Broadcast Video Content Segmentation by Supervised Learning

  • Kevin W. Wilson
  • Ajay Divakaran
Part of the Signals and Communication Technology book series (SCT)


This chapter reviews previous work on broadcast video summarization with an emphasis on scene change detection. We then describe our recent work using supervised learning to train a scene change detector. We have been able to achieve 80% scene change detection rate for a 10% false positive rate.


Support Vector Machine Video Shot Audio Feature Video Summarization Scene Change 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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  1. 1.
    Adams, B., Venkatesh, S., Bui, H.H., Dorai, C.: A probabilistic framework for extracting narrative act boundaries and semantics in motion pictures. Multimedia Tools and Applications 27(2) (2005)Google Scholar
  2. 2.
    Aigrain, P., Zhang, H., Petkovic, D.: Content-based representation and retrieval of visual media: A state-of-the-art review. Multimedia Tools and Applications 3(3) (1996)Google Scholar
  3. 3.
    Chang, S.F., Kennedy, L.S., Zavesky, E.: Columbia university’s semantic video search engine. In: ACM Conference on Image and Video Retrieval (2007)Google Scholar
  4. 4.
    Hanjalic, A., Lagendijk, R.L., Biemond, J.: Automated high-level movie segmentation for advanced video-retrieval systems. IEEE Transactions on Circuits and Systems for Video Technology 9(4) (1999)Google Scholar
  5. 5.
    Hastie, T., Tibshirani, R., Friedman, J.: The Elements of Statistical Learning: Data Mining, Inference, and Prediction. Springer (2001). -21
  6. 6.
    Kender, J., Yeo, B.: Video scene segmentation via continuous video coherence. In: IEEE Conference on Computer Vision and Pattern Recognition (1998)Google Scholar
  7. 7.
    Lienhart, R.W.: Comparison of automatic shot boundary detection algorithms. pp. 290–301. SPIE (1998).
  8. 8.
    Manjunath, B.S., Salembier, P., Sikora, T. (eds.): Introduction to MPEG-7 Multimedia Content Description Interface. Wiley (2002)Google Scholar
  9. 9.
    Moncrieff, S., Venkatesh, S.: Narrative structure detection through audio pace. In: IEEE Multimedia Modeling (2006)Google Scholar
  10. 10.
    Niu, F., Goela, N., Divakaran, A., Abdel-Mottaleb, M.: Audio scene segmentation for video with generic content. Multimedia Content Access: Algorithms II, SPIE Electronic Imaging (2008)Google Scholar
  11. 11.
    Otsuka, I., Radhakrishnan, R., Siracusa, M., Divakaran, A., Mishima, H.: An enhanced video summarization system using audio features for a personal video recorder. IEEE Transactions on Consumer Electronics 52(1) (2006)Google Scholar
  12. 12.
    Sivic, J., Zisserman, A.: Video google: A text retrieval approach to object matching in videos. In: IEEE International Conference on Computer Vision (2003)Google Scholar
  13. 13.
    Sundaram, H., Chang, S.F.: Video analysis and summarization at structural and semantic levels. In: D. Feng, W.C. Siu, H. Zhang (eds.) Multimedia Information Retrieval and Management: Technological Fundamentals and Applications. Springer Verlag (2003)Google Scholar
  14. 14.
    Truong, B.T., Venkatesh, S.: Video abstraction: A systematic review and classification. ACM Transactions on Multimedia Computing, Communications and Applications 3(1) (2007)Google Scholar
  15. 15.
    Truong, B.T., Venkatesh, S., Dorai, C.: Scene extraction in motion pictures. IEEE Transactions on Circuits and Systems for Video Technology 15(1) (2003)Google Scholar
  16. 16.
    Wei, C.Y., Dimitrova, N., Chang, S.F.: Color-mood analysis of films based on syntactic and psychological models. In: IEEE International Conference on Multimedia and Expo (2004)Google Scholar
  17. 17.
    Worring, M., Snoek, C.G.M., de Rooij, O., Ngyen, G.P., Smeulders, A.W.M.: The mediamill semantic video search engine. In: IEEE International Conference on Acoustics, Speech, and Signal Processing (2007)Google Scholar
  18. 18.
    Xiong, Z., Radhakrishnan, R., Divakaran, A., Rui, Y., Huang, T.S.: A Unified Framework for Video Summarization, Browsing, and Retrieval. Elsevier (2006)Google Scholar
  19. 19.
    Zhang, H., Low, C.Y., Smoliar, S.W., Wu, J.H.: Video parsing, retrieval, and browsing: An integrated and content-based solution. In: ACM Multimedia (1995)Google Scholar

Copyright information

© Springer Science+Business Media, LLC 2009

Authors and Affiliations

  1. 1.Mitsubishi Electric Research LaboratoryCambridgeUSA

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