OSA's Digital Library

Journal of the Optical Society of America A

Journal of the Optical Society of America A


  • Editor: Stephen A. Burns
  • Vol. 26, Iss. 10 — Oct. 1, 2009
  • pp: 2243–2256

Perceptual analysis of distance measures for color constancy algorithms

Arjan Gijsenij, Theo Gevers, and Marcel P. Lucassen  »View Author Affiliations

JOSA A, Vol. 26, Issue 10, pp. 2243-2256 (2009)

View Full Text Article

Enhanced HTML    Acrobat PDF (881 KB)

Browse Journals / Lookup Meetings

Browse by Journal and Year


Lookup Conference Papers

Close Browse Journals / Lookup Meetings

Article Tools



Color constancy algorithms are often evaluated by using a distance measure that is based on mathematical principles, such as the angular error. However, it is unknown whether these distance measures correlate to human vision. Therefore, the main goal of our paper is to analyze the correlation between several performance measures and the quality, obtained by using psychophysical experiments, of the output images generated by various color constancy algorithms. Subsequent issues that are addressed are the distribution of performance measures, suggesting additional and alternative information that can be provided to summarize the performance over a large set of images, and the perceptual significance of obtained improvements, i.e., the improvement that should be obtained before the difference becomes noticeable to a human observer.

© 2009 Optical Society of America

OCIS Codes
(150.0150) Machine vision : Machine vision
(330.1690) Vision, color, and visual optics : Color
(330.5510) Vision, color, and visual optics : Psychophysics

ToC Category:
Machine Vision

Original Manuscript: March 20, 2009
Revised Manuscript: July 31, 2009
Manuscript Accepted: August 20, 2009
Published: September 25, 2009

Virtual Issues
Vol. 4, Iss. 12 Virtual Journal for Biomedical Optics

Arjan Gijsenij, Theo Gevers, and Marcel P. Lucassen, "Perceptual analysis of distance measures for color constancy algorithms," J. Opt. Soc. Am. A 26, 2243-2256 (2009)

Sort:  Author  |  Year  |  Journal  |  Reset  


  1. E. H. Land, “The retinex theory of color vision,” Sci. Am. 237, 108-128 (1977). [CrossRef] [PubMed]
  2. G. Buchsbaum, “A spatial processor model for object colour perception,” J. Franklin Inst. 310, 1-26 (1980). [CrossRef]
  3. G. D. Finlayson and E. Trezzi, “Shades of gray and colour constancy,” in Twelfth Color Imaging Conference: Color Science and Engineering Systems, Technologies, and Applications (Society for Imaging Science and Technology, 2004), pp. 37-41.
  4. J. van de Weijer, T. Gevers, and A. Gijsenij, “Edge-based color constancy,” IEEE Trans. Image Process. 16, 2207-2214 (2007). [CrossRef] [PubMed]
  5. F. Ciurea and B. V. Funt, “A large image database for color constancy research,” in Eleventh Color Imaging Conference: Color Science and Engineering Systems, Technologies, and Applications (Society for Imaging Science and Technology, 2003), pp. 160-164.
  6. K. Barnard, L. Martin, B. V. Funt, and A. Coath, “A data set for color research,” Color Res. Appl. 27, 147-151 (2002). [CrossRef]
  7. S. D. Hordley and G. D. Finlayson, “Reevaluation of color constancy algorithm performance,” J. Opt. Soc. Am. A 23, 1008-1020 (2006). [CrossRef]
  8. D. A. Forsyth, “A novel algorithm for color constancy,” Int. J. Comput. Vis. 5, 5-36 (1990). [CrossRef]
  9. G. D. Finlayson, S. D. Hordley, and P. M. Hubel, “Color by correlation: a simple, unifying framework for color constancy,” IEEE Trans. Pattern Anal. Mach. Intell. 23, 1209-1221 (2001). [CrossRef]
  10. G. D. Finlayson, S. D. Hordley, and I. Tastl, “Gamut constrained illuminant estimation,” Int. J. Comput. Vis. 67, 93-109 (2006). [CrossRef]
  11. D. H. Brainard and W. T. Freeman, “Bayesian color constancy,” J. Opt. Soc. Am. A 14, 1393-1411 (1997). [CrossRef]
  12. M. D'Zmura, G. Iverson, and B. Singer, “Probabilistic color constancy,” in Geometric Representations of Perceptual Phenomena (Lawrence Erlbaum, 1995), pp. 187-202.
  13. P. V. Gehler, C. Rother, A. Blake, T. P. Minka, and T. Sharp, “Bayesian color constancy revisited,” in IEEE Conference on Computer Vision and Pattern Recognition (IEEE, 2008), pp. 1-8. [CrossRef]
  14. M. Ebner, “Evolving color constancy,” Pattern Recogn. Lett. 27, 1220-1229 (2006). [CrossRef]
  15. A. Gijsenij, T. Gevers, and J. van de Weijer, “Generalized gamut mapping using image derivative structures for color constancy,” Int. J. Comput. Vis. (to be published), http://www.springerlink.com/content/q598825t7654648n/?p=155b2db7234942feaacfcf6d88a50b2c&pi=0. (September 2009).
  16. Color constancy demonstration (Mathematica), http://cat.cvc.uab.es/~joost/code/ColorConstancy.zip.
  17. J. von Kries, “Die gesichtsempfindungen,” in Handbuch der Physiologie des Menschen (1904), Vol. 3, pp. 109-282.
  18. G. West and M. H. Brill, “Necessary and sufficient conditions for von Kries chromatic adaptation to give color constancy,” J. Math. Biol. 15, 249-258 (1982). [CrossRef] [PubMed]
  19. G. D. Finlayson, M. S. Drew, and B. V. Funt, “Color constancy: generalized diagonal transforms suffice,” J. Opt. Soc. Am. A 11, 3011-3019 (1994). [CrossRef]
  20. B. V. Funt and B. C. Lewis, “Diagonal versus affine transformations for color correction,” J. Opt. Soc. Am. A 17, 2108-2112 (2000). [CrossRef]
  21. Commission Internationale de L'Eclairage (CIE), “Colorimetry,” CIE Publ. no. 15.2, 2nd ed. (CIE, 1986).
  22. Commission Internationale de L'Eclairage (CIE), “Improvement to industrial colour-difference evaluation,” CIE Publ. no. 142-2001 (CIE, 2001).
  23. M. Stokes, M. Anderson, S. Chandrasekar, and R. Motta, “A standard default color space for the Internet--sRGB,” version 1.10 (1996) www.w3.org/Graphics/Color/sRGB.html.
  24. G. Wyszecki and W. S. Stiles, Color Science: Concepts and Methods, Quantitative Data and Formulae (Wiley, 2000).
  25. J. Slater, Modern Television Systems to HDTV and Beyond (Taylor & Francis, 2004).
  26. L. E. Arend, A. Reeves, J. Schirillo, and R. Goldstein, “Simultaneous color constancy: papers with diverse Munsell values,” J. Opt. Soc. Am. A 8, 661-672 (1991). [CrossRef] [PubMed]
  27. E. Brunswik, “Zur Entwicklung der Albedowahrnehmung,” Z. Psychol. 109, 40-115 (1928).
  28. P. B. Delahunt and D. H. Brainard, “Does human color constancy incorporate the statistical regularity of natural daylight?” J. Vision 4, 57-81 (2004). [CrossRef]
  29. D. H. Foster, S. M. C. Nascimento, and K. Amano, “Information limits on neural identification of colored surfaces in natural scenes,” Visual Neurosci. 21, 331-336 (2004). [CrossRef]
  30. J. E. Bailey, M. Neitz, D. Tait, and J. Neitz, “Evaluation of an updated hrr color vision test,” Visual Neurosci. 22, 431-436 (2004). [CrossRef]
  31. H. A. David, “Ranking from unbalanced paired-comparison data,” Biometrika 74, 432-436 (1987). [CrossRef]
  32. R. L. Alfvin and M. D. Fairchild, “Observer variability in metameric color matches using color reproduction media,” Color Res. Appl. 22, 174-188 (1997). [CrossRef]
  33. E. Kirchner, G. J. van den Kieboom, L. Njo, R. Supèr, and R. Gottenbos, “Observation of visual texture of metallic and pearlescent materials,” Color Res. Appl. 32, 256-266 (2007). [CrossRef]
  34. Bruce Lindbloom's web site, http://www.brucelindbloom.com.
  35. R. V. Hogg and E. A. Tanis, Probability and Statistical Inference (Prentice Hall, 2001).
  36. J. W. Tukey, Exploratory Data Analysis (Addison-Wesley, 1977).
  37. H. F. Weisberg, Central Tendency and Variability (Sage Publications, 1992).
  38. A. Gijsenij and T. Gevers, “Color constancy using natural image statistics,” in IEEE Conference on Computer Vision and Pattern Recognition (IEEE, 2007), pp. 1-8. [CrossRef]
  39. J. van de Weijer, C. Schmid, and J. J. Verbeek, “Using high-level visual information for color constancy,” in IEEE International Conference on Computer Vision (IEEE, 2007), pp. 1-8. [CrossRef]
  40. S. Bianco, F. Gasparini, and R. Schettini, “Consensus-based framework for illuminant chromaticity estimation,” J. Electron. Imaging 17, 023013 (2008). [CrossRef]
  41. S. Bianco, G. Ciocca, C. Cusano, and R. Schettini, “Improving color constancy using indoor-outdoor image classification,” IEEE Trans. Image Process. 17, 2381-2392 (2008). [CrossRef] [PubMed]
  42. A. Chakrabarti, K. Hirakawa, and T. Zickler, “Color constancy beyond bags of pixels,” in IEEE Conference on Computer Vision and Pattern Recognition (IEEE, 2008), pp. 1-8. [CrossRef]
  43. B. V. Funt, K. Barnard, and L. Martin, “Is machine colour constancy good enough?” in Computer Vision--ECCV'98: 5th European Conference on Computer Vision (Springer, 1998), pp. 445-459.
  44. G. D. Finlayson, S. D. Hordley, and P. Morovic, “Colour constancy using the chromagenic constraint,” in IEEE Conference on Computer Vision and Pattern Recognition (IEEE, 2005), pp. 1079-1086.
  45. C. Fredembach and G. D. Finlayson, “The bright-chromagenic algorithm for illuminant estimation,” J. Imaging Sci. Technol. 52, 040906 (2008). [CrossRef]
  46. S. D. Hordley, “Scene illuminant estimation: past, present, and future,” Color Res. Appl. 31, 303-314 (2006). [CrossRef]
  47. E. H. Weber, “Der Tastinn und das Gemeingfühl,” in Handwörterbüch der Physiologie (1846), Vol. 3, pp. 481-588.
  48. T. N. Cornsweet, Visual Perception (Academic, 1970).

Cited By

Alert me when this paper is cited

OSA is able to provide readers links to articles that cite this paper by participating in CrossRef's Cited-By Linking service. CrossRef includes content from more than 3000 publishers and societies. In addition to listing OSA journal articles that cite this paper, citing articles from other participating publishers will also be listed.

« Previous Article  |  Next Article »

OSA is a member of CrossRef.

CrossCheck Deposited