Shape matching and recognition using group-wised points

Abstract

Shape matching/recognition is a very critical problem in the field of computer vision, and a lot of descriptors and methods have been studied in the literature. However, based on predefined descriptors, most of current matching stages are accomplished by finding the optimal correspondence between every two contour points, i.e., in a pair-wised manner. In this paper, we provide a novel matching method which is to find the correspondence between groups of contour points. The points in the same group are adjacent to each other, resulting in a strong relationship among them. Two groups are considered to be matched when the two point sequences formed by the two groups lead to a perfect one-to-one mapping. The proposed group-wised matching method is able to obtain a more robust matching result, since the co-occurrence (order) information of the grouped points is used in the matching stage. We test our method on three famous benchmarks: MPEG-7 data set, Kimia's data set and Tari1000 data set. The retrieval results show that the new group-wised matching method is able to get encouraging improvements compared to some traditional pair-wised matching approaches. © 2011 Springer-Verlag.