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CLC number: TP37; TP31

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Received: 2007-06-12

Revision Accepted: 2007-08-14

Crosschecked: 0000-00-00

Cited: 13

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Journal of Zhejiang University SCIENCE A 2007 Vol.8 No.12 P.1953~1961

http://doi.org/10.1631/jzus.2007.A1953


Support Vector Machine active learning for 3D model retrieval


Author(s):  LENG Biao, QIN Zheng, LI Li-qun

Affiliation(s):  Department of Computer Science and Technology, Tsinghua University, Beijing 100084, China; more

Corresponding email(s):   lengb04@mails.tsinghua.edu.cn

Key Words:  3D model retrieval, Shape descriptor, Relevance feedback, Support Vector Machine (SVM), Active learning


LENG Biao, QIN Zheng, LI Li-qun. Support Vector Machine active learning for 3D model retrieval[J]. Journal of Zhejiang University Science A, 2007, 8(12): 1953~1961.

@article{title="Support Vector Machine active learning for 3D model retrieval",
author="LENG Biao, QIN Zheng, LI Li-qun",
journal="Journal of Zhejiang University Science A",
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number="12",
pages="1953~1961",
year="2007",
publisher="Zhejiang University Press & Springer",
doi="10.1631/jzus.2007.A1953"
}

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%A QIN Zheng
%A LI Li-qun
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%D 2007
%I Zhejiang University Press & Springer
%DOI 10.1631/jzus.2007.A1953

TY - JOUR
T1 - Support Vector Machine active learning for 3D model retrieval
A1 - LENG Biao
A1 - QIN Zheng
A1 - LI Li-qun
J0 - Journal of Zhejiang University Science A
VL - 8
IS - 12
SP - 1953
EP - 1961
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Y1 - 2007
PB - Zhejiang University Press & Springer
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DOI - 10.1631/jzus.2007.A1953


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.

Darkslateblue:Affiliate; Royal Blue:Author; Turquoise:Article

Reference

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