Shot Boundary Detection of Video Sequence Using Hierarchical Hidden Markov Models 


Vol. 27,  No. 8, pp. 786-795, Aug.  2002


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  Abstract

In this paper, we present a histogram and moment-based video scene change detection technique using hierarchical Hidden Markov Models(HMMs). The proposed method extracts histograms from a low-frequency subband and moments of edge components from high-frequency subbands of wavelet transformed images. Then each HMM is trained by using histogram difference and directional moment difference, respectively, extracted from manually labeled video. The video segmentation process consists of two steps. A histogram-based HMM is first used to segment the input video sequence into three categories: shot, cut, gradual scene changes. In the second stage, a moment-based HMM is used to further segment the gradual changes into a fade and a dissolve. The experimental results show that the proposed technique is more effective in partitioning video frames than the previous threshold-based methods.

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  Cite this article

[IEEE Style]

J. Park, W. Cho, S. Park, "Shot Boundary Detection of Video Sequence Using Hierarchical Hidden Markov Models," The Journal of Korean Institute of Communications and Information Sciences, vol. 27, no. 8, pp. 786-795, 2002. DOI: .

[ACM Style]

Jong-Hyun Park, Wan-Hyun Cho, and Soon-Young Park. 2002. Shot Boundary Detection of Video Sequence Using Hierarchical Hidden Markov Models. The Journal of Korean Institute of Communications and Information Sciences, 27, 8, (2002), 786-795. DOI: .

[KICS Style]

Jong-Hyun Park, Wan-Hyun Cho, Soon-Young Park, "Shot Boundary Detection of Video Sequence Using Hierarchical Hidden Markov Models," The Journal of Korean Institute of Communications and Information Sciences, vol. 27, no. 8, pp. 786-795, 8. 2002.

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pISSN : 1226 - 4717
eISSN : 2287 - 3880