OSA's Digital Library

Journal of the Optical Society of America A

Journal of the Optical Society of America A

| OPTICS, IMAGE SCIENCE, AND VISION

  • Editor: Franco Gori
  • Vol. 31, Iss. 5 — May. 1, 2014
  • pp: 1049–1058

Illuminant estimation for color constancy: why spatial-domain methods work and the role of the color distribution

Dongliang Cheng, Dilip K. Prasad, and Michael S. Brown  »View Author Affiliations


JOSA A, Vol. 31, Issue 5, pp. 1049-1058 (2014)
http://dx.doi.org/10.1364/JOSAA.31.001049


View Full Text Article

Enhanced HTML    Acrobat PDF (1667 KB)





Browse Journals / Lookup Meetings

Browse by Journal and Year


   


Lookup Conference Papers

Close Browse Journals / Lookup Meetings

Article Tools

Share
Citations

Abstract

Color constancy is a well-studied topic in color vision. Methods are generally categorized as (1) low-level statistical methods, (2) gamut-based methods, and (3) learning-based methods. In this work, we distinguish methods depending on whether they work directly from color values (i.e., color domain) or from values obtained from the image’s spatial information (e.g., image gradients/frequencies). We show that spatial information does not provide any additional information that cannot be obtained directly from the color distribution and that the indirect aim of spatial-domain methods is to obtain large color differences for estimating the illumination direction. This finding allows us to develop a simple and efficient illumination estimation method that chooses bright and dark pixels using a projection distance in the color distribution and then applies principal component analysis to estimate the illumination direction. Our method gives state-of-the-art results on existing public color constancy datasets as well as on our newly collected dataset (NUS dataset) containing 1736 images from eight different high-end consumer cameras.

© 2014 Optical Society of America

OCIS Codes
(040.1490) Detectors : Cameras
(150.2950) Machine vision : Illumination
(330.1720) Vision, color, and visual optics : Color vision
(330.1715) Vision, color, and visual optics : Color, rendering and metamerism

ToC Category:
Vision, Color, and Visual Optics

History
Original Manuscript: December 5, 2013
Revised Manuscript: March 18, 2014
Manuscript Accepted: March 20, 2014
Published: April 17, 2014

Virtual Issues
Vol. 9, Iss. 7 Virtual Journal for Biomedical Optics

Citation
Dongliang Cheng, Dilip K. Prasad, and Michael S. Brown, "Illuminant estimation for color constancy: why spatial-domain methods work and the role of the color distribution," J. Opt. Soc. Am. A 31, 1049-1058 (2014)
http://www.opticsinfobase.org/josaa/abstract.cfm?URI=josaa-31-5-1049


Sort:  Author  |  Year  |  Journal  |  Reset  

References

  1. S. M. Newhall, R. W. Burnham, and R. M. Evans, “Color constancy in shadows,” J. Opt. Soc. Am. 48, 976–984 (1958). [CrossRef]
  2. G. Buchsbaum, “A spatial processor model for object colour perception,” J. Franklin Inst. 310, 1–26 (1980). [CrossRef]
  3. K. T. Blackwell and G. Buchsbaum, “Quantitative studies of color constancy,” J. Opt. Soc. Am. A 5, 1772–1780 (1988). [CrossRef]
  4. J. S. Werner and B. E. Schefrin, “Loci of achromatic points throughout the life span,” J. Opt. Soc. Am. A 10, 1509–1516 (1993). [CrossRef]
  5. Q. Zaidi, B. Spehar, and J. DeBonet, “Color constancy in variegated scenes: role of low-level mechanisms in discounting illumination changes,” J. Opt. Soc. Am. A 14, 2608–2621 (1997). [CrossRef]
  6. K.-H. Bäuml, “Increments and decrements in color constancy,” J. Opt. Soc. Am. A 18, 2419–2429 (2001). [CrossRef]
  7. N. N. Krasilnikov, O. I. Krasilnikova, and Y. E. Shelepin, “Mathematical model of the color constancy of the human visual system,” J. Opt. Technol. 69, 327–332 (2002). [CrossRef]
  8. 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]
  9. A. Gijsenij, T. Gevers, and J. van de Weijer, “Computational color constancy: survey and experiments,” IEEE Trans. Image Process. 20, 2475–2489 (2011). [CrossRef]
  10. K. Barnard, V. Cardei, and B. Funt, “A comparison of computational color constancy algorithms. I: methodology and experiments with synthesized data,” IEEE Trans. Image Process. 11, 972–984 (2002). [CrossRef]
  11. K. Barnard, L. Martin, A. Coath, and B. Funt, “A comparison of computational color constancy algorithms. II: experiments with image data,” IEEE Trans. Image Process. 11, 985–996 (2002). [CrossRef]
  12. C. van Trigt, “Linear models in color constancy theory,” J. Opt. Soc. Am. A 24, 2684–2691 (2007). [CrossRef]
  13. S. D. Hordley and G. D. Finlayson, “Reevaluation of color constancy algorithm performance,” J. Opt. Soc. Am. A 23, 1008–1020 (2006). [CrossRef]
  14. M. D’Zmura and G. Iverson, “Color constancy. I. Basic theory of two-stage linear recovery of spectral descriptions for lights and surfaces,” J. Opt. Soc. Am. A 10, 2148–2165 (1993). [CrossRef]
  15. M. D’Zmura and G. Iverson, “Color constancy. II. Results for two-stage linear recovery of spectral descriptions for lights and surfaces,” J. Opt. Soc. Am. A 10, 2166–2180 (1993). [CrossRef]
  16. M. D’Zmura and G. Iverson, “Color constancy. III. General linear recovery of spectral descriptions for lights and surfaces,” J. Opt. Soc. Am. A 11, 2389–2400 (1994). [CrossRef]
  17. B. Funt and H. Jiang, “Nondiagonal color correction,” in International Conference on Image Processing (IEEE, 2003), pp. 481–484.
  18. G. Iverson and M. D’Zmura, “Criteria for color constancy in trichromatic bilinear models,” J. Opt. Soc. Am. A 11, 1970–1975 (1994). [CrossRef]
  19. D. H. Brainard and B. A. Wandell, “Analysis of the retinex theory of color vision,” J. Opt. Soc. Am. A 3, 1651–1661 (1986). [CrossRef]
  20. G. D. Finlayson and E. Trezzi, “Shades of gray and colour constancy,” in Color and Imaging Conference (IS&T, 2004), pp. 37–41.
  21. J. Van De Weijer, T. Gevers, and A. Gijsenij, “Edge-based color constancy,” IEEE Trans. Image Process. 16, 2207–2214 (2007). [CrossRef]
  22. L. Shi and B. Funt, “Maxrgb reconsidered,” J. Imaging Sci. Technol. 56, 1 (2012). [CrossRef]
  23. M. P. Lucassen, T. Gevers, A. Gijsenij, and N. Dekker, “Effects of chromatic image statistics on illumination induced color differences,” J. Opt. Soc. Am. A 30, 1871–1884 (2013). [CrossRef]
  24. M. S. Drew and B. V. Funt, “Variational approach to interreflection in color images,” J. Opt. Soc. Am. A 9, 1255–1265 (1992). [CrossRef]
  25. 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]
  26. T. Celik and T. Tjahjadi, “Adaptive colour constancy algorithm using discrete wavelet transform,” Comput. Vis. Image Underst. 116, 561–571 (2012). [CrossRef]
  27. A. Chakrabarti, K. Hirakawa, and T. Zickler, “Color constancy with spatio-spectral statistics,” IEEE Trans. Pattern Anal. Mach. Intell. 34, 1509–1519 (2012). [CrossRef]
  28. A. Gijsenij and T. Gevers, “Color constancy using natural image statistics and scene semantics,” IEEE Trans. Pattern Anal. Mach. Intell. 33, 687–698 (2011). [CrossRef]
  29. A. Gijsenij, T. Gevers, and J. Van De Weijer, “Improving color constancy by photometric edge weighting,” IEEE Trans. Pattern Anal. Mach. Intell. 34, 918–929 (2012). [CrossRef]
  30. H.-C. Lee, “Method for computing the scene-illuminant chromaticity from specular highlights,” J. Opt. Soc. Am. A 3, 1694–1699 (1986). [CrossRef]
  31. H. R. V. Joze, M. S. Drew, G. D. Finlayson, and P. A. T. Rey, “The role of bright pixels in illumination estimation,” in Color and Imaging Conference (IS&T, 2012), pp. 41–46.
  32. M. S. Drew, H. R. V. Joze, and G. D. Finlayson, “Specularity, the zeta-image, and information-theoretic illuminant estimation,” in Computer Vision–ECCV 2012. Workshops and Demonstrations (Springer, 2012), pp. 411–420.
  33. R. T. Tan, K. Nishino, and K. Ikeuchi, “Color constancy through inverse-intensity chromaticity space,” J. Opt. Soc. Am. A 21, 321–334 (2004). [CrossRef]
  34. F.-J. Chang, S.-C. Pei, and W.-L. Chao, “Color constancy by chromaticity neutralization,” J. Opt. Soc. Am. A 29, 2217–2225 (2012). [CrossRef]
  35. R. Kawakami, J. Takamatsu, and K. Ikeuchi, “Color constancy from blackbody illumination,” J. Opt. Soc. Am. A 24, 1886–1893 (2007). [CrossRef]
  36. K. Barnard, “Improvements to gamut mapping colour constancy algorithms,” in European Conference on Computer Vision (Springer, 2000), pp. 390–403.
  37. D. A. Forsyth, “A novel algorithm for color constancy,” Int. J. Comput. Vis. 5, 5–35 (1990). [CrossRef]
  38. V. C. Cardei, B. Funt, and K. Barnard, “Estimating the scene illumination chromaticity by using a neural network,” J. Opt. Soc. Am. A 19, 2374–2386 (2002). [CrossRef]
  39. 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]
  40. P. V. Gehler, C. Rother, A. Blake, T. Minka, and T. Sharp, “Bayesian color constancy revisited,” in IEEE Conference on Computer Vision and Pattern Recognition (IEEE, 2008), pp. 1–8.
  41. D. H. Brainard and W. T. Freeman, “Bayesian color constancy,” J. Opt. Soc. Am. A 14, 1393–1411 (1997). [CrossRef]
  42. L. Shi, W. Xiong, and B. Funt, “Illumination estimation via thin-plate spline interpolation,” J. Opt. Soc. Am. A 28, 940–948 (2011). [CrossRef]
  43. Y. Weiss and W. T. Freeman, “What makes a good model of natural images?” in IEEE Conference on Computer Vision and Pattern Recognition (IEEE, 2007), pp. 1–8.
  44. K. Barnard, L. Martin, B. Funt, and A. Coath, “A data set for color research,” Color Res. Appl. 27, 147–151 (2002). [CrossRef]
  45. A. Gijsenij, T. Gevers, and M. P. Lucassen, “Perceptual analysis of distance measures for color constancy algorithms,” J. Opt. Soc. Am. A 26, 2243–2256 (2009). [CrossRef]
  46. 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.
  47. C. Fredembach and G. D. Finlayson, “The bright-chromagenic algorithm for illuminant estimation,” J. Imaging Sci. Technol. 52, 040906 (2008). [CrossRef]

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