No abstract

No abstract

No abstract

No abstract

This paper proposes a novel object matching algorithm based on shape contours. In order to ensure low computational complexity in shape representation, our descriptor is composed by a small number of interest points which are generated by considering both curvatures and the overall shape trend. To effectively describe each point of interest, we introduce a simple and highly discriminative point descriptor, namely Point Context, which represents its geometrical and topological location. For shape matching, we observed that the correspondences are not only dependent on the similarities between these single points in different objects, but they are also related to the geometric relations between multiple points of interest in the same object. Therefore, a high-order graph matching formulation is introduced to merge the single point similarities and the similarities between point triangles. The main contributions of this paper include (i) the introduction of a novel shape descriptor with robust shape points and their descriptors and (ii) the implementation of a high-order graph matching algorithm that solves the shape matching problem. Our method is validated through a series of object retrieval experiments on four datasets demonstrating its robustness and accuracy.