Text Driven Temporal Segmentation of Cricket Videos

  • K. Pramod Sankar
  • Saurabh Pandey
  • C. V. Jawahar
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4338)


In this paper we address the problem of temporal segmentation of videos. We present a multi-modal approach where clues from different information sources are merged to perform the segmentation. Specifically, we segment videos based on textual descriptions or commentaries of the action in the video. Such a parallel information is available for cricket videos, a class of videos where visual feature based (bottom-up) scene segmentation algorithms generally fail, due to lack of visual dissimilarity across space and time. With additional top-down information from textual domain, these ambiguities could be resolved to a large extent. The video is segmented to meaningful entities or scenes, using the scene level descriptions provided by the commentary. These segments can then be automatically annotated with the respective descriptions. This allows for a semantic access and retrieval of video segments, which is difficult to obtain from existing visual feature based approaches. We also present techniques for automatic highlight generation using our scheme.


Textual Description Semantic Concept Scene Category Visual Domain 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.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Rui, Y., Huang, T.S., Mehrotra, S.: Constructing table-of-content for videos. Multimedia Syst 7, 359–368 (1999)CrossRefGoogle Scholar
  2. 2.
    Jiang, H., Helal, A., Elmagarmid, A.K., Joshi, A.: Scene change detection techniques for video database systems. Multimedia Syst 6, 186–195 (1998)CrossRefGoogle Scholar
  3. 3.
    Koprinska, I., Carrato, S.: Temporal video segmentation: A survey. Signal Processing: Image Communication, 477–500 (2001)Google Scholar
  4. 4.
    Lefevre, S., Holler, J., Vincent, N.: A review of real-time segmentation of uncompressed video sequences for content-based search and retrieval. Real-Time Imaging 9, 73–98 (2003)CrossRefGoogle Scholar
  5. 5.
    Hanjalic, A., Lagendijk, R.L., Biemond, J.: Automated high-level movie segmentation for advanced video retrieval systems. IEEE Trans. Circuits Syst. Video Technol. 9, 580 (1999)CrossRefGoogle Scholar
  6. 6.
    Demarty, C., Beucher, S.: Morphological tools for indexing video documents. In: Proc. IEEE Intl. Conf. Multimedia Computing and Systems, p. 991 (1999)Google Scholar
  7. 7.
    Zabih, R., Miller, J., Mai, K.: A feature-based algorithm for detecting and classifying production effects. Multimedia Syst 7, 119–128 (1999)CrossRefGoogle Scholar
  8. 8.
    Rasheed, Z., Shah, M.: Scene detection in hollywood movies and tv shows. In: Proc. Computer Vision and Pattern Recognition, June 2003, vol. 2, pp. 343–348 (2003)Google Scholar
  9. 9.
    Rui, Y., Gupta, A., Acero, A.: Automatically extracting highlights for tv baseball programs. In: ACM Multimedia, pp. 105–115. ACM Press, New York (2000)Google Scholar
  10. 10.
    Babaguchi, N., Kawai, Y., Kitahashi, T.: Event based indexing of broadcast sports video by intermodal collaboration. IEEE Trans. Multimedia 4, 68–75 (2002)CrossRefGoogle Scholar
  11. 11.
    Sudhir, G., Lee, J.C.M., Jain, A.K.: Automatic classification of tennis video for high-level content-based retrieval. In: Proc. International Workshop on Content-Based Access of Image and Video Databases, pp. 81–90 (1998)Google Scholar
  12. 12.
    Kolekar, M.H., Sengupta, S.: A hierarchical framework for generic sports video classification. In: ACCV (2), pp. 633–642 (2006)Google Scholar
  13. 13.
    Jadon, R.S., Chaudhury, S., Biswas, K.K.: Sports video characterization using scene dynamics. In: ICVGIP, pp. 545–549 (2004)Google Scholar
  14. 14.
    Fatemi, O., Zhang, S., Panchanathan, S.: Optical flow based model for scene cut detection. In: Canadian Conf. on Electrical and Computer Engineering., vol. 1, pp. 470–473 (1996)Google Scholar
  15. 15.
    Gunsel, B., Ferman, A., Tekalp, A.: Temporal video segmentation using unsupervised clustering and semantic object tracking. Journal of Electronic Imaging 7, 592–604 (1998)CrossRefGoogle Scholar
  16. 16.
    Lienhart, R., Kuhmunch, C., Effelsberg, W.: On the detection and recognition of television commercials. In: International Conference on Multimedia Computing and Systems, pp. 509–516 (1997)Google Scholar
  17. 17.
    Wang, L., Liu, X., Lin, S., Xu, G., Shum, H.Y.: Generic slow-motion replay detection in sports video. In: ICIP, pp. 1585–1588 (2004)Google Scholar
  18. 18.
    Li, B., Errico, J.H., Pan, H., Sezan, I.: Bridging the semantic gap in sports video retrieval and summarization. J. Vis. Commun. Image R. 15, 393–424 (2004)MATHCrossRefGoogle Scholar
  19. 19.
    Cox, I.J., Hingorani, S.L., Rao, S.B., Maggs, B.M.: A maximum likelihood stereo algorithm. Comput. Vis. Image Underst. 63, 542–567 (1996)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • K. Pramod Sankar
    • 1
  • Saurabh Pandey
    • 1
  • C. V. Jawahar
    • 1
  1. 1.Centre for Visual Information TechnologyInternational Institute of Information TechnologyHyderabadIndia

Personalised recommendations