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Evaluation of Diversity-Focused Strategies for Multimedia Retrieval

  • Julien Ah-Pine
  • Gabriela Csurka
  • Jean-Michel Renders
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5706)

Abstract

In this paper, we propose and evaluate different strategies to promote diversity in the top results of multimedia retrieval systems. These strategies consist in clustering, explicitly or implictly, the elements of the top list of some initial ranking and produce a re-ranking that favours elements belonging to different clusters. We evaluate these strategies in the particular case of ImageCLEFPhoto 2008 Collection. Results show that most of these strategies succeed in increasing a diversity performance measure, while keeping or slightly degrading precision of the top list and, more interestingly, they achieve this in complementary ways.

Keywords

Query Image Multimedia Retrieval Basic Ranking Sport Stadium Ground Truth Cluster 
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.

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References

  1. 1.
    Clinchant, S., Renders, J.M., Csurka, G.: Trans-media pseudo-relevance feedback methods in multimedia retrieval. In: Peters, C., Jijkoun, V., Mandl, T., Müller, H., Oard, D.W., Peñas, A., Petras, V., Santos, D. (eds.) CLEF 2007. LNCS, vol. 5152, pp. 569–576. Springer, Heidelberg (2008)CrossRefGoogle Scholar
  2. 2.
    Ah-Pine, J., Cifarelli, C., Clinchant, S., Csurka, G., Renders, J.: XRCE’s participation to imageCLEF 2008. In: Working Notes of the 2008 CLEF Workshop, Aarhus, Denmark (2008)Google Scholar
  3. 3.
    Ponte, J., Croft, W.: A language modelling approach to information retrieval. In: Proceedings of the 21st Annual International ACM SIGIR Conference on Information Retrieval, Melbourne, Australia, pp. 275–281. ACM, New York (1998)Google Scholar
  4. 4.
    Perronnin, F., Dance, C.: Fisher kernels on visual vocabularies for image categorization. In: Proceedings of IEEE CVPR Computer Vision and Pattern Recognition, Minneapolis, Minnesota, USA (2007)Google Scholar
  5. 5.
    Carbonell, J., Goldstein, J.: The use of MMR, diversity-based reranking for reordering documents and producing summaries. In: SIGIR 1998: Proceedings of the 21st annual international ACM SIGIR conference on Research and development in information retrieval, Melbourne, Australia, pp. 335–336. ACM, New York (1998)Google Scholar
  6. 6.
    Michaud, P., Marcotorchino, F.: Modeles d’optimisation en analyse des données relationnelles. Math. Sci. Hum. 67, 7–38 (1979)zbMATHGoogle Scholar
  7. 7.
    Marcotorchino, J., Michaud, P.: Heuristic approach of the similarity aggregation problem. Methods of Operation Research 43, 395–404 (1981)zbMATHGoogle Scholar
  8. 8.
    Arni, T., Clough, P., Sanderson, M., Grubinger, M.: Overview of the ImageCLEFphoto 2008 photographic retrieval task. In: Peters, C., et al. (eds.) CLEF 2008. LNCS, vol. 5706, pp. 500–511. Springer, Heidelberg (2009)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • Julien Ah-Pine
    • 1
  • Gabriela Csurka
    • 1
  • Jean-Michel Renders
    • 1
  1. 1.Xerox Research Centre EuropeMeylanFrance

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