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The task of a content-based image retrieval (CBIR) system is to cater to users who expect to get relevant images with high precision and efficiency in response to query images. This paper presents a concept learning approach that integrates a mixture model of the data, relevance feedback and long-term continuous learning. The concepts are incrementally refined with increased retrieval experiences. The concept knowledge can be immediately transplanted to deal with the dynamic database situationsdoi:10.1109/icme.2003.1221030 dblp:conf/icmcs/DongB03 fatcat:bk7vbclksjfz3givegpoqevdk4