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An MCMC-based prior sub-hypergraph matching in presence of outliers

Correspondence problems are very challenging due to the complexity of real-world scenes. Some hypergraph matching methods have been proposed for improving the recall of the solution, but the numerous outliers are brought since the precision is rarely considered. To solve this issue, we propose a sub-hypergraph matching method, which is robust with better integration of geometric information and reduces the difficulty of NP-hard problem happened in hypergraphs. To narrow the search space and solve the optimization problem, a new prior strategy and cell-algorithm in Markov Chain Monte Carlo (MCMC) framework is proposed on sub-hypergraph matching. The experiments show that our proposed method significantly outperforms other state-of-the-art algorithms.