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LIG at ImageCLEF 2008

  • Loic Maisonnasse
  • Philippe Mulhem
  • Eric Gaussier
  • Jean Pierre Chevallet
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5706)

Abstract

This paper describes the work of the LIG for ImageCLEF 2008. For ImageCLEFPhoto, two non diversified runs (text only and text + image), and two diversified runs were officially submitted. We add in this paper results on image only runs. The text retrieval part is based on a language model of Information Retrieval, and the image part uses RGB histograms. Text+image results are obtained by late fusion, by merging text and image results. We tested three strategies for promoting diversity using date/location or visual features. Diversification on image only runs does not perform well. Diversification on image and text+image outperforms non diversified runs. In a second part, this paper describes the runs and results obtained by the LIG at ImageCLEFmed 2008. This contribution incorporates knowledge in the language modeling approach to information retrieval (IR) through the graph modeling approach proposed in . Our model makes use of the textual part of the corpus and of the medical knowledge found in the Unified Medical Language System (UMLS) knowledge sources. And the model is extended to combine different graph detection methods on queries and documents. The results show that detection combination improves the performances.

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References

  1. 1.
    Müller, H., Kalpathy-Cramer, J., Kahn Jr., C., Hatt, W., Bedrick, S., Hersh, W.: Overview of the ImageCLEFmed 2008 medical image retrieval task. In: Peters, C., et al. (eds.) CLEF 2008. LNCS, vol. 5706, pp. 512–522. Springer, Heidelberg (2009)Google Scholar
  2. 2.
    Jianfeng, G., Jian-Yun, N., Guangyuan, W., Guihong, C.: Dependence language model for information retrieval. In: SIGIR 2004 (2004)Google Scholar
  3. 3.
    Maisonnasse, L., Gaussier, E., Chevallet, J.P.: Multi-relation modeling on multi concept extraction lig participation at imageclefmed. In: Working Notes for the CLEF 2008 Workshop, Aarhus, Denmark, September 17-19 (2008)Google Scholar
  4. 4.
    Maisonnasse, L., Gaussier, E., Chevallet, J.P.: Multiplying concept sources for graph modeling. 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. 585–592. Springer, Heidelberg (2008)CrossRefGoogle Scholar
  5. 5.
    Ponte, J.M., Croft, W.B.: A language modeling approach to information retrieval. In: Proceedings of the ACM SIGIR, pp. 275–281 (1998)Google Scholar
  6. 6.
    Robertson, S.E., Walker, S., Jones, S., Hancock-beaulieu, M.M., Gatford, M.: Okapi at trec-3, pp. 109–126 (1995)Google Scholar
  7. 7.
    Salton, G., McGill, M.J.: Introduction to modern information retrieval. McGraw-Hill, New York (1983)zbMATHGoogle Scholar
  8. 8.
    The Zettair search engine, http://www.seg.rmit.edu.au/zettair/
  9. 9.
    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
  10. 10.
    Zhou, W., Yu, C., Smalheiser, N., Torvik, V., Hong, J.: Knowledge-intensive conceptual retrieval and passage extraction of biomedical literature. In: Research and Development in Information Retrieval (2007)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • Loic Maisonnasse
    • 1
  • Philippe Mulhem
    • 2
  • Eric Gaussier
    • 2
  • Jean Pierre Chevallet
    • 2
  1. 1.Université de Lyon, INSA-Lyon, LIRISFrance
  2. 2.Grenoble University, LIGFrance

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