Classification of contour shapes using class segment sets

Both example-based and model-based approaches for classifying contour shapes can encounter difficulties when dealing with classes that have large nonlinear variability, especially when the variability is structural or due to articulation. This paper proposes a part-based approach to address this problem. Bayesian classification is performed within a three-level framework, which consists of models for contour segments, for classes, and for the entire database of training examples. The class model enables different parts of different exemplars of a class to contribute to the recognition of an input shape. The method is robust to occlusion and is invariant to planar rotation, translation, and scaling. Furthermore, the method is completely automated. It achieves 98% classification accuracy on a large database with many classes.