Advertisement

Stretching Bayesian Learning in the Relevance Feedback of Image Retrieval

  • Ruofei Zhang
  • Zhongfei (Mark) Zhang
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3023)

Abstract

This paper is about the work on user relevance feedback in image retrieval. We take this problem as a standard two-class pattern classification problem aiming at refining the retrieval precision by learning through the user relevance feedback data. However, we have investigated the problem by noting two important unique characteristics of the problem: small sample collection and asymmetric sample distributions between positive and negative samples. We have developed a novel approach to stretching Bayesian learning to solve for this problem by explicitly exploiting the two unique characteristics, which is the methodology of BAyesian Learning in Asymmetric and Small sample collections, thus called BALAS. Different learning strategies are used for positive and negative sample collections in BALAS, respectively, based on the two unique characteristics. By defining the relevancy confidence as the relevant posterior probability, we have developed an integrated ranking scheme in BALAS which complementarily combines the subjective relevancy confidence and the objective feature-based distance measure to capture the overall retrieval semantics. The experimental evaluations have confirmed the rationale of the proposed ranking scheme, and have also demonstrated that BALAS is superior to an existing relevance feedback method in the current literature in capturing the overall retrieval semantics.

Keywords

Support Vector Machine Image Retrieval Image Database Negative Sample Query 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.

References

  1. 1.
    Bimbo, A.D.: Visual Information Retrieval. Morgan kaufmann Pub., San Francisco (1999)Google Scholar
  2. 2.
    Blom, G.: Probability and Statistics: Theory and Applications. Springer, London (1989)zbMATHGoogle Scholar
  3. 3.
    Chiu, S.-T.: A comparative review of bandwidth selection for kernel density estimation. Statistica Sinica 16, 129–145 (1996)MathSciNetGoogle Scholar
  4. 4.
    Cox, I.J., Miller, M.L., Minka, T.P., Papathomas, T.V., Yianilos, P.N.: The bayesian image retrieval system, pichunter: Theory, implementation and psychophysical experiments. IEEE Trans. on Image Processing 9(1), 20–37 (2000)CrossRefGoogle Scholar
  5. 5.
    Dillon, W.R., Goldstein, M.: Multivariate Analysis, Mehtods and Applications. John Wiley and Sons, New York (1984)Google Scholar
  6. 6.
    Duda, R.O., Hart, P.E.: Pattern Classification and Scene Analysis. John Wiley and Sons, New York (1973)zbMATHGoogle Scholar
  7. 7.
    Ishikawa, Y., Subramanya, R., Faloutsos, C.: Mindreader: Query databases through multiple examples. In: Proceedings the 24th VLDB Conference, New York (1998)Google Scholar
  8. 8.
    Marsicoi, M.D., Cinque, L., Levialdi, S.: Indexing pictorial documents by their content: a survey of current techniques. Imagee and Vision Computing 15, 119–141 (1997)CrossRefGoogle Scholar
  9. 9.
    Ripley, B.D., Venables, W.N.: Modern Applied Statistics with S. Springer, New York (2002)zbMATHGoogle Scholar
  10. 10.
    Rocchio, J.J.: Relevance feedback in information retrieval. In: The SMART Retreival System — Experiments in Automatic Document Processing, pp. 313–323. Prentice Hall, Inc., Englewood Cliffs (1971)Google Scholar
  11. 11.
    Rui, Y., Huang, T.S.: Optimizing learning in image retrieval. In: IEEE Conf. Computer Vision and Pattern Recognition, South Carolina (June 2000)Google Scholar
  12. 12.
    Silverman, B.W.: Density Estimation for Statistics and Data Analysis. Chapman and Hall, New York (1986)zbMATHGoogle Scholar
  13. 13.
    Terrell, G.R., Scott, D.W.: Variable kernel density estimation. The Annals of Statistics 20, 1236–1265 (1992)zbMATHCrossRefMathSciNetGoogle Scholar
  14. 14.
    Tieu, K., Viola, P.: Boosting image retrieval. In: Proceedings IEEE Conf. Computer Vision and Pattern Recognitin, South Carolina (June 2000)Google Scholar
  15. 15.
    Vapnik, V.: The Nature of Statistical Learning Theory. Springer, New York (1995)zbMATHGoogle Scholar
  16. 16.
    Wu, Y., Tian, Q., Huang, T.S.: Discriminant em algorithm with application to image retrieval. In: Proceedigns IEEE Conf. Computer Vision and Pattern Recognition, South Carolina (June 2000)Google Scholar
  17. 17.
    Zhang, R., Zhang, Z.: Addressing cbir efficiency, effectiveness, and retrieval subjectivity simultaneously. In: ACM Multimedia 2003 Multimedia Information Retrieval Workshop, Berkeley, CA (November 2003)Google Scholar
  18. 18.
    Zhang, R., Zhang, Z.: A robust color object analysis approach to efficient image retrieval. EURASIP Journal on Applied Signal Processing (2004)Google Scholar
  19. 19.
    Zhou, X.S., Huang, T.S.: Small sample learning during multimedia retrieval using biasmap. In: Proceedings IEEE Conf. Computer Vision and Pattern Recognition, Hawaii (December 2001)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2004

Authors and Affiliations

  • Ruofei Zhang
    • 1
  • Zhongfei (Mark) Zhang
    • 1
  1. 1.Department of Computer ScienceState University of New York at BinghamtonBinghamton, NYUSA

Personalised recommendations