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

ThefieldofContent-BasedVisualInformationRetrieval(CBVIR)hasexperiencedtremendousgrowth 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, queryby-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.

• Type: Journal
• Year: 2002
• Where: MTA
• Authors: Oge Marques, Borko Furht
• Keywords: Images, MTA 2002, Relevance Feedback, Relevance Feedback Capabilities