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This paper proposes an interactive approach for region-based image clustering and retrieval. By performing clustering before image retrieval, the search space can be reduced to those clusters that are close to the query target. First, the image is segmented to regions by using an unsupervised segmentation method. This is an area where a vast number of regions are involved. To reduce search space for region-based image retrieval, we use clustering based on genetic algorithm. Fuzzy similarity is used in order to compute the similarity of two images. Moreover, a two-class SVM is trained based on user interests to improve image retrieval. Experiments were performed on COREL image database and show the effectiveness of the proposed approach.