A shape matching framework using metric partition constraint

The crucial problem for shape matching is to balance between discrimination power and computation complexity. Popular solutions mainly rely on either global or local information of shape contours, and neglect their intrinsic correlation. But the methods that combine both information may bring high computation complexity. In this paper, we present a shape matching framework, in which a novel shape descriptor named metric partition constraint (MPC) is proposed, and many metric methods can be included. The metric information is used to bridge the local points and the global shape. Meanwhile, we devise a partition smoothing process to improve the robustness to local deformation. Finally, Comprehensive comparisons with the classical shape context and other latest methods on standard datasets show the excellent performance in terms of precision while retaining computational efficiency.