Many image retrieval systems adopt the bag-of-words model and rely on matching of local descriptors. However, these descriptors of keypoints, such as SIFT, may lead to false matches, since they do not consider the contextual information of the keypoints. In this paper, we incorporate the cues of meaningful regions where local descriptors are extracted. We describe a matching region estimation (MRE) method to find appropriate matching regions for local descriptor matching pairs. Then the region matching quality is evaluated and the true matched regions will enhance the similarity of local descriptors. Consequently, the image retrieval accuracy can be improved. Extensive experiments on benchmark datasets show the effectiveness of our method and our result compares favorably with the state-of-the-art.