Advertisement

Pic-A-Topic: Gathering Information Efficiently from Recorded TV Shows on Travel

  • Tetsuya Sakai
  • Tatsuya Uehara
  • Kazuo Sumita
  • Taishi Shimomori
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4182)

Abstract

We introduce a system called Pic-A-Topic, which analyses closed captions of Japanese TV shows on travel to perform topic segmentation and topic sentence selection. Our objective is to provide a table-of-contents interface that enables efficient viewing of desired topical segments within recorded TV shows to users of appliances such as hard disk recorders and digital TVs. According to our experiments using 14.5 hours of recorded travel TV shows, Pic-A-Topic’s F1-measure for the topic segmentation task is 82% of manual performance on average. Moreover, a preliminary user evaluation experiment suggests that this level of performance may be indistinguishable from manual performance.

Keywords

Broadcast News Shot Boundary Topic Sentence Topic Word Electronic Program Guide 
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.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Aoki, H., Shimotsuji, S., Hori, O.: A Shot Classification Method of Selecting Key-Frames for Video Browsing. In: Proceedings of ACM Multimedia 1996 (1996)Google Scholar
  2. 2.
    Aoki, H.: High-Speed Topic Organizer of TV Shows Using Video Dialog Detection (in Japanese). IEICE Transactions on Information and Systems J88-D-II(1), 17–27 (2005)Google Scholar
  3. 3.
    Boykin, S., Merlino, A.: Machine Learning of Event Segmentation for News on Demand. Communications of the ACM 43(2), 35–41 (2000)CrossRefGoogle Scholar
  4. 4.
    Chua, T.-S., et al.: Story Boundary Detection in Large Broadcast News Video Archives - Techniques, Experience and Trends. In: Proceedings of ACM Multimedia 2004 (2004)Google Scholar
  5. 5.
    Hauptmann, A.G., Lee, D.: Topic Labeling of Broadcast News Stories in the Informedia Digital Video Library. In: Proceedings of ACM Digital Libraries 1998 (1998)Google Scholar
  6. 6.
    Hauptmann, A.G., Witbrock, M.J.: Story Segmentation and Detection of Commercials in Broadcast News Video. Advances in Digital Libraries 1998 (1998)Google Scholar
  7. 7.
    Hearst, M.A.: Multi-Paragraph Segmentation of Expository Text. In: Proceedings of ACL 1994, pp. 9–16 (1994)Google Scholar
  8. 8.
    Ide, I., et al.: Threading News Video Topics. In: ACM SIGMM Workshop on Multimedia Information Retrieval (MIR 2003), pp. 239–246 (2003)Google Scholar
  9. 9.
    Jasinschi, R.S., et al.: Integrated Multimedia Processing for Topic Segmentation and Classification. In: Proceedings of IEEE ICIP (2001)Google Scholar
  10. 10.
    Mani, I., et al.: The TIPSTER SUMMAC Text Summarization Evaluation. In: Proceedings of EACL 1999, pp. 77–85 (1999)Google Scholar
  11. 11.
    Miyamori, H., Tanaka, K.: Webified Video: Media Conversion from TV Program to Web Content and their Integrated Viewing Method. In: Proceedings of ACM WWW 2005 (2005)Google Scholar
  12. 12.
    Nitta, N., Babaguchi, N.: Story Segmentation of Broadcasted Sports Videos for Semantic Content Acquisition (in Japanese). IEICE Transactions on Information and Systems J86-D-II 8, 1222–1233 (2003)Google Scholar
  13. 13.
    Over, P., Kraaij, W., Smeaton, A.F.: TRECVID 2005 - An Introduction. In: Proceedings of TREC 2005 (2005)Google Scholar
  14. 14.
    Pickering, M., Wong, L., Rüger, S.M.: ANSES: Summarisation of News Video. In: Bakker, E.M., Lew, M., Huang, T.S., Sebe, N., Zhou, X.S. (eds.) CIVR 2003. LNCS, vol. 2728, pp. 481–486. Springer, Heidelberg (2003)CrossRefGoogle Scholar
  15. 15.
    Robertson, S.E., Sparck Jones, K.: Simple, Proven Approaches to Text Retrieval. University of Cambridge Computer Laboratory, TR356 (1997)Google Scholar
  16. 16.
    Rui, Y., Gupta, A., Acero, A.: Automatically Extracting Highlights for TV Baseball Programs. In: Proceedings of ACM Multimedia 2000(2000)Google Scholar
  17. 17.
    Sakai, T., et al.: Efficient Analysis of Student Questionnaires using Information Retrieval Techniques (in Japanese). In: Proceedings of the National Conference 2003/Spring of the Japan Society for Management Information, pp. 182–185 (2003)Google Scholar
  18. 18.
    Sakai, T., et al.: ASKMi: A Japanese Question Answering System based on Semantic Role Analysis. In: Proceedings of RIAO 2004, pp. 215–231 (2004)Google Scholar
  19. 19.
    Sakai, T.: Advanced Technologies for Information Access. International Journal of Computer Processing of Oriental Languages 18(2), 95–113 (2005)CrossRefGoogle Scholar
  20. 20.
    Smeaton, A.F., et al.: The Físchlár-News-Stories System: Personalised Access to an Archive of TV News. In: Proceedings of RIAO 2004 (2004)Google Scholar
  21. 21.
    Smith, M.A., Kanade, T.: Video Skimming and Characterization through the Combination of Image and Language Understanding. In: Proceedings of IEEE ICCV 1998 (1998)Google Scholar
  22. 22.
    Uehara, T., Horikawa, M., Sumita, K.: Navigation System for News Programs Featuring Direct Access to Desired Scenes (in Japanese). Toshiba Review 55(10) (2000)Google Scholar
  23. 23.
    Yamada, I., et al.: Meta-Data Generation for Football Games using Announcer’s Commentary (in Japanese). In: Proceedings of Forum on Information Technology 2004, pp. 177–178 (2004)Google Scholar
  24. 24.
    Zhang, H.-J., et al.: Video Parsing, Retrieval and Browsing: An Integrated and Content-Based Solution. In: ACM Multimedia 1995, pp. 15–24 (1995)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Tetsuya Sakai
    • 1
  • Tatsuya Uehara
    • 1
  • Kazuo Sumita
    • 1
  • Taishi Shimomori
    • 1
  1. 1.Toshiba Corporate R&D CenterKawasakiJapan

Personalised recommendations