Ranking list preservation for feature matching

Feature matching plays a very important role in many computer vision and pattern recognition tasks. The spatial neighborhood relationship (representing the topological structures of some key feature points of an image scene) is generally well preserved between two feature points of an image pair. Several mismatch-removing methods that maintain the local neighborhood structures of potential true matches have been proposed. Defining local neighborhood structures is a crucial issue in the feature matching problem. In this paper, we propose a robust and efficient topological structure measurement called top K rank preservation (TopKRP) for mismatch removal from given putative point set. We transform feature points from the feature space to the ranking list space. Thus, the topological structure similarity of two feature points can be simply calculated by comparing their ranking lists, which are measured by the top K ranking similarity based on the spatial Euclidean distance as well as the angle correlation. TopKRP is validated on 10 public image pairs with typical scenes and 2 artificially established datasets, namely, MI52 and RS153. Experimental results demonstrate that the proposed approach outperforms several state-of-the-art feature matching methods, especially when the number of mismatches is large. (C) 2020 Elsevier Ltd. All rights reserved.

Automated summary

Feature matching is crucial in computer vision and pattern recognition, with preserving the local neighborhood structures of feature points being a key issue. This paper introduces a topological structure measurement method called TopKRP for efficient mismatch removal.