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ZJUS-A
Journal of Zhejiang University-SCIENCE A (Applied Physics & Engineering)  2006, Vol. 7 Issue (12): 2063-2072    DOI: 10.1631/jzus.2006.A2063
Information & Computer Science     
Ensemble learning HMM for motion recognition and retrieval by Isomap dimension reduction
XIANG Jian, WENG Jian-guang, ZHUANG Yue-ting, WU Fei
School of Computer Science, Zhejiang University, Hangzhou 310027, China
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Abstract  Along with the development of motion capture technique, more and more 3D motion databases become available. In this paper, a novel approach is presented for motion recognition and retrieval based on ensemble HMM (hidden Markov model) learning. Due to the high dimensionality of motion’s features, Isomap nonlinear dimension reduction is used for training data of ensemble HMM learning. For handling new motion data, Isomap is generalized based on the estimation of underlying eigenfunctions. Then each action class is learned with one HMM. Since ensemble learning can effectively enhance supervised learning, ensembles of weak HMM learners are built. Experiment results showed that the approaches are effective for motion data recognition and retrieval.

Key words： Feature      Isomap      HMM (hidden Markov model)      Ensemble learning      Motion recognition and retrieval     
Received: 10 May 2006     
CLC:  TP37  
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XIANG Jian
WENG Jian-guang
ZHUANG Yue-ting
WU Fei
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XIANG Jian, WENG Jian-guang, ZHUANG Yue-ting, WU Fei. Ensemble learning HMM for motion recognition and retrieval by Isomap dimension reduction. Journal of Zhejiang University-SCIENCE A (Applied Physics & Engineering), 2006, 7(12): 2063-2072.

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http://www.zjujournals.com/xueshu/zjus-a/10.1631/jzus.2006.A2063     OR     http://www.zjujournals.com/xueshu/zjus-a/Y2006/V7/I12/2063

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