Fast Content-Based Mining of Web2.0 Videos

  • Sébastien Poullot
  • Michel Crucianu
  • Olivier Buisson
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5353)

Abstract

The accumulation of many transformed versions of the same original videos on Web2.0 sites has a negative impact on the quality of the results presented to the users and on the management of content by the provider. An automatic identification of such content links between video sequences can address these difficulties. We put forward a fast solution to this video mining problem, relying on a compact keyframe descriptor and an adapted indexing solution. Two versions are developed, an off-line one for mining large databases and an online one to quickly post-process the results of keyword-based interactive queries. After demonstrating the reliability of the method on a ground truth, the scalability on a database of 10,000 hours of video and the speed on 3 interactive queries, some results obtained on Web2.0 content are illustrated.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Arasu, A., Ganti, V., Kaushik, R.: Efficient exact set-similarity joins. In: Proc. 32nd intl. conf. on Very Large Data Bases, VLDB Endowment, pp. 918–929 (2006)Google Scholar
  2. 2.
    Bayardo, R.J., Ma, Y., Srikant, R.: Scaling up all pairs similarity search. In: Proc. 16th intl. conf. on World Wide Web, pp. 131–140. ACM, New York (2007)CrossRefGoogle Scholar
  3. 3.
    Fagin, R., Kumar, R., Sivakumar, D.: Efficient similarity search and classification via rank aggregation. In: Proc. ACM SIGMOD intl. conf. on Management of Data, pp. 301–312. ACM, New York (2003)Google Scholar
  4. 4.
    Gionis, A., Indyk, P., Motwani, R.: Similarity search in high dimensions via hashing. In: Proc. 25th intl. conf. on Very Large Data Bases, pp. 518–529. Morgan Kaufmann Inc., San Francisco (1999)Google Scholar
  5. 5.
    Lejsek, H., Ásmundsson, F.H., Jónsson, B.T., Amsaleg, L.: Scalability of local image descriptors: a comparative study. In: Proc. 14th ACM intl. conf. on Multimedia, pp. 589–598. ACM, New York (2006)CrossRefGoogle Scholar
  6. 6.
    Law-To, J., Chen, L., Joly, A., Laptev, I., Buisson, O., Gouet-Brunet, V., Boujemaa, N., Stentiford, F.: Video copy detection: a comparative study. In: Proc. 6th ACM intl. Conf. on Image and Video Retrieval, pp. 371–378. ACM, New York (2007)CrossRefGoogle Scholar
  7. 7.
    Oostveen, J., Kalker, T., Haitsma, J.: Feature extraction and a database strategy for video fingerprinting. In: Chang, S.-K., Chen, Z., Lee, S.-Y. (eds.) VISUAL 2002. LNCS, vol. 2314, pp. 117–128. Springer, Heidelberg (2002)CrossRefGoogle Scholar
  8. 8.
    Poullot, S., Buisson, O., Crucianu, M.: Z-grid-based probabilistic retrieval for scaling up content-based copy detection. In: Proc. ACM intl. Conf. on Image and Video Retrieval, Amsterdam, The Netherlands, pp. 348–355 (July 2007)Google Scholar
  9. 9.
    Satoh, S.: News video analysis based on identical shot detection. In: Proc. IEEE Intl. Conf. on Multimedia and Expo., pp. 69–72 (2002)Google Scholar
  10. 10.
    Satoh, S., Takimoto, M., Adachi, J.: Scene duplicate detection from videos based on trajectories of feature points. In: Proc. intl. workshop on Multimedia Information Retrieval, pp. 237–244. ACM, New York (2007)CrossRefGoogle Scholar
  11. 11.
    Sivic, J., Zisserman, A.: Video Google: A text retrieval approach to object matching in videos. In: Proc. 9th IEEE Intl. Conf. on Computer Vision, pp. 1470–1477. IEEE Computer Society, Washington (2003)CrossRefGoogle Scholar
  12. 12.
    Takimoto, M., Satoh, S., Sakauchi, M.: Identification and detection of the same scene based on flash light patterns. In: Proc. IEEE Intl. Conf. on Multimedia and Expo., pp. 9–12. IEEE Computer Society, Los Alamitos (2006)Google Scholar
  13. 13.
    Wu, X., Hauptmann, A.G., Ngo, C.-W.: Novelty detection for cross-lingual news stories with visual duplicates and speech transcripts. In: Proc. 15th intl. conf. on Multimedia, pp. 168–177. ACM, New York (2007)CrossRefGoogle Scholar
  14. 14.
    Wu, X., Hauptmann, A.G., Ngo, C.-W.: Practical elimination of near-duplicates from web video search. In: Proc. 15th intl. conf. on Multimedia, pp. 218–227. ACM, New York (2007)CrossRefGoogle Scholar
  15. 15.
    Wu, X., Zhao, W.-L., Ngo, C.-W.: Near-duplicate keyframe retrieval with visual keywords and semantic context. In: Proc. 6th ACM intl. Conf. on Image and Video Retrieval, pp. 162–169. ACM, New York (2007)CrossRefGoogle Scholar
  16. 16.
    Yamagishi, F., Satoh, S., Sakauchi, M.: A news video browser using identical video segment detection. In: Aizawa, K., Nakamura, Y., Satoh, S. (eds.) PCM 2004. LNCS, vol. 3332, pp. 205–212. Springer, Heidelberg (2004)CrossRefGoogle Scholar
  17. 17.
    Zhai, Y., Shah, M.: Tracking news stories across different sources. In: Proc. 13th ACM intl. conf. on Multimedia, pp. 2–10. ACM, New York (2005)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2008

Authors and Affiliations

  • Sébastien Poullot
    • 1
    • 2
  • Michel Crucianu
    • 1
  • Olivier Buisson
    • 2
  1. 1.Vertigo - CNAMParis cedex 03France
  2. 2.Institut National de l’AudiovisuelBry-sur-MarneFrance

Personalised recommendations