Abstract
Hypergraph matching plays an important role in the development of computer vision and has gained more and more attention. Although many algorithms have been generated, it is always difficult to solve the mathematical model of the hypergraph matching. Most algorithms use approximately local optimal solution to replace the global optimal solution of objective function. This will cause the hypergraph matching is unable to obtain the optimal solution in limited time. In order to solve this problem, we propose a new hypergraph matching algorithm based on CCRP to overcome the relaxation problem. We compare the proposed method with several excellent matching algorithms in three common benchmarks including Synthetic, CMU House and Willow. Our experimental results demonstrate the superiority of our new algorithm in matching accuracy and robustness against noise and deformation.
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