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Recently, keypoint descriptors such as Scale Invariant Feature Transform (SIFT) have been proved promising in similarity retrieval of images, which adopts matching score as similarity. However, the matching score is easy to be decreased once there are little variances between image details, and hence lead to low retrieval performance. In this paper, we propose a novel retrieval approach that improves the matching score with reduced time of matching by Kernel-based Fuzzy C-Means clustering (KFCM), which proves to be a better trade-off between matching and retrieval precision. Experiments conducted on three representative image databases show that our retrieval approach is surprisingly effective, outperforming the SIFT based method, not only in object-based image retrieval but also for searching scenes with similar semantic.