A hierarchical shape tree for shape classification

This paper presents a novel approach to hierarchical shape classification. We combine two shape features: contour and skeleton. Weights of two features are learned through large-margin optimization. The proposed approach uses a shape tree to efficiently represent the similarity of different shape classes. The tree is generated offline by a bottom-up clustering approach using stochastic optimization. A coarse-to-fine matching strategy is adopted to have a high classification accuracy. Bayesian classifier is used to perform the final decision. The proposed method was tested in a variety of challenging shape datasets. The results show great improvement over many previous algorithms.