Content-based image retrieval (CBIR) is a research area dedicated to address the retrieve and search multimedia documents for digital libraries. Relevance feedback is a powerful techniques in CBIR and has been an active research topic for the past few years. In this paper, we review the current state-of-the-art of research on relevance feedbacks for CBIR and present the iFind system developed at Microsoft Research China equipped with a set of powerful relevance feedback algorithms. We also provide an outlook on the remaining research issues in CBIR, especially on applying learning and data mining technologies in search of multimedia data on the Web.
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