Shape Retrieval and Classification Based on Geodesic Paths in Skeleton Graphs

Skeleton- is well-known to be superior to contour-based representation when shapes have large nonlinear variability, especially articulation. However, approaches to shape similarity based on skeletons suffer from the instability of skeletons, and matching of skeleton graphs is still an open problem. To deal with this problem for shape retrieval, the authors first propose to match skeleton graphs by comparing the geodesic paths between skeleton endpoints. In contrast to typical tree or graph matching methods, they do not explicitly consider the topological graph structure. Their approach is motivated by the fact that visually similar skeleton graphs may have completely different topological structures, while the paths between their end nodes still remain similar. The proposed comparison of geodesic paths between endpoints of skeleton graphs yields correct matching results in such cases. The experimental results demonstrate that the method is able to produce correct results in the presence of articulations, stretching, and contour deformations. The authors also utilize the geodesic skeleton paths for shape classification. Similar to shape retrieval, direct graph matching algorithms like graph edit distance have great difficulties with the instability of the skeleton graph structure. In contrast, the representation based on skeleton paths remains stable. Therefore, a simple Bayesian classifier is able to obtain excellent shape classification results.