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

作     者：Leng, Biao Du, Changchun Guo, Shuang Zhang, Xiangyang Xiong, Zhang 

作者机构：Beihang Univ Sch Comp Sci & Engn Beijing 100191 Peoples R China 

出 版 物：《NEUROCOMPUTING》 (神经计算)

年 卷 期：2015年第168卷

页      面：761-769页

核心收录：

学科分类：08[工学] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

基　　金：National Natural Science Foundation of China New Teachers' Fund for Doctor Stations, Ministry of Education 

主　　题：3D model classification Graph fusion Boost modified 

摘      要：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. (C) 2015 Elsevier B.V. All rights reserved.