Multimedia Tools and Applications

, Volume 13, Issue 1, pp 73–91 | Cite as

Color Space Quantization for Color-Content-Based Query Systems

  • Jia Wang
  • Wen-jann Yang
  • Raj Acharya


Color histograms are widely used in most of color content-based image retrieval systems to represent color content. However, the high dimensionality of a color histogram hinders efficient indexing and matching. To reduce histogram dimension with the least loss in color content, color space quantization is indispensable. This paper highlights and emphasizes the importance and the objectives of color space quantization. The color conservation property is examined by investigating and comparing different clustering techniques in perceptually uniform color spaces and for different images. For studying color spaces, perceptually uniform spaces, such as the Mathematical Transformation to Munsell system (MTM) and the C.I.E. L*a*b*, are investigated. For evaluating quantization approaches, the uniform quantization, the hierarchical clustering, and the Color-Naming-System (CNS) supervised clustering are studied. For analyzing color loss, the error bound, the quantized error in color space conversion, and the average quantized error of 400 color images are explored. A color-content-based image retrieval application is shown to demonstrate the differences when applying these clustering techniques. Our simulation results suggest that good quantization techniques lead to more effective retrieval.

color-content-based image retrieval color histogram color space quantization feature extraction matching clustering techniques color loss 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    R. Duda and P. Hart, Pattern Classification and Scene Analysis. Wiley: New York, 1973, p. 235.Google Scholar
  2. 2.
    C. Faloutsos, W. Equitz, M. Flickner, W. Niblack, D. Petrovic, and R. Barber, “Efficient and effective querying by image content,” Journal of Intelligent Information Systems, Vol. 3, No. 3, pp. 231–262, 1994.Google Scholar
  3. 3.
    J.D. Foley, A. van Dam, S.K. Feiner, and J.F. Hughes, Computer Graphics: Principles and Practice. 2nd edn., Addison-Wesley: Reading, MA, 1990.Google Scholar
  4. 4.
    R.C. Gonzalez and R.E. Woods, Digital Image Processing. Addison-Welsley Publishing Company, 1992, p. 225.Google Scholar
  5. 5.
    W. Hsu, T. Chua, and H. Pung, “An integrated color-spatial approach to content-based image retrieval,” in Proceedings of the 1995 ACM Multimedia Conference, San Francisco, CA, Nov. 1995, pp. 305–313.Google Scholar
  6. 6.
    A.K. Jain, Fundamental of Digital Image Processing. Prentice Hall: NJ, 1989.Google Scholar
  7. 7.
    K.L. Kelley and D.B. Judd, Color Universal Language and Dictionary of Names. Natural Bureau of Standards (U.S.), Spec. Publ. 440, Dec. 1976.Google Scholar
  8. 8.
    M. Miyahara and Y. Yoshida, “Mathematical transform of (R, G, B) color data to Munsell (H, V, C) color data,” SPIE Visual Communications and Image Processing '88, Vol. 1001, pp. 650–657.Google Scholar
  9. 9.
    H. Sawhney, W. Niblack, and M. Flickner, “Query by image and video content: The QBIC system,” Computer, Vol. 28, No. 9, pp. 23–32, Sept. 1995.Google Scholar
  10. 10.
    R.J. Schalkoff, Pattern Recognition: Statistical, Structural and Neural Approaches. JohnWiley & Sons, 1992, p. 120.Google Scholar
  11. 11.
    J.R. Smith and S.F. Chang, “Single color extraction and image query,” in IEEE International Conference on Image Processing (ICIP-95), Washington, DC, Oct. 1995.Google Scholar
  12. 12.
    M.J. Swain and D.H. Ballard, “Color indexing,” International J. of Computer Vision, Vol. 7, No. 1, pp. 11–32, 1991.Google Scholar
  13. 13.
    K. Tan, T. Chua, and B. Ooi, “Fast signature-based color-spatial image retrieval,” in IEEE International Conference on Multimedia Computing and Systems, Ottawa, Ontario, Canada, June 3–6, 1997, pp. 362–369.Google Scholar
  14. 14.
    S. Tominaga, “A computer method for specifying colors by means of color naming,” in Cognitive Engineering in the Design of Human-Computer Interaction and Expert Systems, G. Salvendy (Ed.), Elsevier Science Publishers, 1987, pp. 131–138.Google Scholar
  15. 15.
    S. Tominaga, “Color classification of natural color images,” COLOR Research and Application, Vol. 17, No. 4, pp. 230–239, Aug. 1992.Google Scholar

Copyright information

© Kluwer Academic Publishers 2001

Authors and Affiliations

  • Jia Wang
    • 1
  • Wen-jann Yang
    • 2
  • Raj Acharya
    • 1
  1. 1.Department of Electrical and Computer EngineeringState University of New York at BuffaloBuffaloUSA
  2. 2.Center of Excellence for Document Analysis and RecognitionState University of New York at BuffaloBuffaloUSA

Personalised recommendations