Adaptive Multi-Class Metric Content-Based Image Retrieval

  • Jing Peng
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 1929)


Relevance feedback methods for content-based image retrieval have shown promise in a variety of image database applications. These techniques assume two (relevant and irrelevant) class relevance feedback. While simple computationally, two class relevance feedback often becomes inadequate in providing sufficient information to help rapidly improve retrieval performance. We propose a locally adaptive technique for content-based image retrieval that enables relevance feedback to take on multi-class form. We estimate a exible multi-class metric for computing retrievals based on Chi-squared distance analysis. As a result, local data distributions can be sufficiently exploited, whereby rapid performance improvement can be achieved. The efficacy of our method is validated and compared against other competing techniques using a number of real world data sets.


Image Retrieval Relevance Feedback Retrieval Performance Classi Cation Class Posterior Probability 
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.
    R.H. Creecy, B.M. Masand, S.J. Smith, and D.L. Waltz, “Trading Mips and Memory for Knowledge Engineering,” CACM, 35:48–64, 1992.Google Scholar
  2. 2.
    R.O. Duda and P.E. Hart, Pattern Classification and Scene Analysis. John Wiley & Sons, Inc., 1973.Google Scholar
  3. 3.
    J.H. Friedman “Flexible Metric Nearest Neighbor Classification,” Tech. Report, Dept. of Statistics, Stanford University, 1994.Google Scholar
  4. 4.
    T. Hastie and R. Tibshirani, “Discriminant Adaptive Nearest Neighbor Classification,” IEEE Trans. on Pattern Analysis and Machine Intelligence, Vol. 18, No. 6, pp. 607–615, 1996.CrossRefGoogle Scholar
  5. 5.
    Y. Ishikawa, R. Subramanya, and C. Faloutsos, “MindReader: Query databases through multiple examples,” Proceedings of the 24th VLDB Conference, New York, 1998.Google Scholar
  6. 6.
    T. Mitchell, Machine Learning. McGraw-Hill, New York, 1997.zbMATHGoogle Scholar
  7. 7.
    P.M. Murphy and D.W. Aha, UCI repository of machine learning databases., 1995.
  8. 8.
    J.P. Myles and D.J. Hand, “The Multi-Class Metric Problem in Nearest Neighbor Discrimination Rules,” Pattern Recognition, Vol. 23, pp. 1291–1297, 1990.CrossRefGoogle Scholar
  9. 9.
    J. Peng, B. Bhanu and S. Qing, “Probabilistic Feature Relevance Learning for Content-Based Image Retrieval”, Computer Vision and Image Understanding, Vol. 75, No 1/2, pp. 150–164, 1999.CrossRefGoogle Scholar
  10. 10.
    J. Peng and B. Bhanu, “Feature Relevance Estimation for Image Databases,” Proc. of the 5th Int. Workshop on Multimedia Information Systems, pp. 12–19, 1999.Google Scholar
  11. 11.
    Y. Rui, T.S. Huang and S. Mehrotra, “Content-based image retrieval with relevance feedback in MARS”, Proceedings of IEEE International Conference on Image Processing, pp. 815–818, Santa Barbara, California, October, 1997.Google Scholar
  12. 12.
    N. Vasconcelos and A. Lippman, “A Probabilistic Architecture for Content-Based Image Retrieval”, Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 216–221, Hilton Head Island, South Carolina, 2000.Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2000

Authors and Affiliations

  • Jing Peng
    • 1
  1. 1.Computer Science DepartmentOklahoma State UniversityStillwaterUSA

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