Abstract
Video affective content analysis is a fascinating but seldom addressed field in entertainment computing research communities. To recognize affective content in video, a video affective content representation and recognition framework based on Video Affective Tree (VAT) and Hidden Markov Models (HMMs) was proposed. The proposed video affective content recognizer has good potential to recognize the basic emotional events of audience. However, due to Expectation-Maximization (EM) methods like the Baum-Welch algorithm tend to converge to the local optimum which is the closer to the starting values of the optimization procedure, the estimation of the recognizer parameters requires a more careful examination. A Genetic Algorithm combined HMM (GA-HMM) is presented here to address this problem. The idea is to combine a genetic algorithm to explore quickly the whole solution space with a Baum-Welch algorithm to find the exact parameter values of the optimum. The experimental results show that GA-HMM can achieve higher recognition rate with less computation compared with our previous works.
Chapter PDF
References
Hanjalic, A.: Extracting Moods from Pictures and Sounds: Towards truly personalized TV. IEEE Signal Processing Magazine 3, 90–100 (2006)
Hanjalic, A., Xu, L.-Q.: Affective video content representation and modeling. IEEE Trans. Multimedia 2, 143–154 (2005)
Kang, H.-B.: Affective Content Detection using HMMs. In: Proceedings of the eleventh ACM international conference on Multimedia, pp. 259–262 (November 2-8, 2003)
Sun, K., Yu, J.: Video Affective Content Representation and Recognition Using Video Affective Tree and Hidden Markov Models. LNCS. Springer, New York (to appear, 2007)
McLachlan, G.J., Krishnan, T.: The EM Algorithm and Extensions. Wiley, New York (1997)
Rabiner, L.: A tutorial on hidden Markov models and selected applications in speech recognition. Proc. IEEE 77(2), 256–286 (1989)
Coley, D.A.: An introduction to genetic algorithms for scientists and engineers. World Scientific Press, Singapore (1999)
Goldstein, E.: Sensation and Perception. Brooks/Cole (1999)
Information Technology—Multimedia Content Description Interface—Part 4: Audio, ISO/IEC CD 15938-4 (2001)
Author information
Authors and Affiliations
Editor information
Rights and permissions
Copyright information
© 2007 IFIP International Federation for Information Processing
About this paper
Cite this paper
Sun, K., Yu, J. (2007). Video Affective Content Recognition Based on Genetic Algorithm Combined HMM. In: Ma, L., Rauterberg, M., Nakatsu, R. (eds) Entertainment Computing – ICEC 2007. ICEC 2007. Lecture Notes in Computer Science, vol 4740. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-74873-1_30
Download citation
DOI: https://doi.org/10.1007/978-3-540-74873-1_30
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-74872-4
Online ISBN: 978-3-540-74873-1
eBook Packages: Computer ScienceComputer Science (R0)