Robust feature matching using guided local outlier factor

Abstract

Matching local features on two or more images is fundamental for many applications in the field of computer vision and pattern recognition. Identifying and rejecting mismatches is an important part in the framework of feature matching, due to the putative correspondences always contaminated by mismatches with the error-prone local feature detectors. In this paper, we introduce a novel method, namely Guided Local Outlier Factor (GLOF) for feature matching with gross mismatches under multi-granularity neighborhood structure-preserving. We first construct a tentative correspondence set by matching multi-features. Then, we identify and remove mismatches. Inspired by the anomaly detection technique, putative correspondences are assigned to a particular score, so abnormal instances, i.e., mismatches can be classified by a user-defined threshold. More specially, the neighborhood preserving guides the local searching procedure. Moreover, to eliminate the fluctuation of the matching results with different sizes of local neighbors, we use the multi-granularity algorithm to average out the deviation. Experimental results demonstrate that the introduced approach is superior to several state-of-the-art methods in terms of mismatch rejection on publicly available datasets.