Video Shot Detection Using Hidden Markov Models with Complementary Features

Beijing, China August 30-September 01

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http://doi.ieeecomputersociety.org/10.1109/ICICIC.2006.549

First International Conference on Inn ...

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	Weigang Zhang, Jianqiu Lin, Xiaopeng Chen, Qingming Huang, Yang Liu, "Video Shot Detection Using Hidden Markov Models with Complementary Features," Innovative Computing ,Information and Control, International Conference on, vol. 3, pp. 593-596, First International Conference on Innovative Computing, Information and Control - Volume III (ICICIC'06), 2006.

	BibTex	x
	@article{ 10.1109/ICICIC.2006.549, author = {Weigang Zhang and Jianqiu Lin and Xiaopeng Chen and Qingming Huang and Yang Liu}, title = {Video Shot Detection Using Hidden Markov Models with Complementary Features}, journal ={Innovative Computing ,Information and Control, International Conference on}, volume = {3}, year = {2006}, isbn = {0-7695-2616-0}, pages = {593-596}, doi = {http://doi.ieeecomputersociety.org/10.1109/ICICIC.2006.549}, publisher = {IEEE Computer Society}, address = {Los Alamitos, CA, USA}, }

	RefWorks Procite/RefMan/Endnote	x
	TY - CONF JO - Innovative Computing ,Information and Control, International Conference on TI - Video Shot Detection Using Hidden Markov Models with Complementary Features SN - 0-7695-2616-0 SP593 EP596 A1 - Weigang Zhang, A1 - Jianqiu Lin, A1 - Xiaopeng Chen, A1 - Qingming Huang, A1 - Yang Liu, PY - 2006 KW - null VL - 3 JA - Innovative Computing ,Information and Control, International Conference on ER -

Weigang Zhang, Harbin Institute of Technology at Weihai, China

Jianqiu Lin, Harbin Institute of Technology at Weihai, China

Xiaopeng Chen, Harbin Institute of Technology at Weihai, China

Qingming Huang, Graduate School of Chinese Academy of Sciences, China

Yang Liu, Harbin Institute of Technology, China

Shot detection is the first stage of video analysis. In this paper, we present a machine learning based shot detection approach using Hidden Markov Models (HMMs), in which both the color and shape clues are utilized. Its advantages are twofold. First, the temporal characteristics of different shot transitions are exploited and an HMM is constructed for each type of shot transitions, including cut and gradual transitions. As trained HMMs are used to recognize the shot transition patterns automatically, it does not suffer from any trouble of threshold selection problem. Second, two complementary features, statistical corner change ratio (SCCR) and HSV color histogram difference, are used. The former summarizes the shape well whereas the latter summarizes the appearance well. Experimental results on a set of test videos demonstrate the efficacy of this shot detection approach.

Citation:

Weigang Zhang, Jianqiu Lin, Xiaopeng Chen, Qingming Huang, Yang Liu, "Video Shot Detection Using Hidden Markov Models with Complementary Features," icicic, vol. 3, pp.593-596, First International Conference on Innovative Computing, Information and Control - Volume III (ICICIC'06), 2006

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