Robust SIFT-based feature matching using Kendall's rank correlation measure

Abstract

The scale invariant feature transform, SIFT, is one of the most efficient image matching techniques based on local features. It has been applied to various scientific domains such as machine vision, robot navigation, object recognition, etc. In this work, a SIFT improvement is proposed that makes feature matching more robust in the presence of different types of image noise. Thus, Kendall's rank correlation measure is employed to improve the performance of feature matching. Its exploitation reduces the number of erroneous SIFT feature matches without adding significantly to the execution time. The results of the SIFT improvement are validated through matching examples between similar images.