Beyond Shot Retrieval: Searching for Broadcast News Items Using Language Models of Concepts

  • Robin Aly
  • Aiden Doherty
  • Djoerd Hiemstra
  • Alan Smeaton
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5993)


Current video search systems commonly return video shots as results. We believe that users may better relate to longer, semantic video units and propose a retrieval framework for news story items, which consist of multiple shots. The framework is divided into two parts: (1) A concept based language model which ranks news items with known occurrences of semantic concepts by the probability that an important concept is produced from the concept distribution of the news item and (2) a probabilistic model of the uncertain presence, or risk, of these concepts. In this paper we use a method to evaluate the performance of story retrieval, based on the TRECVID shot-based retrieval groundtruth. Our experiments on the TRECVID 2005 collection show a significant performance improvement against four standard methods.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Aly, R., Hiemstra, D., de Vries, A.P.: Reusing annotation labor for concept selection. In: CIVR 2009: Proceedings of the International Conference on Content-Based Image and Video Retrieval 2009. ACM, New York (2009)Google Scholar
  2. 2.
    Aslam, J.A., Montague, M.: Models for metasearch. In: SIGIR 2001: Proceedings of the 24th annual international ACM SIGIR conference on Research and development in information retrieval, pp. 276–284. ACM, New York (2001)CrossRefGoogle Scholar
  3. 3.
    Byrne, D., Doherty, A., Snoek, C.G.M., Jones, G., Smeaton, A.F.: Everyday concept detection in visual lifelogs: Validation. relationships and trends. Multimedia Tools and Applications Journal (2009)Google Scholar
  4. 4.
    Chelba, C., Acero, A.: Position specific posterior lattices for indexing speech. In: ACL 2005: Proceedings of the 43rd Annual Meeting on Association for Computational Linguistics, Morristown, NJ, USA, pp. 443–450. Association for Computational Linguistics (2005)Google Scholar
  5. 5.
    Chia, T.K., Sim, K.C., Li, H., Ng, H.T.: A lattice-based approach to query-by example spoken document retrieval. In: SIGIR 2008. Proceedings of the 31st annual international ACM SIGIR conference on Research and development in information retrieval, pp. 363–370. ACM, New York (2008)CrossRefGoogle Scholar
  6. 6.
    Donald, K.M., Smeaton, A.F.: A comparison of score, rank and probabilitybased fusion methods for video shot retrieval. In: Leow, W.-K., Lew, M., Chua, T.-S., Ma, W.-Y., Chaisorn, L., Bakker, E.M. (eds.) CIVR 2005. LNCS, vol. 3568, pp. 61–70. Springer, Heidelberg (2005)Google Scholar
  7. 7.
    Hauff, C., Aly, R., Hiemstra, D.: The effectiveness of concept based search for video retrieval. In: Workshop Information Retrieval (FGIR 2007), Halle-Wittenberg. LWA 2007: Lernen - Wissen - Adaption, vol. 2007, pp. 205–212. Gesellschaft fuer Informatik (2007)Google Scholar
  8. 8.
    Hiemstra, D.: Using Language Models for Information Retrieval. PhD thesis, University of Twente, Enschede (January 2001)Google Scholar
  9. 9.
    Hsu, W.H., Kennedy, L.S., Chang, S.-F.: Video search reranking via information bottleneck principle. In: MULTIMEDIA 2006: Proceedings of the 14th annual ACM international conference on Multimedia, pp. 35–44. ACM Press, New York (2006)CrossRefGoogle Scholar
  10. 10.
    Mamou, J., Carmel, D., Hoory, R.: Spoken document retrieval from call-center conversations. In: SIGIR 2006: Proceedings of the 29th annual international ACM SIGIR conference on Research and development in information retrieval, pp. 51–58. ACM Press, New York (2006)CrossRefGoogle Scholar
  11. 11.
    Markowitz, H.: Portfolio selection. The Journal of Finance 7(1), 77–91 (1952)CrossRefGoogle Scholar
  12. 12.
    Natsev, A.P., Haubold, A., Tešić, J., Xie, L., Yan, R.: Semantic concept-based query expansion and re-ranking for multimedia retrieval. In: MULTIMEDIA 2007: Proceedings of the 15th international conference on Multimedia, pp. 991–1000. ACM Press, New York (2007)CrossRefGoogle Scholar
  13. 13.
    Petersohn, C.: Fraunhofer hhi at trecvid 2004: Shot boundary detection system. In: TREC Video Retrieval Evaluation Online Proceedings, TRECVID (2004)Google Scholar
  14. 14.
    Platt, J.: Advances in Large Margin Classifiers. In: Probabilistic outputs for support vector machines and comparison to regularized likelihood methods, pp. 61–74. MIT Press, Cambridge (2000)Google Scholar
  15. 15.
    Ponte, J.M.: A language modeling approach to information retrieval. PhD thesis, University of Massachusetts Amherst (1998)Google Scholar
  16. 16.
    Smeaton, A.F., Over, P., Doherty, A.R.: Video shot boundary detection: Seven years of trecvid activity. Computer Vision and Image Understanding (2009)Google Scholar
  17. 17.
    Smeaton, A.F., Over, P., Kraaij, W.: Evaluation campaigns and trecvid. In: MIR 2006: Proceedings of the 8th ACM International Workshop on Multimedia Information Retrieval, pp. 321–330. ACM Press, New York (2006)CrossRefGoogle Scholar
  18. 18.
    Snoek, C.G.M., Worring, M.: Concept-based video retrieval. Foundations and Trends in Information Retrieval 4(2), 215–322 (2009)Google Scholar
  19. 19.
    Snoek, C.G.M., Worring, M., van Gemert, J.C., Geusebroek, J.-M., Smeulders, A.W.M.: The challenge problem for automated detection of 101 semantic concepts in multimedia. In: MULTIMEDIA 2006: Proceedings of the 14th annual ACM international conference on Multimedia, pp. 421–430. ACM Press, New York (2006)CrossRefGoogle Scholar
  20. 20.
    Varian, H.R.: Economics and search. SIGIR Forum 33(1), 1–5 (1999)CrossRefGoogle Scholar
  21. 21.
    Voorhees, E.M., Harman, D.: Overview of the ninth text retrieval conference (trec-9). In: Proceedings of the Ninth Text REtrieval Conference TREC-9, pp. 1–14 (2000)Google Scholar
  22. 22.
    Wang, J.: Mean-variance analysis: A new document ranking theory in information retrieval. In: ECIR 2009: Proceedings of the 31th European Conference on IR Research on Advances in Information Retrieval, pp. 4–16. Springer, Heidelberg (2009)Google Scholar
  23. 23.
    Zhai, C., Lafferty, J.: A study of smoothing methods for language models applied to information retrieval. ACM Trans. Inf. Syst. 22(2), 179–214 (2004)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • Robin Aly
    • 1
  • Aiden Doherty
    • 2
  • Djoerd Hiemstra
    • 1
  • Alan Smeaton
    • 2
  1. 1.Datbase SystemsUniversity TwenteEnschedeThe Netherlands
  2. 2.CLARITY: Center for Sensor Web TechnologyDublin City UniversityIreland

Personalised recommendations