Image Searching and Browsing by Active Aspect-Based Relevance Learning

  • Mark J. Huiskes
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4071)


Aspect-based relevance learning is a relevance feedback scheme based on a natural model of relevance in terms of image aspects. In this paper we propose a number of active learning and interaction strategies, capitalizing on the transparency of the aspect-based framework. Additionally, we demonstrate that, relative to other schemes, aspect-based relevance learning upholds its retrieval performance well under feedback consisting mainly of example images that are only partially relevant.


Image Retrieval Target Image Relevance Feedback Image Searching Relevant Image 
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.
    Zhang, H., Zheng, C., Li, M., Su, Z.: Relevance feedback and learning in content-based image search. WWW: Internet and web information systems 6, 131–155 (2003)Google Scholar
  2. 2.
    Zhou, X., Huang, T.: Relevance feedback in image retrieval: A comprehensive review. ACM Multimedia Systems Journal 8(6), 536–544 (2003)CrossRefGoogle Scholar
  3. 3.
    Huiskes, M.: Aspect-based relevance learning for image retrieval. In: Leow, W.-K., Lew, M., Chua, T.-S., Ma, W.-Y., Chaisorn, L., Bakker, E.M. (eds.) CIVR 2005. LNCS, vol. 3568, pp. 639–649. Springer, Heidelberg (2005)CrossRefGoogle Scholar
  4. 4.
    Tong, S., Chang, E.: Support vector machine active learning for image retrieval. In: Proc. of 9th ACM Int. Conference on Multimedia, pp. 107–118 (2001)Google Scholar
  5. 5.
    Cord, M., Gosselin, P., Philipp-Foliguet, S.: Stochastic exploration and active learning for image retrieval. Image and Vis. Computing (January 2006) (accepted for publication)Google Scholar
  6. 6.
    Zhou, Z.-H., Chen, K.-J., Jiang, Y.: Exploiting unlabeled data in content-based image retrieval. In: Boulicaut, J.-F., Esposito, F., Giannotti, F., Pedreschi, D. (eds.) ECML 2004. LNCS (LNAI), vol. 3201, pp. 525–536. Springer, Heidelberg (2004)CrossRefGoogle Scholar
  7. 7.
    Zhang, H., Su, Z.: Relevance feedback in CBIR. In: Zhou, X., Pu, P. (eds.) Visual and Multimedia Information Systems, pp. 21–35. Kluwer Academic Publishers, Dordrecht (2002)Google Scholar
  8. 8.
    Canu, S., Grandvalet, Y., Guigue, V., Rakotomamonjy, A.: SVM and kernel methods Matlab toolbox. In: Perception Syst. et Inf., INSA de Rouen, France (2005)Google Scholar
  9. 9.
    Chen, Y., Zhou, X., Huang, T.: One-class SVM for learning in image retrieval. In: Proc. IEEE ICIP 2001, Thessaloniki, Greece, vol. 1, pp. 34–37 (2001)Google Scholar
  10. 10.
    Hoi, C., Lyu, M.: Biased support vector machine for relevance feedback in image retrieval. In: Proc. Intl. Joint Conf. on Neural Networks, Budapest, Hungary (2004)Google Scholar
  11. 11.
    Rocchio Jr., J.: Relevance feedback in information retrieval. In: Salton, G. (ed.) The SMART retrieval system: experiments in automatic document processing, pp. 313–323. Prentice-Hall, Englewood Cliffs (1971)Google Scholar
  12. 12.
    Ciocca, G., Schettini, R.: A relevance feedback mechanism for content-based image retrieval. Information Processing and Management 35(5), 605–632 (1999)CrossRefGoogle Scholar
  13. 13.
    Huiskes, M., Pauwels, E.: Indexing, learning and CBR for special purpose image databases. In: Zelkowitz, M. (ed.) Advances in Computers. Elsevier, Amsterdam (2005)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Mark J. Huiskes
    • 1
  1. 1.Centre for Mathematics and Computer Science (CWI)AmsterdamThe Netherlands

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