The Potential of User Feedback Through the Iterative Refining of Queries in an Image Retrieval System

  • Maher Ben Moussa
  • Marco Pasch
  • Djoerd Hiemstra
  • Paul van der Vet
  • Theo Huibers
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4398)


Inaccurate or ambiguous expressions in queries lead to poor results in information retrieval. We assume that iterative user feedback can improve the quality of queries. To this end we developed a system for image retrieval that utilizes user feedback to refine the user’s search query. This is done by a graphical user interface that returns categories of images and requires the user to choose between them in order to improve the initial query in terms of accuracy and unambiguousness. A user test showed that, although there was no improvement in search time or required search restarts, iterative user feedback can indeed improve the performance of an image retrieval system in terms of user satisfaction.


Image Retrieval User Satisfaction Relevance Feedback Query Expansion User Feedback 
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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  1. 1.
    Baeza-Yates, R., Ribiero-Neto, B.: Modern Information Retrieval. Addison-Wesley, Reading (1999)Google Scholar
  2. 2.
    Salton, G., Buckley, C.: Improving retrieval performance by relevance feedback. Journal of the American Association of Information Science 41(4), 288–297 (1990)CrossRefGoogle Scholar
  3. 3.
    Harman, D.: Relevance feedback revisited. In: Belkin, N., Ingwersen, P., Pejtersen, A. (eds.) Proceedings of the 15th International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 1–10. ACM Press, New York (1992)CrossRefGoogle Scholar
  4. 4.
    Godin, R., Gecsei, J., Pichet, C.: Design of a browsing interface for information retrieval. In: Belkin, N., van Rijsbergen, C. (eds.) Proceedings of the 12th International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 32–39. ACM Press, New York (1989)CrossRefGoogle Scholar
  5. 5.
    Open directory project (2006),, Date retrieved: 22 March 2006
  6. 6.
    Sieg, A., et al.: Using concept hierarchies to enhance user queries in web-based information retrieval. In: Proceedings of the IASTED International Conference on Artificial Intelligence and Applications (2004)Google Scholar
  7. 7.
    Rode, H., Hiemstra, D.: Using Query Profiles for Clarification. In: Lalmas, M., et al. (eds.) ECIR 2006. LNCS, vol. 3936, Springer, Heidelberg (2006)CrossRefGoogle Scholar
  8. 8.
    Lin, C.Y., Tseng, B.L., Smith, J.R.: Video Collaborative Annotation Forum: Establishing Ground-Truth Labels on Large Multimedia Datasets. In: Proceedings of the TRECVID video retrieval evaluation workshop (2003)Google Scholar
  9. 9.
    Hatcher, E., Gospodnetic, O.: Lucene in Action. Manning, Greenwich (2005)Google Scholar
  10. 10.
    FotoSearch Stock Photography and Stock Footage (2006),
  11. 11.
    Howitt, D., Cramer, D.: An Introduction to Statistics in Psychology, 2nd edn. Pearson Education Limited, Harlow (2000)Google Scholar
  12. 12.
    Dix, A., et al.: Human-computer interaction. Prentice Hall, New York (2003)Google Scholar
  13. 13.
    Nemeth, Y., Shapira, B., Taeib-Maimon, M.: Evaluation of the Real and Perceived Value of Automatic and Interactive Query Expansion. In: SIGIR’04, Sheffield, UK, ACM Press, New York (2004)Google Scholar

Copyright information

© Springer Berlin Heidelberg 2007

Authors and Affiliations

  • Maher Ben Moussa
    • 1
  • Marco Pasch
    • 1
  • Djoerd Hiemstra
    • 1
  • Paul van der Vet
    • 1
  • Theo Huibers
    • 1
  1. 1.University of Twente, P.O. Box 217, 7500 AE EnschedeThe Netherlands

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