Feature Matching Based on Top K Rank Similarity

Abstract

Feature matching plays a key component in many computer vision and pattern recognition tasks. Observing that the spatial neighborhood relationship (representing the topological structures of an image scene) is generally well preserved between two feature points of an image pair, some mismatch removing methods based on maintaining the local neighborhood structures of the potential true matches have been proposed. How to define the local neighborhood structure is an issue of vital importance. In this paper, we propose a robust and efficient method, called Top $K$ Rank Preservation (Top-KRP), for mismatch removal from given putative point set matching correspondences. Instead of preserving the intersection of neighbors, TopKRP aims at preserving the top $K$ rank of two feature points. The developed approach is validated on numerous challenging real image pairs for general feature matching, and the experimental results demonstrate that it outperforms several state-of-the-art feature matching methods, especially in case of a large number of mismatches.