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Evidence-based SVM fusion for 3D model retrieval

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Abstract

Many existing 3D model retrieval use KNN (k-nearest neighbor) method for similarity search, but it is inefficient in high-dimension space search. In this paper, the classification tools are integrated for supporting more effective 3D model search in the high-dimensional feature space. Our proposed algorithm used multiple SVM classifiers to predict 3D models for a given query and D-S Evidence theory is used to fuse all the prediction results. Experimental results show that our proposed 3D model retrieval algorithm can improve the accuracy significantly compared with the traditional kNN method.

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Acknowledgments

This work is partly supported by National Natural Science Foundation of China (Grant No.60873164), National High-Tech R&D Plan (Grant No. 2009AA062802), the Shandong Provincial Natural Science Foundation(Grant No.ZR2009GL014), the Scientific Research Foundation for the Excellent Middle-Aged and Youth Scientists of Shandong Province of China (Grant No.BS2010DX037), Ministry of Culture Science and Technology Innovation Project(Grant No. 46-2010),the Fundamental Research Funds for the Central Universities(Grant No. 09CX04044A, 10CX04043A,10CX04014B)

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Authors and Affiliations

  1. College of Computer and Communication Engineering, China University of Petroleum, Qingdao, Shandong, 266555, China

    Zongmin Li, Zijian Wu, Zhenzhong Kuang, Kai Chen & Yongzhou Gan

  2. Department of Computer Science, UNC-Charlotte, Charlotte, NC, 28223, USA

    Jianping Fan

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  1. Zongmin Li
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Correspondence to Zhenzhong Kuang.

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Li, Z., Wu, Z., Kuang, Z. et al. Evidence-based SVM fusion for 3D model retrieval. Multimed Tools Appl 72, 1731–1749 (2014). https://doi.org/10.1007/s11042-013-1475-z

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