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Journal of Zhejiang University SCIENCE A 2006 Vol.7 No.12 P.2063~2072

10.1631/jzus.2006.A2063


Ensemble learning HMM for motion recognition and retrieval by Isomap dimension reduction


Author(s):  XIANG Jian, WENG Jian-guang, ZHUANG Yue-ting, WU Fei

Affiliation(s):  School of Computer Science, Zhejiang University, Hangzhou 310027, China

Corresponding email(s):   xiang_bj@cs.zju.edu.cn, wengjg@cs.zju.edu.cn

Key Words:  Feature, Isomap, HMM (hidden Markov model), Ensemble learning, Motion recognition and retrieval


XIANG Jian, WENG Jian-guang, ZHUANG Yue-ting, WU Fei. Ensemble learning HMM for motion recognition and retrieval by Isomap dimension reduction[J]. Journal of Zhejiang University Science A, 2006, 7(12): 2063~2072.

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author="XIANG Jian, WENG Jian-guang, ZHUANG Yue-ting, WU Fei",
journal="Journal of Zhejiang University Science A",
volume="7",
number="12",
pages="2063~2072",
year="2006",
publisher="Zhejiang University Press & Springer",
doi="10.1631/jzus.2006.A2063"
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%T Ensemble learning HMM for motion recognition and retrieval by Isomap dimension reduction
%A XIANG Jian
%A WENG Jian-guang
%A ZHUANG Yue-ting
%A WU Fei
%J Journal of Zhejiang University SCIENCE A
%V 7
%N 12
%P 2063~2072
%@ 1673-565X
%D 2006
%I Zhejiang University Press & Springer
%DOI 10.1631/jzus.2006.A2063

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T1 - Ensemble learning HMM for motion recognition and retrieval by Isomap dimension reduction
A1 - XIANG Jian
A1 - WENG Jian-guang
A1 - ZHUANG Yue-ting
A1 - WU Fei
J0 - Journal of Zhejiang University Science A
VL - 7
IS - 12
SP - 2063
EP - 2072
%@ 1673-565X
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PB - Zhejiang University Press & Springer
ER -
DOI - 10.1631/jzus.2006.A2063


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.

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

Reference

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