Video Event Retrieval from a Small Number of Examples Using Rough Set Theory

  • Kimiaki Shirahama
  • Yuta Matsuoka
  • Kuniaki Uehara
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6523)


In this paper, we develop an example-based event retrieval method which constructs a model for retrieving events of interest in a video archive, by using examples provided by a user. But, this is challenging because shots of an event are characterized by significantly different features, due to camera techniques, settings and so on. That is, the video archive contains a large variety of shots of the event, while the user can only provide a small number of examples. Considering this, we use “rough set theory” to capture various characteristics of the event. Specifically, by using rough set theory, we can extract classification rules which can correctly identify different subsets of positive examples. Furthermore, in order to extract a larger variety of classification rules, we incorporate “bagging” and “random subspace method” into rough set theory. Here, we define indiscernibility relations among examples based on outputs of classifiers, built on different subsets of examples and different subsets of feature dimensions. Experimental results on TRECVID 2009 video data validate the effectiveness of our example-based event retrieval method.


Example-based event retrieval Rough set theory Bagging Random subspace method high-dimensional small sample size problem 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Peng, Y., Ngo, C.-W.: EMD-Based Video Clip Retrieval by Many-to-Many Matching. In: Leow, W.-K., Lew, M., Chua, T.-S., Ma, W.-Y., Chaisorn, L., Bakker, E.M. (eds.) CIVR 2005. LNCS, vol. 3568, pp. 71–81. Springer, Heidelberg (2005)CrossRefGoogle Scholar
  2. 2.
    Kashino, K., Kurozumi, T., Murase, H.: A Quick Search Method for Audio and Video Signals based on Histogram Pruning. IEEE Transactions on Multimedia 5(3), 348–357 (2003)CrossRefGoogle Scholar
  3. 3.
    Natsev, A., Naphade, M., Tešć, J.: Learning the Semantics of Multimedia Queries and Concepts from a Small Number of Examples. In: Proc. of ACM MM 2005, pp. 598–607 (2005)Google Scholar
  4. 4.
    Shirahama, K., Sugihara, C., Uehara, K.: Query-based Video Event Definition Using Rough Set Theory and High-dimensional Representation. In: Boll, S., Tian, Q., Zhang, L., Zhang, Z., Chen, Y.-P.P. (eds.) MMM 2010. LNCS, vol. 5916, pp. 358–369. Springer, Heidelberg (2010)CrossRefGoogle Scholar
  5. 5.
    McDonal, C.: Machine Learning: A Survey of Current Techniques. Artificial Intelligence Review 3, 243–280 (1989)CrossRefGoogle Scholar
  6. 6.
    Komorowski, J., Øhrn, A., Skowron, A.: The ROSETTA Rough Set Software System. In: Klösgen, W., Zytkow, J. (eds.) Handbook of Data Mining and Knowledge Discovery, ch. D.2.3. Oxford University Press, Oxford (2002)Google Scholar
  7. 7.
    Vapnik, V.: The Nature of Statistical Learning Theory, 2nd edn. Springer, Heidelberg (1999)MATHGoogle Scholar
  8. 8.
    Hsu, C., Chang, C., Lin, C.: A Practical Guide to Support Vector Classification,
  9. 9.
    Breiman, L.: Bagging Predictors. Machine Learning 24(2), 123–140 (1996)MATHGoogle Scholar
  10. 10.
    Ho, T.: The Random Subspace Method for Constructing Decision Forests. IEEE Transactions on Pattern Analysis and Machine Intelligence 20(8), 832–844 (1998)CrossRefGoogle Scholar
  11. 11.
    Sande, K., Gevers, T., Snoek, C.: Evaluating Color Descriptors for Object and Scene Recognition. IEEE Transactions on Pattern Analysis and Machine Intelligence 32(9), 1582–1596 (2010)CrossRefGoogle Scholar
  12. 12.
    Smeaton, A., Over, P., Kraaij, W.: Evaluation campaigns and TRECVid. In: Proc. of MIR 2006, pp. 321–330 (2006)Google Scholar
  13. 13.
    Tao, D., Tang, X., Li, X., Wu, X.: Asymmetric Bagging and Random Subspace for Support Vector Machines-based Relevance Feedback in Image Retrieval. IEEE Transactions on Pattern Analysis and Machine Intelligence 28(7), 1088–1099 (2006)CrossRefGoogle Scholar
  14. 14.
    Saha, S., Murthy, C., Pal, S.: Rough Set Based Ensemble Classifier for Web Page Classification. Fundamenta Informaticae 76(1-2), 171–187 (2007)MathSciNetGoogle Scholar
  15. 15.
    Guo, G., Dyer, C.: Learning from Examples in the Small Sample Case: Face Expression Recognition. IEEE Transactions on Systems, Man and Cybernetics - Part B 35(3), 477–488 (2005)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Kimiaki Shirahama
    • 1
  • Yuta Matsuoka
    • 2
  • Kuniaki Uehara
    • 2
  1. 1.Graduate School of EconomicsKobe UniversityKobeJapan
  2. 2.Graduate School of System InformaticsKobe UniversityKobeJapan

Personalised recommendations