Skip to main content

A Rough Set Approach to Video Genre Classification

  • Conference paper
Advanced Concepts for Intelligent Vision Systems (ACIVS 2006)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 4179))

  • 835 Accesses

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

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

Similar content being viewed by others

References

  1. 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)

    Google Scholar 

  2. Truong, B.T., Dorai, C.: Automatic genre identification for content-based video categorization. In: Proc. of 15th ICPR, (4), pp. 230–233 (2000)

    Google Scholar 

  3. 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)

    Google Scholar 

  4. 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)

    Google Scholar 

  5. 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)

    Google Scholar 

  6. Ma, Y.F., Zhang, H.J.: Motion pattern based video classification using support vector machines. EURASIP JASP 2, 199–208 (2003)

    Google Scholar 

  7. 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)

    Google Scholar 

  8. 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)

    Google Scholar 

  9. Pawlak, Z.: Rough sets: present state and the future. Foundations of Computing and Decision Sciences 11, 157–166 (1993)

    MathSciNet  Google Scholar 

  10. 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)

    Google Scholar 

  11. Maaate: http://www.cmis.csiro.au/dmis/Maaate/

  12. 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)

    Google Scholar 

  13. Hu, X.H.: Knowledge discovery in databases: an attribute-oriented rough set approach. Ph.D Dissertation, University of Regina, Canada (1995)

    Google Scholar 

  14. Zhong, N., Dong, J.Z., Ohsyga, S.: Using Rough Sets with Heuristics for feature Selection. Journal of Intelligent Information Systems 16, 199–214 (2001)

    Article  MATH  Google Scholar 

  15. Rosetta: http://www.idi.ntnu.no/~aleks/rosetta/rosetta.html

Download references

Author information

Authors and Affiliations

Authors

Editor information

Editors and Affiliations

Rights and permissions

Reprints 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)

Publish with us

Policies and ethics