration.

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