Headlines Usefulness for Content-Based Indexing of TV Sports News

  • Kazimierz ChorośEmail author
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 183)


In the classical indexing process of text documents keywords are derived mainly from the title, chapter titles, figure legends, table captions, and other special part of a text. The same strategy seems to be adequate also for a video indexing. The content analysis is more effective when the structure of a video is taking into account. A digital video similarly to text document is also hierarchically structured into a strict hierarchy. It is composed of different structural units such as: acts, episodes (sequences), scenes, camera shots and finally, single frames. The sequence of scenes in a video is usually organized in a standard way typical for a given category of a video. Particularly TV shows are edited respecting standard rules. The chapter presents the results of analyses of the structure of TV sports news and of the usefulness of sport headlines for content-based video indexing. The sport headlines and the video editing schemes recognized for a given video type may significantly help to reduce the number of frames analyzed during content-based indexing process.


Sport Event News Video Video Shot Sport Video Indexing Process 
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.
    Geetha, P., Narayanan, V.: A Survey of content-based video retrieval. Journal of Computer Science 4(6), 474–486 (2008)CrossRefGoogle Scholar
  2. 2.
    Money, A.G., Agius, H.: Video summarisation: a conceptual framework and survey of the state of the art. Journal of Visual Communication and Image Representation 19, 121–143 (2008)CrossRefGoogle Scholar
  3. 3.
    Hu, W., Xie, N., Li, L., Zeng, X., Maybank, S.: A survey on visual content-based video indexing and retrieval. IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews 41(6), 797–819 (2011)CrossRefGoogle Scholar
  4. 4.
    Choroś, K.: Video Shot Selection and Content-Based Scene Detection for Automatic Classification of TV Sports News. In: Tkacz, E., Kapczynski, A. (eds.) Internet – Technical Development and Applications. AISC, vol. 64, pp. 73–80. Springer, Heidelberg (2009)CrossRefGoogle Scholar
  5. 5.
    Tapu, R., Zaharia, T.: High Level Video Temporal Segmentation. In: Bebis, G. (ed.) ISVC 2011, Part I. LNCS, vol. 6938, pp. 224–235. Springer, Heidelberg (2011)Google Scholar
  6. 6.
    Han, H., Kim, J.: An useful method for scene categorization from new video using visual features. In: Third World Congress on Nature and Biologically Inspired Computing (NaBIC), pp. 480–484 (2011)Google Scholar
  7. 7.
    Rao, K.S., Pachpande, K., Vempada, R.R., Maity, S.: Segmentation of TV broadcast news using speaker specific information. In: Proceedings of the National Conference on Communications (NCC), pp. 1–5 (2012)Google Scholar
  8. 8.
    Jia, Y., Chen, Z., Yu, S.: Reader emotion classification of news headlines. In: Proceedings of the International Conference on Natural Language Processing and Knowledge Engineering (NLP-KE), pp. 1–6 (2009)Google Scholar
  9. 9.
    Ko, C.-C., Xie, W.-M.: News video segmentation and categorization techniques for content-demand browsing. In: Proceedings of the Congress on Image and Signal Processing (CISP 2008), vol. 2, pp. 530–534 (2008)Google Scholar
  10. 10.
    Yang, Y., Lin, S.-X., Zhang, Y.-D., Tang, S.: Statistical Framework for Shot Segmentation and Classification in Sports Video. In: Yagi, Y., Kang, S.B., Kweon, I.S., Zha, H. (eds.) ACCV 2007, Part II. LNCS, vol. 4844, pp. 106–115. Springer, Heidelberg (2007)CrossRefGoogle Scholar
  11. 11.
    Li, L., Zhang, N., Duan, L., Huang, Q., Du, J., Guan, L.: Automatic sports genre categorization and view-type classification over large-scale dataset. In: Proceedings of the seventeen ACM International Conference on Multimedia, pp. 653–656 (2009)Google Scholar
  12. 12.
    Choroś, K., Pawlaczyk, P.: Content-Based Scene Detection and Analysis Method for Automatic Classification of TV Sports News. In: Szczuka, M., Kryszkiewicz, M., Ramanna, S., Jensen, R., Hu, Q. (eds.) RSCTC 2010. LNCS(LNAI), vol. 6086, pp. 120–129. Springer, Heidelberg (2010)CrossRefGoogle Scholar
  13. 13.
    Zhang, N., Guan, L.: An efficient framework on large-scale video genre classification. In: IEEE International Workshop on Multimedia Signal Processing (MMSP), pp. 481–486 (2010)Google Scholar
  14. 14.
    Ling-Yu, D., Min, X., Qi, T., Chang-Sheng, X., Jin, J.S.: A unified framework for semantic shot classification in sports video. IEEE Transactions on Multimedia, 1066–1083 (2005)Google Scholar
  15. 15.
    Ishida, K., Tanaka, M.: Identification of the part of soccer court from video signal by neural networks. In: Proceedings of the Int. Conf. on Control, Automation and Systems 2008, pp. 2563–2568 (2008)Google Scholar
  16. 16.
    Lien, C.-C., Chiang, C.-L., Lee, C.-H.: Scene-based event detection for baseball videos. Journal of Visual Communication and Image Representation 18, 1–14 (2007)CrossRefGoogle Scholar
  17. 17.
    Huang, Y., Choiu, C., Sandnes, F.E.: An intelligent strategy for the automatic detection of highlights in tennis video recordings. Expert Systems with Applications 36(6), 9907–9918 (2009)CrossRefGoogle Scholar
  18. 18.
    Huang, Y., Choiu, C., Sandnes, F.E.: An intelligent strategy for the automatic detection of highlights in tennis video recordings. Expert Systems with Applications 36, 9907–9918 (2009)CrossRefGoogle Scholar
  19. 19.
    Cai, Z.Q., Tai, J.: Line detection in soccer video. In: Proceedings of the Fifth International Conference on Information, Communications and Signal Processing, pp. 538–541 (2005)Google Scholar
  20. 20.
    Li, Y., Liu, G., Qian, X.: Ball and field line detection for placed kick refinement. In: Proc. Global Congress on Intelligent Systems (GCIS), vol. 4, pp. 404–407 (2009)Google Scholar
  21. 21.
    Geng, Y., Xu, D., Feng, S.: Hierarchical Video Summarization Based on Video Structure and Highlight. In: Yeung, D.-Y., Kwok, J.T., Fred, A., Roli, F., de Ridder, D. (eds.) SSPR 2006 and SPR 2006. LNCS, vol. 4109, pp. 226–234. Springer, Heidelberg (2006)CrossRefGoogle Scholar
  22. 22.
    Zhang, S., Zhang, Y., Chen, T., Hall, P.M., Martin, R.: Video structure analysis. Tsinghua Science and Technology 12(6), 714–718 (2007)CrossRefGoogle Scholar
  23. 23.
    Choroś, K.: Video structure analysis for content-based indexing and categorisation of TV sports news. Int. J. Intelligent Information and Database Systems 6 (in press, 2012)Google Scholar
  24. 24.
    Choroś, K.: Video Structure Analysis and Content-Based Indexing in the Automatic Video Indexer AVI. In: Nguyen, N.T., Zgrzywa, A., Czyżewski, A., et al. (eds.) Advances in Multimedia and Network Information System Technologies. AISC, vol. 80, pp. 79–90. Springer, Heidelberg (2010)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  1. 1.Institute of InformaticsWrocław University of TechnologyWrocławPoland

Personalised recommendations