Mining consistent correspondences using co-occurrence statistics

Abstract

In this paper, we propose a mismatch removal method, which mines consistent image feature correspondences using co-occurrence statistics. The proposed method relies on a co-occurrence matrix that counts the number of pixel value pairs co-occurring within the images. Specifically, we propose to integrate the co-occurrence statistics with local spatial information, to preserve the consensus of neighborhood elements. Then, a new measure based on co-occurrence statistics is defined for correspondence similarity, to preserve the consensus of neighborhood topology. After that, with the consensus of neighborhood elements and neighborhood topology, the mismatch removal problem is formulated into a mathematical model, which has a closed-form solution. Extensive experiments show that the proposed method is able to achieve superior or competitive performance on matching accuracy over several state-of-the-art competing methods. In addition, we further exploit the consensus of neighborhood elements and neighborhood topology to propose a novel guided sampling method, which can significantly improve the quality of sampling minimal subsets over state-of-the-arts for two-view geometric model fitting.