Objects Similarity Measurement Based on Skeleton Tree Descriptor Matching

In this paper, we proposed a framework to address the problem of binary object (2D or 3D) recognition. In our method, a binary object is represented as a Skeleton Tree (ST), transformed from its skeleton (or centerline). Both topological and geometrical features are embedded in the ST and this allows comparisons between different objects by tree matching algorithms. Tree descriptor is used to represent the topological features of the ST, and the maximal isomorphic subtrees (MIST) are obtained by searching for the longest matching substrings in the tree descriptors. A novel method of ST matching based on tree descriptor is also presented. The problems with cyclic skeleton and noise on the skeleton are discussed too. Experiments on a variety of objects get satisfying results, which show the potential of our method in the presence of rotation, scaling, translation and reflection. The time complexity of the algorithm is o(n³), where n is the number of the skeleton branches in ST.