Feature Matching Based on Minimum Relative Motion Entropy for Image Registration

Abstract

Accurate point matching is widely used, and it is a critical and challenging process in feature-based image registration. To improve feature matching accuracy on putative matches with heavy outliers and similar local structures, an accurate and robust feature point matching algorithm based on minimum relative motion entropy (MRME) is proposed, in which the relative motion between the putative matches and their K-nearest neighbors is formulated. Based on the relative motion clustering result, the relative motion entropy is defined to find the coincident relative motions. According to relative motions with MRME, the outliers are removed in a two-stage feature match strategy. With quasi-linear time complexity, outliers with random or irregular relative motion are removed efficiently and accurately, while inliers with coincident relative motion are retained. Three data sets with repetitive patterns, viewpoint changes, low overlapping areas, and local deformations are used to demonstrate the performance of the proposed algorithm. MRME is shown to be more robust and accurate than ten state-of-the-art feature matching algorithms.