Image Registration Using SIFT and Canonical Correlation Analysis

Abstract

To improve the quality of SIFT feature matching, a mismatches removal method is proposed based on the collinearity property of the canonical correlation features. The influence of each match on the collineartiy degree is analyzed to find false matches. Firstly, a putative set of matches is obtained based on distances between SIFT feature descriptors. Secondly, the spatial relationship of matched points is encoded by canonical correlation analysis. The match with the largest effect on the collinearity degree is iteratively eliminated. Experimental results show the proposed method can reserve a high rate of correct matches determined by a threshold. Compared with several other mismatches removal methods, it provides comparable or better results.