Neural Computing and Applications

, Volume 18, Issue 3, pp 237–247 | Cite as

A novel approach to color normalization using neural network

  • H. D. ChengEmail author
  • Xiaopeng Cai
  • Rui Min
Original Article


Color is a powerful descriptor that often simplifies object extraction and identification, and many computer vision systems use color to aid object recognition. However, image colors strongly depend on lighting geometry (direction and intensity of light source) and illuminant color (spectral power distribution). Either small variation in the intensity or the change of scene illumination can dramatically make object color changed. To overcome the lighting dependency problem, a color constancy or normalization algorithm should be used for preprocessing. This paper presents a novel approach to performing color normalization. A nonlinear mapping function is estimated using a neural network. Once the mapping function is found accurately, an image under unknown illumination may be transformed to the image under the predetermined illumination, which will be useful for color image processing. Three groups of experiments were conducted. In our experiments, images are processed by various neural networks and the performance is boosted by using a committee machine, and then the mapping errors are estimated and the results are compared with those of other algorithms. The experimental results demonstrate that the performance of the proposed method is superior to that of other color normalization algorithms.


Color normalization Color constancy Neural networks Committee machine Genetic algorithm 


  1. 1.
    Nassau K (1998) Color for science, art and technology. Elsevier, AmsterdamGoogle Scholar
  2. 2.
    MacDonald LW, Luo MR (2002) Colour image science: exploiting digital media. Wiley, ChichesterGoogle Scholar
  3. 3.
    Finlayson GD, Tian GY (1999) Color normalization for color object recognition. Int J Pattern Recognit Artif Intell 13:1271–1285Google Scholar
  4. 4.
    Jameson D, Hurvich LM (1989) Essay concerning color constancy. Annu Rev Psychol 40:1–22Google Scholar
  5. 5.
    Barnard K, Cardei V, Funt B (2002) A comparison of computational color constancy algorithms - Part I: Methodology and experiments with synthesized data. IEEE Trans Image Process 11:972Google Scholar
  6. 6.
    Barnard K, Martin L, Coath A, Funt B (2002) A comparison of computational color constancy algorithms. Part II: Experiments with image data. IEEE Trans Image Process 11:985Google Scholar
  7. 7.
    Maloney LT, Wandell BA (1986) Color constancy: a method for recovering surface spectral reflectance. J Opt Soc Am A Opt Image Sci Vis 3:29–33Google Scholar
  8. 8.
    Dzmura M (1992) Color constancy: surface color from changing illumination. J Opt Soc Am A Opt Image Sci Vis 9:490–493CrossRefGoogle Scholar
  9. 9.
    Tominaga S, Wandell BA (1990) Component estimation of surface spectral reflectance. J Opt Soc Am A Opt Image Sci Vis 7:312–317Google Scholar
  10. 10.
    Craven BJ, Foster DH (1992) An operational approach to color constancy. Vis Res 32:1359–1366Google Scholar
  11. 11.
    Buchsbaum G (1980) A spatial processor model for object color perception. J Frank Inst 310:1–26Google Scholar
  12. 12.
    Land EH (1977) The retinex theory of color vision. Sci Am 237:108–129CrossRefGoogle Scholar
  13. 13.
    Forsyth DA (1990) A novel algorithm for color constancy. Int J Comput Vis 5:5–36Google Scholar
  14. 14.
    Barnard K (2000) Improvements to gamut mapping colour constancy algorithms. Comput Vis Eccv 2000 Pt I Proc 1842:390–403Google Scholar
  15. 15.
    Tominaga S, Ebisui S, Wandell BA (2001) Scene illuminant classification: brighter is better. J Opt Soc Am A Opt Image Sci Vis 18:55–64Google Scholar
  16. 16.
    Tominaga S, Wandell BA (2002) Natural scene-illuminant estimation using the sensor correlation. Proc IEEE 90:42–56Google Scholar
  17. 17.
    Brainard DH, Freeman WT (1997) Bayesian color constancy. J Opt Soc Am A Opt Image Sci Vis 14:1393–1411Google Scholar
  18. 18.
    Barnard K, Martin L, Funt B (2000) Color by correlation in a three dimensional color space. Presented at 6th European Conference on Computer Vision, Dublin, IrelandGoogle Scholar
  19. 19.
    Finlayson GD, Hordley SD, Hubel PM (2001) Color by correlation: a simple, unifying framework for color constancy. IEEE Trans Pattern Anal Mach Intell 23:1209Google Scholar
  20. 20.
    Rosenberg C, Hebert M, Thrun S (2001) Color constancy using KL-divergence. In: Proceedings of the IEEE international conference on computer vision. Vancouver, CanadaGoogle Scholar
  21. 21.
    Geusebroek JM, Van den Boomgaard R, Smeulders AWM, Geerts H (2001) Color invariance. IEEE Trans Pattern Anal Mach Intell 23:1338Google Scholar
  22. 22.
    Sonka M, Hlavac V, Boyle R (1999) Image processing, analysis, and machine vision, 2nd edn. PWS, Pacific GroveGoogle Scholar
  23. 23.
    Cheng HD, Jiang XH, Sun Y, Wang JL (2001) Color image segmentation: advances and prospects. Pattern Recognit 34:2259–2281zbMATHGoogle Scholar
  24. 24.
    Finlayson GD, Schiele B, Crowley JL (1998) Comprehensive color image normalization. In: Proceedings of the 5th European conference on computer vision 1, Freiburg, GermanyGoogle Scholar
  25. 25.
    Lin ZY, Wang JX, Ma KK (2002) Using eigencolor normalization for illumination-invariant color object recognition. Pattern Recognit 35:2629–2642zbMATHGoogle Scholar
  26. 26.
    Courtney SM, Finkel LH, Buchsbaum G (1995) Network simulations of retinal and cortical contributions to color constancy. Vis Res 35:413–434Google Scholar
  27. 27.
    Funt B, Cardei VC, Barnard K (1997) Neural network color constancy and specularly reflecting surfaces. In: Processing of AIC color 97 II, Kyoto, JapanGoogle Scholar
  28. 28.
    Cardei VC, Funt B, Barnard K (2002) Estimating the scene illumination chromaticity by using a neural network. J Opt Soc Am A Opt Image Sci Vis 19:2374–2386Google Scholar
  29. 29.
    Agarwal V, Gribok AV, Abidi MA (2007) Machine learning approach to color constancy. Neural Netw 20:559–563zbMATHGoogle Scholar
  30. 30.
    Haykin SS (1999) Neural networks: a comprehensive foundation. 2nd edn.: Institute of Electrical and Electronics Engineers, Prentice-Hall, Upper Saddle River zbMATHGoogle Scholar
  31. 31.
    Funt B, Barnard K, Martin L (1998) Is machine colour constancy good enough? Lect Notes Comput Sci 1406:445Google Scholar
  32. 32.
    Swain MJ, Ballard DH (1991) Color indexing. Int J Comput Vis 7:11–32Google Scholar

Copyright information

© Springer-Verlag London Limited 2008

Authors and Affiliations

  1. 1.The Department of Computer ScienceUtah State UniversityLoganUSA

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