A Keypoint Matching Method Based on Hierarchical Learning

Keypoint matching is an important task of computer vision and the major problem is to find a fast and robust keypoints algorithm. This paper presents a binary descriptor matching algorithm based on hierarchical learning method. The descriptor learning process is divided into two levels of coarse and fine, which combines the advantages of the fixed-point sampling mode and random sampling mode, and the process enhances the performance of learning. Meanwhile, a more reasonable point-pair identification model is built and applied into the keypoint matching algorithm which improves the matching precision. Experimental results demonstrate that the proposed algorithm outperforms the classical methods with lower computation time.