Abstract
Video classification provides an efficient way to manage and utilize the video data. Existing works on this topic fall into this category: enlarging the feature set until the classification is reliable enough. However, some features may be redundant or irrelevant. In this paper, we address the problem of choosing efficient feature set in video genre classification to achieve acceptable classification results but relieve computation burden significantly. A rough set approach is proposed. In comparison with existing works and the decision tree method, experimental results verify the efficiency of the proposed approach.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Fischer, S., Lienhart, R., Effelsberg, W.: Automatic Recognition of Film Genres. In: Proc. of the 3rd ACM Int. Conf. on Multimedia, San Francisco, US, pp. 295–304 (1995)
Truong, B.T., Dorai, C.: Automatic genre identification for content-based video categorization. In: Proc. of 15th ICPR, (4), pp. 230–233 (2000)
Liu, Z., Huang, J.C., Wang, Y., Chen, T.: Audio feature extraction and analysis for scene classification. In: Proc. of IEEE Signal Processing Society Workshop on Multimedia Signal Processing, Princeton, US, pp. 343–348 (1997)
Liu, Z., Huang, J.C., Wang, Y.: Classification of TV programs based on audio information using hidden Markov Model. In: Proc. of IEEE Workshop Multimedia Signal Processing, Los Angeles, US, pp. 27–32 (1998)
Xu, L.Q., Li, Y.: Video classification using spatial-temporal features and PCA. In: Proc. of the ICME, Baltimore, US, vol. (3), pp. 485–488 (2003)
Ma, Y.F., Zhang, H.J.: Motion pattern based video classification using support vector machines. EURASIP JASP 2, 199–208 (2003)
Jin, S.H., Bae, T.M., Choo, J.H., Ro, Y.M.: Video genre classification using multimodal features. In: Yeung, M.M. (ed.) Storage and Retrieval Methods and Applications for Multimedia 2004. SPIE, vol. 5307, pp. 307–318 (2003)
Rasheed, Z., Shah, M.: Movie genre classification by exploiting audio-visual features of previews. In: Proc. of 16th ICPR, Orlando, US, vol. 2, pp. 1086–1089 (2002)
Pawlak, Z.: Rough sets: present state and the future. Foundations of Computing and Decision Sciences 11, 157–166 (1993)
Zhang, M., Yao, J.T.: A rough sets based approach to feature selection. In: Proc. of the 23rd Inter. Conf. of AFIPS, Banff, CA, pp. 434–439 (2004)
John, G.H., Kohavi, R., Pfleger, K.: Irrelevant features and the subset selection problem. In: Proc. of 11the ICML, San Francisco, CA, pp. 121–129 (1994)
Hu, X.H.: Knowledge discovery in databases: an attribute-oriented rough set approach. Ph.D Dissertation, University of Regina, Canada (1995)
Zhong, N., Dong, J.Z., Ohsyga, S.: Using Rough Sets with Heuristics for feature Selection. Journal of Intelligent Information Systems 16, 199–214 (2001)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2006 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Cheng, W., Liu, C., Wang, X. (2006). A Rough Set Approach to Video Genre Classification. In: Blanc-Talon, J., Philips, W., Popescu, D., Scheunders, P. (eds) Advanced Concepts for Intelligent Vision Systems. ACIVS 2006. Lecture Notes in Computer Science, vol 4179. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11864349_110
Download citation
DOI: https://doi.org/10.1007/11864349_110
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-44630-9
Online ISBN: 978-3-540-44632-3
eBook Packages: Computer ScienceComputer Science (R0)