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Evidence-based SVM fusion for 3D model retrieval

机译：基于证据的SVM融合用于3D模型检索

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摘要

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.
机译：现有的许多3D模型检索都使用KNN（k最近邻）方法进行相似性搜索，但是在高维空间搜索中效率低下。在本文中，集成了分类工具以支持在高维特征空间中更有效的3D模型搜索。我们提出的算法使用多个SVM分类器预测给定查询的3D模型，并使用D-S证据理论融合所有预测结果。实验结果表明，与传统的kNN方法相比，我们提出的3D模型检索算法可以显着提高精度。

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  • 来源
    《Multimedia Tools and Applications》 |2014年第2期|1731-1749|共19页
  • 作者

    Zongmin Li; Zijian Wu; Zhenzhong Kuang; Kai Chen; Yongzhou Gan; Jianping Fan;

  • 作者单位

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

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

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

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

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

    Department of Computer Science, UNC-Charlotte, Charlotte, NC 28223, USA;

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  • 原文格式 PDF
  • 正文语种 eng
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  • 关键词

    3D model retrieval; Evidence theory; Classifier fusion; Classification;

    机译：3D模型检索;证据理论;分类器融合;分类;

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