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Published in:
2007 | OriginalPaper | Chapter
Shape Classification Based on Skeleton Path Similarity
Authors : Xingwei Yang, Xiang Bai, Deguang Yu, Longin Jan Latecki
Published in: Energy Minimization Methods in Computer Vision and Pattern Recognition
Publisher: Springer Berlin Heidelberg
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Most of the traditional methods for shape classification are based on contour. They often encounter difficulties when dealing with classes that have large nonlinear variability, especially when the variability is structural or due to articulation. It is well-known that shape representation based on skeletons is superior to contour based representation in such situations. However, approaches to shape similarity based on skeletons suffer from the instability of skeletons and matching of skeleton graphs is still an open problem. Using a skeleton pruning method, we are able to obtain stable pruned skeletons even in the presence of significant contour distortions. In contrast to most existing methods, it does not require converting of skeleton graphs to trees and it does not require any graph editing. We represent each shape as set of shortest paths in the skeleton between pairs of skeleton endpoints. Shape classification is done with Bayesian classifier. We present excellent classification results for complete shape.