More effective image matching with Scale Invariant Feature Transform

Abstract

Feature matching is based on finding reliable corresponding points in the images. This requires to solve a twofold problem: detecting repeatable feature points and describing them as distinctive as possible. SIFT (Scale Invariant Feature Transform) has been proven to be the most reliable solution to this problem. It combines a scale invariant detector and a very robust descriptor based on gray image gradients. Even if in general the detected SIFT feature points have a repeatability score greater than 40 %, an important proportion of them are not identified as good corresponding points by the SIFT matching procedure. In this paper we introduce a new and effective method that increases the number of valid corresponding points. To improve the distinctness of the original SIFT descriptor the color information is considered. In our method we compute the cross correlation and the histogram intersection between neighbour keypoints regions that has been adapted to scale and rotation. Finally, the experimental results prove that our method outperforms the original matching method.