3D Model Retrieval with Multi-Granular Semantics Based on Gaussian Process Classifier
GAO Bo-Yong1,2, ZHANG San-Yuan1, PAN Xiang3
1. Department of Computer Science and Engineering, College of Computer Science and Technology, Zhejiang University, Hangzhou 310027 2.Department of Computer Science and Technology, College of Information Engineering, China Jiliang University, Hangzhou 310018 3. Department of Computer Science and Technology, Institute of Computer Science, Zhejiang University of Technology, Hangzhou 310014
Abstract In order to solve the inconsistency between users’ intentions in semantic 3D model retrieval system, a retrieval framework with multi-granular semantics is established, in which learning model can adapt to different user search intentions. Firstly, model classification is divided into different levels and the multi-granularity structure of semantic concept is formed. Then, a hybrid shape feature based on views is used to describe the shape characteristics of 3D model. And the Gaussian process classifier is used to associate low-level features with query concepts on a different level of semantic concept. Compared with existing research, the retrieval framework with multi-granular semantics allows the users to set their retrieval intentions according to selecting the granular level of semantics, and the results meet the user semantics as much as possible. The experimental results of retrieval performance evaluation using the benchmark show that the retrieval performance using proposed method is significantly higher than content-based retrieval and confident with human concept.
GAO Bo-Yong,ZHANG San-Yuan,PAN Xiang. 3D Model Retrieval with Multi-Granular Semantics Based on Gaussian Process Classifier[J]. , 2011, 24(5): 597-603.
[1] Tangelder J, Veltkamp R. A Survey of Content Based 3D Shape Retrieval Methods. Multimedia Tools and Applications, 2008, 39(3): 441-471 [2] Pan Xiang, Zhang Sanyuan, Ye Xiuzi. A Survey of Content-Based 3D Model Retrieval with Semantic Features.Chinese Journal of Computers, 2009, 32(6): 1069-1079 (in Chinese) (潘 翔,张三元,叶修梓.三维模型语义检索研究进展.计算机学报, 2009, 32(6): 1069-1079) [3] Ohbuchi R, Yamamoto A, Kobayashi J. Learning Semantic Categories for 3D Model Retrieval // Proc of the International Workshop on Multimedia Information Retrieval. Augsburg, Germany, 2007: 31-40 [4] Gao Boyong, Zheng Herong, Zhang Sanyuan. An Overview of Semantics Processing in Content-Based 3D Model Retrieval // Proc of the International Conference on Artificial Intelligence and Computational Intelligence.Shanghai, China, 2009: 54-59 [5] Novotni M, Park G J, Wessel R, et al. Evaluation of Kernel Based Methods for Relevance Feedback in 3D Shape Retrieval [EB/OL]. [2010-08-31]. http://cg.cs.uni.bonn.de/aigaion2root/attachments/novoteni-2005-evailation.pdf [6] Yamamoto A, Tezuka M, Shimizu T, et al. SHREC’08 Entry: Semi-Supervised Learning for Semantic 3D Model Retrieval // Proc of the IEEE International Conference on Shape Modeling and Applications. New York, USA, 2008: 241-243 [7] Li Zhanjun, Ramani K. Ontology-Based Design Information Extraction and Retrieval. Artificial Intelligence for Engineering Design, Analysis, and Manufacturing, 2007, 21(2): 137-154 [8] Pan Xiang, Zhang Sanyuan, Ye Xiuzi, et al. 3D Model Retrieval Based on Spatial and Normal Feature. Pattern Recognition and Artificial Intelligence, 2005, 18(2): 211-215 (in Chinese) (潘 翔,张三元,叶修梓,等.基于空间-法向量特征的三维模型检索.模式识别与人工智能, 2005, 18(2): 211-215) [9] Neural C W, Williams C K I, Rasmussen C E. Gaussian Processes for Regression. Cambridge, USA: MIT Press, 1996 [10] Schlkopf B, Smola A J. Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond. Cambridge, USA: MIT Press, 2002 [11] Rasmussen C E, Williams C K I. Gaussian Processes for Machine Learning.Cambridge, USA: MIT Press, 2006 [12] Shilane P, Min P, Kazhdan M, et al. The Princeton Shape Benchmark // Proc of the Conference on Shape Modeling Applications.Genoa, Italy, 2004: 167-178 [13] Hou Suyu, Ramani K. Calligraphic Interfaces: Classifier Combination for Sketch-Based 3D Part Retrieval. Computers Graphics, 2007, 31(4): 598-609 [14] Liu Yujie, Li Zongmin, Li Hua. Ensembling Neural Networks-Based 3D Model Retrieval // Proc of the 3rd International Conference on Pervasive Computing and Applications. Alexandria, Egypt, 2008: 456-460
2011, Vol. 24 
