Scalable Multi-Consistency Feature Matching with Non-Cooperative Games

Correspondence selection aiming at seeking correct relationships between two images is a fundamental and critical task in computer vision. This paper attempts to select consistent correspondences in the context of dynamic scenarios where multiple matching consistencies are normally incorporated. To this end, we present a grid-based game-theoretic matching (Grid-GTM) method which is divided into three processes, i.e., grid matching, local games and enrichment. Specifically, grid matching translates the multi-consistency problem into several independent single-consistency problems to decrease difficulties of selection and boost the efficiency. Local games extended under the guidance of a novel payoff function guarantee that mismatches are effectively removed. Enrichment is added to recover correct matches neglected by local games. Crucially, our approach achieves the state-of-the-art performance compared with seven algorithms in comprehensive evaluations. In addition, we construct a dataset that involves multiple consistencies under three different scenes in this paper.