Contour Guided Hierarchical Model for Shape Matching

Yuanqi Su, Yuehu Liu, Bonan Cuan, Nanning Zheng; Proceedings of the IEEE International Conference on Computer Vision (ICCV), 2015, pp. 1609-1617

For its simplicity and effectiveness, star model is popular in shape matching. However, it suffers from the loose geometric connections among parts. In the paper, we present a novel algorithm that reconsiders these connections and reduces the global matching to a set of interrelated local matching. For the purpose, we divide the shape template into overlapped parts and model the matching through a part-based layered structure that uses the latent variable to constrain parts' deformation. As for inference, each part is used for localizing candidates by the partial matching. Thanks to the contour fragments, the partial matching can be solved via modified dynamic programming. The overlapped regions among parts of the template are then explored to make the candidates of parts meet at their shared points. The process is fulfilled via a refined procedure based on iterative dynamic programming. Results on ETHZ shape and Inria Horse datasets demonstrate the benefits of the proposed algorithm.