| Support Vector Machine active learning for 3D model retrieval |
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| 作者姓名： | LENG Biao QIN Zheng LI Li-qun |
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| 作者单位： | LENG Biao1，QIN Zheng1，2，LI Li-qun2 (1Department of Computer Science and Technology，Tsinghua University，Beijing 100084，China) (2School of Software，Tsinghua University，Beijing 100084，China) |
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| 基金项目： | the National Basic Research Program (973) of China (No. 2004CB719401)，the National Research Foundation for the Doctoral Program of Higher Education of China (No.20060003060) |
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| 摘 要： | 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
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| 关 键 词： | 多媒体技术 计算机软件 3D技术 检索方法 |
| 收稿时间： | 2007-06-12 |
| 修稿时间： | 2007-08-14 |
Support Vector Machine active learning for 3D model retrieval |
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| LENG Biao QIN Zheng LI Li-qun.Support Vector Machine active learning for 3D model retrieval[J].Journal of Zhejiang University Science,2007,8(12):1953-1961. |
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| Authors: | Leng Biao Qin Zheng Li Li-quan |
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| Institution: | (1) Department of Computer Science and Technology, Tsinghua University, Beijing, 100084, China;(2) 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.
Project supported by the National Basic Research Program (973) of China (No. 2004CB719401) and the National Research Foundation
for the Doctoral Program of Higher Education of China (No. 20060003060) |
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| Keywords: | |
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