Case Study: MUSE
In this chapter we present MUSE (MUltimedia SEarch and Retrieval Using Relevance Feedback), a CBVIR system with relevance feedback and learning capabilities developed by the authors over the past two years. MUSE is an ongoing project within the Department of Computer Science and Engineering at Florida Atlantic University. The ultimate goal of this project is to build an intelligent system for searching and retrieving visual information in large repositories1.
KeywordsFeature Vector Cluster Algorithm Target Image Query Image Relevance Feedback
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