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This paper provides a novel content-based image retrieval algorithm based on ROI (Region Of Interest) by using SIFT (Scale Invariant Feature Transform) feature matching. SIFT descriptors, which are invariant to image scaling and transformation and rotation, and partially invariant to illumination changes and affine, present the local features of an image. Therefore, feature keypoints can be extracted more accurately by using SIFT from user-defined ROI of an image than color, texture, shape and spatial relations feature. To decrease unavailable features matching, a dynamic probability function replaces the original fixed value to determine the similarity distance of ROI and database from training images. These are the kernel of content-based image retrieval. The experimental results show that this method improves the stability and precision of image retrieval.