Efficient and Accurate Hypergraph Matching

Feature matching is used to match features in the model image to their correspondences in the test image. Hypergraph matching makes use of the relationship among multiple features to improve matching accuracy, and existing algorithms usually achieve this aim by maximizing the matching score between features. In this paper we transform hypergraph matching of features to hypergraph clustering of candidate matches, which is then solved in the scenario of a multi-player clustering game. Noticing that the number of matches generated by this algorithm is usually small, we discuss the reason and present a density based group expansion method to increase the number of matches. Furthermore, we enforce the one-to-one constraint to maintain a high matching accuracy. Experiments on three real datasets show that our algorithm is able to generate a large number of matches efficiently without degrading the matching accuracy evidently.