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An Interactive Video Retrieval Approach Based on Latent Topics

  • Rubén Fernández-Beltran
  • Filiberto Pla
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8156)

Abstract

The huge collections of unconstrained videos have amplified the so-called semantic gap for content-based video retrieval. Therefore, new efficient approaches with higher generalisation power are needed. In this work, we present an interactive video retrieval approach based on latent topics to cope with the semantic gap in an efficient way. A supervised Symmetric extension of probabilistic Latent Semantic Analysis model is presented (sSpLSA). Then, this model is adapted to an on-line interactive information retrieval problem and it is applied to a video retrieval framework based on explicit short-term Relevance Feedback (RF) where queries are inside the database. Finally, several retrieval simulations using the Consumer Columbia Video (CCV) database are performed to compare the proposed approach with a distance-based RF baseline.

Keywords

Content-based video retrieval relevance feedback latent topics 

References

  1. 1.
  2. 2.
    The challenge problem for automated detection of 101 semantic concepts in multimedia (2006)Google Scholar
  3. 3.
    Blei, D.: Probabilistic topic models. Communications of the ACM 55(4), 77–84 (2012)MathSciNetCrossRefGoogle Scholar
  4. 4.
    Blei, D., Ng, A., Jordan, M.: Latent dirichlet allocation. Journal of Machine Learning Research 3(4-5), 993–1022 (2003)MATHGoogle Scholar
  5. 5.
    Bosch, A., Zisserman, A., Munoz, X.: Scene classification via 2009. In: European Conference on Computer Vision (2009)Google Scholar
  6. 6.
    Brants, T., Chen, F., Tsochantaridis, I.: Topic-based document segmentation with probabilistic latent semantic analysis. In: International Conference on Information and Knowledge Management (2002)Google Scholar
  7. 7.
    Chechik, G., Sharma, V., Shalit, U., Bengio, S.: Large scale online learning of image similarity through ranking. Journal of Machine Learning Research 11, 1109–1135 (2010)MathSciNetMATHGoogle Scholar
  8. 8.
    Fei-Fei, L., Perona, P.: A bayesian hierarchical model for learning natural scene categories. In: IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR (2005)Google Scholar
  9. 9.
    Hofmann, T.: Unsupervised learning by probabilistic latent semantic analysis. Machine Learning 42(1-2), 177–196 (2001)CrossRefMATHGoogle Scholar
  10. 10.
    Huang, J., Kumar, S., Mitra, M., Zhu, W., Zabih, R.: Image indexing using color correlograms. In: IEEE Int. Conf. Computer Vision and Pattern Recognition (1997)Google Scholar
  11. 11.
    Jiang, Y., Bhattacharya, S., Chang, S., Shah, M.: High-level event recognition in unconstrained videos. International Journal of Multimedia Information Retrieval 2, 73–101 (2013)CrossRefGoogle Scholar
  12. 12.
    Jiang, Y., Ye, G., Chang, S., Ellis, D., Loui, A.: Consumer video understanding: A benchmark database and an evaluation of human and machine performance. In: Proceedings ACM International Conference on Multimedia Retrieval, ICMR (2011)Google Scholar
  13. 13.
    Monay, F., Gatica-Perez, D.: On image auto-annotation with latent space models. In: ACM International Conference on Multimedia (2003)Google Scholar
  14. 14.
    Ren, W., Singh, S., Singh, M., Zhu, Y.: State-of-the-art on spatio-temporal information-based video retrieval. Pattern Recognition 42(2), 267–282 (2009)CrossRefMATHGoogle Scholar
  15. 15.
    Smeulders, A., Worring, M., Santini, S., Gupta, A., Jain, R.: Content-based image retrieval at the end of the early years. IEEE Transactions on Pattern Analysis and Machine Intelligence 22(12), 1349–1380 (2000)CrossRefGoogle Scholar
  16. 16.
    Snoek, C., Worring, M.: Concept-based video retrieval. Foundations and Trends in Information Retrieval 4(2), 215–322 (2009)Google Scholar
  17. 17.
    Zhou, D., Weston, J., Gretton, A., Bousquet, O., Schölkopf, B.: Ranking on data manifolds. In: Advances in Neural Information Processing Systems. NIPS. MIT Press (2004)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Rubén Fernández-Beltran
    • 1
  • Filiberto Pla
    • 1
  1. 1.Institute of New Imaging TechnologyJaume I UniversityCastellónSpain

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