A powerful 3D model classification mechanism based on fusing multi-graph

Abstract

Recently, integrating several feature descriptors to be a powerful one has become a hot issue in the field of 3D object understanding. The fusing mechanism is so crucial that can significantly affect the performance of 3D model classification. In this paper, a powerful model for 3D model classification, which can novelly integrate several graphs, is proposed. This mechanism is based on graph fusion and modifies each graph׳s weight in a boost manner. Each graph׳s weight in the fusion graph can be dynamically calculated according to its performance. Finally, a fusion graph is acquired to 3D model classification. We conduct the experiments on the publicly available 3D model databases: Princeton shape benchmark (PSB) and SHREC׳09, and the experimental results demonstrate the powerful performance of the proposed method.