A novel hypergraph matching algorithm based on tensor refining

Abstract

Hypergraph matching utilizes high order constraints rather than unary or pairwise ones, which aims to establish a more reliable correspondence between two sets of image features. Although many hypergraph matching methods have been put forward over the past decade, it remains a challenging problem to be solved due to its combinatorial nature. Most of these methods are based on tensor marginalization, where tensor entries representing joint probabilities of the assignment are fixed during the iterations meanwhile the individual assignment probabilities evolving. This will cause some incomplete information which may hurt the matching performance. Addressing this issue, we propose a novel hypergraph matching algorithm based on tensor refining, accompanied with an alternative adjustment method to accelerate the convergence. We make a comparison between the proposed approach and several outstanding matching algorithms on three commonly used benchmarks. The experimental results validate the superiority of our method on both matching accuracy and robustness against noise and deformation.