Shape-based object matching using interesting points and high-order graphs

In shape-based object matching, it is important how to fuse similarities between points on a shape contour and the ones on another contour into the overall similarity. However, existing methods face two critical problems. Firstly, since most contour points are involved for possible matchings without taking into account the usefulness of each point, it causes high computational costs for point matching. Secondly, existing methods do not consider geometrical relations characterised by multiple points. In this paper, we propose a shape-based object matching method which is able to overcome these problems. To counteract the first problem mentioned, we devise a shape descriptor using a small number of interesting points which are generated by considering both curvatures and the overall shape trend. We also introduce a simple and highly discriminative point descriptor, namely Point Context, which represents the geometrical and topological location of each interesting point. For the second problem, we employ high-order graph matching which examines similarities for singleton, pairwise and triple relations of points. We validate the robustness and accuracy of our method through a series of experiments on six datasets.