Robust Feature Matching via Advanced Neighborhood Topology Consensus

Abstract Feature matching is one of the key techniques in many vision-based tasks, which aims to establish reliable correspondences between two sets of features. In this paper, we present a new feature matching method, which formulates the matching of two feature sets as a mathematical model based on two common consistency constraints. We first propose an advanced consensus of neighborhood topology, which can better exploit the consensus of topological structures to identify inliers. In order to have reliable neighborhood information for the feature points, a subset with high percentage inliers obtained by a guided matching strategy from the putative matches for the neighborhood construction is used. We demonstrate the advantages of our proposed method on various real image pairs. The results demonstrate that the proposed method is superior to the state-of-the-art feature matching methods.