A novel pair-wise image matching strategy with compact descriptors

In this paper, we address the problem of pair-wise image matching which determines whether two images depict the same objects or scenes. SIFT-like local descriptor-based matching is the most widely adopted method for this purpose and has achieved the state-of-the-art performance. However, local descriptor-based methods usually fail when an image pair contains multiple similar local regions. This problem becomes more serious when coming to limited computational and storage resources. Although global descriptors, e.g., Fisher Vectors, can solve this issue, it is difficult for global descriptors to distinguish images containing different objects of the same class. Therefore, we propose a novel strategy to integrate local and global descriptors for better matching accuracy. To further fulfill the efficiency requirement of applications, we combine dimension reduction and product quantization to obtain compact descriptors and speed up the matching process with pre-computed lookup tables. Extensive comparisons to the state-of-the-art methods demonstrate our advantages in both matching accuracy and efficiency.