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Relevance feedback and region-based representations are two effective ways to improve the accuracy of content-based image retrieval systems. Although these two techniques have been successfully investigated and developed in the last few years, little attention has been paid to combining them together. We argue that integrating these two approaches and allowing them to benefit from each other will yield better performance than using either of them alone. To do that, on the one hand, two relevance feedback algorithms are proposed based on region representations. One is inspired from the query point movement method. By assembling all of the segmented regions of positive examples together and reweighting the regions to emphasize the latest ones, a pseudo image is formed as the new query. An incremental clustering technique is also considered to improve the retrieval efficiency. The other is the introduction of existing support vector machine-based algorithms. A new kernel is proposed so as to enable the algorithms to be applicable to region-based representations. On the other hand, a rational region weighting scheme based on users' feedback information is proposed. The region weights that somewhat coincide with human perception not only can be used in a query session, but can also be memorized and accumulated for future queries. Experimental results on a database of 10 000 general-purpose images demonstrate the effectiveness of the proposed framework.