Block-based image matching for image retrieval

Due to the lighting, translation, scaling and rotation, image matching is a challenge task in computer vision area. In the past decades, local descriptors (e.g. SIFT, SURF and HOG, etc.) and global features (e.g. HSV, CNN, etc.) play a vital role for this task. However, most image matching methods are based on the whole image, i.e., matching the entire image directly base on some image representation methods (e.g. BoW, VLAD and deep learning, etc.). In most situations, this idea is simple and effective, but we recognize that a robust image matching can be realized based on sub-images. Thus, a block-based image matching algorithm is proposed in this paper. First, a new local composite descriptor is proposed, which combines the advantages of local gradient and color features with spatial information. Then, VLAD method is used to encode the proposed composite descriptors in one block, and block-CNN feature is extracted at the same time. Second, a block-based similarity metric is proposed for similarity calculation of two images. Finally, the proposed methods are verified on several benchmark datasets. Compared with other methods, experimental results show that our method achieves better performance.