The hypergraph matching based on CCRP 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.

Iterative Graph Alignment via Supermodular Approximation Graph matching, the problem of aligning a pair of graphs so as to minimize their edge disagreements, has received widespread attention owing to its broad spectrum of applications in data science. As the problem is NPxe2x80x93hard in the worstcase, a variety of approximation algorithms have been proposed for obtaining high quality, suboptimal solutions. In this paper, we approach the task of designing an efficient polynomialtime approximation algorithm for graph matching from a previously unconsidered perspective. Our key result is that graph matching can be formulated as maximizing a monotone, supermodular set function subject to matroid intersection constraints. We leverage this fact to apply a discrete optimization variant of the minorizationmaximization algorithm which exploits supermodularity of the objective function to iteratively construct and maximize a sequence of global lower bounds on the objective. At each step, we solve a maximum weight matching problem in a bipartite graph. Differing from prior approaches, the algorithm exploits the combinatorial structure inherent in the problem to generate a sequence of iterates featuring monotonically nondecreasing objective value while always adhering to the combinatorial matching constraints. Experiments on realworld data demonstrate the empirical effectiveness of the algorithm relative to the prevailing stateoftheart.