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ZJUS-A
Journal of Zhejiang University-SCIENCE A (Applied Physics & Engineering)  2007, Vol. 8 Issue (12): 1953-1961    DOI: 10.1631/jzus.2007.A1953
Electrical & Electronic Engineering     
Support Vector Machine active learning for 3D model retrieval
LENG Biao, QIN Zheng, LI Li-qun
Department of Computer Science and Technology, Tsinghua University, Beijing 100084, China; School of Software, Tsinghua University, Beijing 100084, China
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Abstract  In this paper, we present a novel Support Vector Machine active learning algorithm for effective 3D model retrieval using the concept of relevance feedback. The proposed method learns from the most informative objects which are marked by the user, and then creates a boundary separating the relevant models from irrelevant ones. What it needs is only a small number of 3D models labelled by the user. It can grasp the user’s semantic knowledge rapidly and accurately. Experimental results showed that the proposed algorithm significantly improves the retrieval effectiveness. Compared with four state-of-the-art query refinement schemes for 3D model retrieval, it provides superior retrieval performance after no more than two rounds of relevance feedback.

Key words： 3D model retrieval      Shape descriptor      Relevance feedback      Support Vector Machine (SVM)      Active learning     
Received: 12 June 2007     
CLC:  TP37  
  TP31  
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LENG Biao
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Cite this article:

LENG Biao, QIN Zheng, LI Li-qun. Support Vector Machine active learning for 3D model retrieval. Journal of Zhejiang University-SCIENCE A (Applied Physics & Engineering), 2007, 8(12): 1953-1961.

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http://www.zjujournals.com/xueshu/zjus-a/10.1631/jzus.2007.A1953     OR     http://www.zjujournals.com/xueshu/zjus-a/Y2007/V8/I12/1953

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