Upgrading Color Distributions for Image Retrieval Can We Do Better?

  • Constantin Vertan
  • Nozha Boujemaa
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 1929)


Content-based image retrieval primarily used color distributions as descriptors of the image content; researches have since focused on the use of various color representation spaces, color and illumination invariance, color quantization and color matching. In order to overcome the many limitations of the description by a firstorder distribution, several higherorder distributions have been introduced since (like autocorrelogram or color coherence vectors). Although they can perform better, their computational complexity is prohibitive and they require parameter setting. We propose to upgrade the first order color distribution (color histogram) by embedding for each color additional information about its perceptual or statistical relevance. Such information is obtained by using local activity measures such as the Laplacian, the entropy and others. We prove that the new color distribution family is compact, robust and easy to compute and provides a superior retrieval performance, independent with respect to the color representation.


Image Retrieval Color Coherence Vector Color Representation Space Superior Retrieval Performance Local Activity Measure 
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.
    M. J. Swain and D. H. Ballard. Color indexing. International Journal of Computer Vision, 7(l):ll–32, 1991.Google Scholar
  2. 2.
    B. M. Mehtre, M. S. Kankanhalli, A. D. Narasimhalu, and G. C. Man. Color matching for image retrieval. Pattern Recognition Letters, 16:325–331, Mar. 1995.Google Scholar
  3. 3.
    G. Pass and R. Zabih. Histogram refinement for content based image retrieval. In IEEE Workshop on Applications of Computer Vision, pages 96–102, 1996.Google Scholar
  4. 4.
    J. Huang, S. R. Kumar, M. Mitra, Zhu W.-J., and R. Zabih. Image indexing using correlograms. In Computer Vision and Pattern Recognition CVPR’ 97, San Juan, Puerto Rico, 17–19Jun. 1997.Google Scholar
  5. 5.
    J. Huang, S. R. Kumar, M. Mitra, and Zhu W.-J. Spatial color indexing and applications. In IEEE International Conference on Computer Vision ICCV’ 98, Bombay, India, 4–7 Jan. 1998.Google Scholar
  6. 6.
    C. Vertan and N. Boujemaa. Color texture classification by normalized color space representation. In Proc. of ICPR’2000, Barcelona, Spain, 3–8 Sept. 2000.Google Scholar
  7. 7.
    A. del Bimbo. Visual Information Retrieval. Morgan Kaufmann, San Francisco, CA, 1999.Google Scholar
  8. 8.
    A.K. Jain. Fundamentals of Digital Image Processing. Prentice Hall Intl., Englewood Cliffs NJ, 1989.Google Scholar
  9. 9.
    F. M. Wahl. Digital Image Signal Processing. Artech House, Boston, 1987.Google Scholar
  10. 10.
    L. Zadeh. Fuzzy sets. Information and Control, 8:338–353, 1965.zbMATHCrossRefMathSciNetGoogle Scholar
  11. 11.
    J. C. Bezdek. Fuzzy models-what are they and why ?IEEE Trans, on Fuzzy Systems, l(l):l–5, Feb. 1993.Google Scholar
  12. 12.
    I. Bloch and H. Maitre. Fuzzy mathematical morphologies: a comparative study. Pattern Recognition, 28(9):1341–1387, Sept. 1995.CrossRefMathSciNetGoogle Scholar
  13. 13.
    C. V. Jawahar and A. K. Ray. Fuzzy statistics of digital images. IEEE Signal Processing Letters, 3(8):225–227, Aug. 1996.CrossRefGoogle Scholar
  14. 14.
    C. Vertan and V. Buzuloiu. Fuzzy nonlinear filtering of color images: A survey. In E. Kerre and M. Nachtegael, editors, Fuzzy Techniques in Image Processing, Heidelberg, Germany, 2000. Physica Verlag.Google Scholar
  15. 15.
    C. Vertan and N. Boujemaa. Using fuzzy histograms and distances for color image retrieval. In Proc. of CIR’2000, Brighton, United Kingdom, 4–5 May 2000.Google Scholar
  16. 16.
    C. Vertan and N. Boujemaa. Embedding fuzzy logic in content based image retrieval. In Proc. of NAFIPS’2000, pages 85–90, Atlanta, Georgia, 13–15 Jul. 2000.Google Scholar
  17. 17.
    C. Nastar, M. Mitschke, C. Meihac, and N. Boujemaa. Surfimage: a flexible content-based image retrieval system. In ACM Multimedia’ 98, Bristol, United Kingdom, 12–16 Sept. 1998.Google Scholar
  18. 18.
    B. V. Funt and G. D. Finlayson. Color constant color indexing. IEEE Trans, on Pattern Analysis and Machine Intelligence, 17(5):522–529, May 1995.CrossRefGoogle Scholar
  19. 19.
    T. Gevers and A. W. M. Smeulders. A comparative study of several color models for color image invariant retrieval. In First Inetrnational Workshop on Image Databases and Multimedia Search, pages 17–26, Amsterdam, Holland, 1996.Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2000

Authors and Affiliations

  • Constantin Vertan
    • 1
  • Nozha Boujemaa
    • 1
  1. 1.INRIA Rocquencourt, IMEDIA ProjectLe Chesnay CedexFrance

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