Objects similarity measure based on skeleton tree descriptor matching

A new object feature, i.e., tree descriptor, was used to represent a skeleton tree. And the maximal isomorphic subtree was obtained by searching the longest same substring in the tree descriptors, then a novel method of objects similarity measuring based on matching skeleton tree was presented. During the matching process, a new model was used to measure the similarity between two skeleton branches, furthermore, the shape feature and topology feature were associated perfectly. The problems of cyclic skeleton and noise on the skeleton were discussed. The similarity computing with our method is invariant under rotation, scaling, translation, and the time complexity of the algorithm is o (n(3)), n is the number of the skeleton branches in the skeleton tree, and the experiments on a variety of objects get satisfying results.