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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Marques, O., Furht, B. MUSE: A Content-Based Image Search and Retrieval System Using Relevance Feedback. Multimedia Tools and Applications 17, 21–50 (2002). https://doi.org/10.1023/A:1014679605305
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DOI: https://doi.org/10.1023/A:1014679605305