Statistical Model for Emotional Video Shot Characterization 


Vol. 28,  No. 12, pp. 1200-1208, Dec.  2003


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  Abstract

Affective computing play an important role in intelligent Human Computer Interactions(HCI). To detect emotional events, it is desirable to construct a computing model for extracting emotion related features from video. In this paper, we propose a statistical model based on the probabilistic distribution of low level features in video shots. the proposed method extracts low level features from video shots and then from a GMM(Gaussian Mixture Model) for them to detect emotional shots. As low level features, we use color, camera motion and sequence of shot lengths. The features can be modeled as a GMM by using EM(Expectation Maximization) algorithm and the relations between time and emotions are estimated by MLE(Maximum Likelihood Estimation). Finally, the two statistical models are combined together using Bayesian framework to detect emotional events in video.

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  Cite this article

[IEEE Style]

H. Park and H. Kang, "Statistical Model for Emotional Video Shot Characterization," The Journal of Korean Institute of Communications and Information Sciences, vol. 28, no. 12, pp. 1200-1208, 2003. DOI: .

[ACM Style]

Hyun-Jae Park and Hang-Bong Kang. 2003. Statistical Model for Emotional Video Shot Characterization. The Journal of Korean Institute of Communications and Information Sciences, 28, 12, (2003), 1200-1208. DOI: .

[KICS Style]

Hyun-Jae Park and Hang-Bong Kang, "Statistical Model for Emotional Video Shot Characterization," The Journal of Korean Institute of Communications and Information Sciences, vol. 28, no. 12, pp. 1200-1208, 12. 2003.

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pISSN : 1226 - 4717
eISSN : 2287 - 3880