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

Full Article  |  PDF Article

References

  • View by:
  • |
  • |
  • |

  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]

2013

2012

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]

T. Celik and T. Tjahjadi, “Adaptive colour constancy algorithm using discrete wavelet transform,” Comput. Vis. Image Underst. 116, 561–571 (2012).
[CrossRef]

A. Chakrabarti, K. Hirakawa, and T. Zickler, “Color constancy with spatio-spectral statistics,” IEEE Trans. Pattern Anal. Mach. Intell. 34, 1509–1519 (2012).
[CrossRef]

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]

L. Shi and B. Funt, “Maxrgb reconsidered,” J. Imaging Sci. Technol. 56, 1 (2012).
[CrossRef]

2011

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]

A. Gijsenij, T. Gevers, and J. van de Weijer, “Computational color constancy: survey and experiments,” IEEE Trans. Image Process. 20, 2475–2489 (2011).
[CrossRef]

L. Shi, W. Xiong, and B. Funt, “Illumination estimation via thin-plate spline interpolation,” J. Opt. Soc. Am. A 28, 940–948 (2011).
[CrossRef]

2009

2008

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]

C. Fredembach and G. D. Finlayson, “The bright-chromagenic algorithm for illuminant estimation,” J. Imaging Sci. Technol. 52, 040906 (2008).
[CrossRef]

2007

2006

2004

2002

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]

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]

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]

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]

K. Barnard, L. Martin, B. Funt, and A. Coath, “A data set for color research,” Color Res. Appl. 27, 147–151 (2002).
[CrossRef]

2001

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]

K.-H. Bäuml, “Increments and decrements in color constancy,” J. Opt. Soc. Am. A 18, 2419–2429 (2001).
[CrossRef]

1997

1994

1993

1992

1990

D. A. Forsyth, “A novel algorithm for color constancy,” Int. J. Comput. Vis. 5, 5–35 (1990).
[CrossRef]

1988

1986

1980

G. Buchsbaum, “A spatial processor model for object colour perception,” J. Franklin Inst. 310, 1–26 (1980).
[CrossRef]

1958

Barnard, K.

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]

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]

K. Barnard, L. Martin, B. Funt, and A. Coath, “A data set for color research,” Color Res. Appl. 27, 147–151 (2002).
[CrossRef]

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]

K. Barnard, “Improvements to gamut mapping colour constancy algorithms,” in European Conference on Computer Vision (Springer, 2000), pp. 390–403.

Bäuml, K.-H.

Bianco, S.

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]

Blackwell, K. T.

Blake, A.

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.

Brainard, D. H.

Buchsbaum, G.

K. T. Blackwell and G. Buchsbaum, “Quantitative studies of color constancy,” J. Opt. Soc. Am. A 5, 1772–1780 (1988).
[CrossRef]

G. Buchsbaum, “A spatial processor model for object colour perception,” J. Franklin Inst. 310, 1–26 (1980).
[CrossRef]

Burnham, R. W.

Cardei, V.

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]

Cardei, V. C.

Celik, T.

T. Celik and T. Tjahjadi, “Adaptive colour constancy algorithm using discrete wavelet transform,” Comput. Vis. Image Underst. 116, 561–571 (2012).
[CrossRef]

Chakrabarti, A.

A. Chakrabarti, K. Hirakawa, and T. Zickler, “Color constancy with spatio-spectral statistics,” IEEE Trans. Pattern Anal. Mach. Intell. 34, 1509–1519 (2012).
[CrossRef]

Chang, F.-J.

Chao, W.-L.

Ciocca, G.

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]

Coath, A.

K. Barnard, L. Martin, B. Funt, and A. Coath, “A data set for color research,” Color Res. Appl. 27, 147–151 (2002).
[CrossRef]

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]

Cusano, C.

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]

D’Zmura, M.

DeBonet, J.

Dekker, N.

Drew, M. S.

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]

M. S. Drew and B. V. Funt, “Variational approach to interreflection in color images,” J. Opt. Soc. Am. A 9, 1255–1265 (1992).
[CrossRef]

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.

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.

Evans, R. M.

Finlayson, G. D.

C. Fredembach and G. D. Finlayson, “The bright-chromagenic algorithm for illuminant estimation,” J. Imaging Sci. Technol. 52, 040906 (2008).
[CrossRef]

S. D. Hordley and G. D. Finlayson, “Reevaluation of color constancy algorithm performance,” J. Opt. Soc. Am. A 23, 1008–1020 (2006).
[CrossRef]

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]

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]

G. D. Finlayson and E. Trezzi, “Shades of gray and colour constancy,” in Color and Imaging Conference (IS&T, 2004), pp. 37–41.

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.

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.

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.

Forsyth, D. A.

D. A. Forsyth, “A novel algorithm for color constancy,” Int. J. Comput. Vis. 5, 5–35 (1990).
[CrossRef]

Fredembach, C.

C. Fredembach and G. D. Finlayson, “The bright-chromagenic algorithm for illuminant estimation,” J. Imaging Sci. Technol. 52, 040906 (2008).
[CrossRef]

Freeman, W. T.

D. H. Brainard and W. T. Freeman, “Bayesian color constancy,” J. Opt. Soc. Am. A 14, 1393–1411 (1997).
[CrossRef]

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.

Funt, B.

L. Shi and B. Funt, “Maxrgb reconsidered,” J. Imaging Sci. Technol. 56, 1 (2012).
[CrossRef]

L. Shi, W. Xiong, and B. Funt, “Illumination estimation via thin-plate spline interpolation,” J. Opt. Soc. Am. A 28, 940–948 (2011).
[CrossRef]

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]

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]

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]

K. Barnard, L. Martin, B. Funt, and A. Coath, “A data set for color research,” Color Res. Appl. 27, 147–151 (2002).
[CrossRef]

B. Funt and H. Jiang, “Nondiagonal color correction,” in International Conference on Image Processing (IEEE, 2003), pp. 481–484.

Funt, B. V.

Gehler, P. V.

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.

Gevers, T.

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]

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]

A. Gijsenij, T. Gevers, and J. van de Weijer, “Computational color constancy: survey and experiments,” IEEE Trans. Image Process. 20, 2475–2489 (2011).
[CrossRef]

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]

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]

J. Van De Weijer, T. Gevers, and A. Gijsenij, “Edge-based color constancy,” IEEE Trans. Image Process. 16, 2207–2214 (2007).
[CrossRef]

Gijsenij, A.

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]

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]

A. Gijsenij, T. Gevers, and J. van de Weijer, “Computational color constancy: survey and experiments,” IEEE Trans. Image Process. 20, 2475–2489 (2011).
[CrossRef]

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]

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]

J. Van De Weijer, T. Gevers, and A. Gijsenij, “Edge-based color constancy,” IEEE Trans. Image Process. 16, 2207–2214 (2007).
[CrossRef]

Hirakawa, K.

A. Chakrabarti, K. Hirakawa, and T. Zickler, “Color constancy with spatio-spectral statistics,” IEEE Trans. Pattern Anal. Mach. Intell. 34, 1509–1519 (2012).
[CrossRef]

Hordley, S. D.

S. D. Hordley and G. D. Finlayson, “Reevaluation of color constancy algorithm performance,” J. Opt. Soc. Am. A 23, 1008–1020 (2006).
[CrossRef]

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]

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.

Hubel, P. M.

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]

Ikeuchi, K.

Iverson, G.

Jiang, H.

B. Funt and H. Jiang, “Nondiagonal color correction,” in International Conference on Image Processing (IEEE, 2003), pp. 481–484.

Joze, H. R. V.

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.

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.

Kawakami, R.

Krasilnikov, N. N.

Krasilnikova, O. I.

Lee, H.-C.

Lucassen, M. P.

Martin, L.

K. Barnard, L. Martin, B. Funt, and A. Coath, “A data set for color research,” Color Res. Appl. 27, 147–151 (2002).
[CrossRef]

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]

Minka, T.

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.

Morovic, P.

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.

Newhall, S. M.

Nishino, K.

Pei, S.-C.

Rey, P. A. T.

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.

Rother, C.

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.

Schefrin, B. E.

Schettini, R.

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]

Sharp, T.

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.

Shelepin, Y. E.

Shi, L.

Spehar, B.

Takamatsu, J.

Tan, R. T.

Tjahjadi, T.

T. Celik and T. Tjahjadi, “Adaptive colour constancy algorithm using discrete wavelet transform,” Comput. Vis. Image Underst. 116, 561–571 (2012).
[CrossRef]

Trezzi, E.

G. D. Finlayson and E. Trezzi, “Shades of gray and colour constancy,” in Color and Imaging Conference (IS&T, 2004), pp. 37–41.

Van De Weijer, J.

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]

A. Gijsenij, T. Gevers, and J. van de Weijer, “Computational color constancy: survey and experiments,” IEEE Trans. Image Process. 20, 2475–2489 (2011).
[CrossRef]

J. Van De Weijer, T. Gevers, and A. Gijsenij, “Edge-based color constancy,” IEEE Trans. Image Process. 16, 2207–2214 (2007).
[CrossRef]

van Trigt, C.

Wandell, B. A.

Weiss, Y.

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.

Werner, J. S.

Xiong, W.

Zaidi, Q.

Zickler, T.

A. Chakrabarti, K. Hirakawa, and T. Zickler, “Color constancy with spatio-spectral statistics,” IEEE Trans. Pattern Anal. Mach. Intell. 34, 1509–1519 (2012).
[CrossRef]

Color Res. Appl.

K. Barnard, L. Martin, B. Funt, and A. Coath, “A data set for color research,” Color Res. Appl. 27, 147–151 (2002).
[CrossRef]

Comput. Vis. Image Underst.

T. Celik and T. Tjahjadi, “Adaptive colour constancy algorithm using discrete wavelet transform,” Comput. Vis. Image Underst. 116, 561–571 (2012).
[CrossRef]

IEEE Trans. Image Process.

A. Gijsenij, T. Gevers, and J. van de Weijer, “Computational color constancy: survey and experiments,” IEEE Trans. Image Process. 20, 2475–2489 (2011).
[CrossRef]

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]

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]

J. Van De Weijer, T. Gevers, and A. Gijsenij, “Edge-based color constancy,” IEEE Trans. Image Process. 16, 2207–2214 (2007).
[CrossRef]

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]

IEEE Trans. Pattern Anal. Mach. Intell.

A. Chakrabarti, K. Hirakawa, and T. Zickler, “Color constancy with spatio-spectral statistics,” IEEE Trans. Pattern Anal. Mach. Intell. 34, 1509–1519 (2012).
[CrossRef]

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]

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]

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]

Int. J. Comput. Vis.

D. A. Forsyth, “A novel algorithm for color constancy,” Int. J. Comput. Vis. 5, 5–35 (1990).
[CrossRef]

J. Franklin Inst.

G. Buchsbaum, “A spatial processor model for object colour perception,” J. Franklin Inst. 310, 1–26 (1980).
[CrossRef]

J. Imaging Sci. Technol.

L. Shi and B. Funt, “Maxrgb reconsidered,” J. Imaging Sci. Technol. 56, 1 (2012).
[CrossRef]

C. Fredembach and G. D. Finlayson, “The bright-chromagenic algorithm for illuminant estimation,” J. Imaging Sci. Technol. 52, 040906 (2008).
[CrossRef]

J. Opt. Soc. Am.

J. Opt. Soc. Am. A

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]

S. D. Hordley and G. D. Finlayson, “Reevaluation of color constancy algorithm performance,” J. Opt. Soc. Am. A 23, 1008–1020 (2006).
[CrossRef]

R. Kawakami, J. Takamatsu, and K. Ikeuchi, “Color constancy from blackbody illumination,” J. Opt. Soc. Am. A 24, 1886–1893 (2007).
[CrossRef]

C. van Trigt, “Linear models in color constancy theory,” J. Opt. Soc. Am. A 24, 2684–2691 (2007).
[CrossRef]

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]

L. Shi, W. Xiong, and B. Funt, “Illumination estimation via thin-plate spline interpolation,” J. Opt. Soc. Am. A 28, 940–948 (2011).
[CrossRef]

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]

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]

G. Iverson and M. D’Zmura, “Criteria for color constancy in trichromatic bilinear models,” J. Opt. Soc. Am. A 11, 1970–1975 (1994).
[CrossRef]

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]

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]

D. H. Brainard and W. T. Freeman, “Bayesian color constancy,” J. Opt. Soc. Am. A 14, 1393–1411 (1997).
[CrossRef]

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]

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]

H.-C. Lee, “Method for computing the scene-illuminant chromaticity from specular highlights,” J. Opt. Soc. Am. A 3, 1694–1699 (1986).
[CrossRef]

K. T. Blackwell and G. Buchsbaum, “Quantitative studies of color constancy,” J. Opt. Soc. Am. A 5, 1772–1780 (1988).
[CrossRef]

M. S. Drew and B. V. Funt, “Variational approach to interreflection in color images,” J. Opt. Soc. Am. A 9, 1255–1265 (1992).
[CrossRef]

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]

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]

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]

K.-H. Bäuml, “Increments and decrements in color constancy,” J. Opt. Soc. Am. A 18, 2419–2429 (2001).
[CrossRef]

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]

J. Opt. Technol.

Other

B. Funt and H. Jiang, “Nondiagonal color correction,” in International Conference on Image Processing (IEEE, 2003), pp. 481–484.

G. D. Finlayson and E. Trezzi, “Shades of gray and colour constancy,” in Color and Imaging Conference (IS&T, 2004), pp. 37–41.

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.

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.

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.

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.

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.

K. Barnard, “Improvements to gamut mapping colour constancy algorithms,” in European Conference on Computer Vision (Springer, 2000), pp. 390–403.

Cited By

OSA participates in CrossRef's Cited-By Linking service. Citing articles from OSA journals and other participating publishers are listed here.

Alert me when this article is cited.


Figures (10)

Fig. 1.
Fig. 1.

In the spatial-domain methods, gradients serve as a means of computing color differences. Spatial gradients with strong responses can be attributed to scene content whose color values are far apart in the color domain as shown in this figure.

Fig. 2.
Fig. 2.

This figure shows example images from [40] where synthetic gradients are introduced by shuffling the image by blocks (top row). Note that the scene content and overall color distribution do not change. The gradients of these images projected on different color planes show that introduction of new gradients makes the distribution more elongated and directional. This shuffling actually improves the illumination estimation for a well-known spatial technique [21].

Fig. 3.
Fig. 3.

Probability map of the image (from [40]) gradients and the illumination information in the various regions of this map are shown. The magenta lines show the principal component analysis (PCA) vector of the pixels inside the yellow box (i.e., illumination information in small gradients), and the green lines show the PCA vector of the pixels outside the gray box (i.e., illumination information in large gradients). The black lines show the ground truth.

Fig. 4.
Fig. 4.

These images (from [40]) of different scenes are taken in the same illumination, but the error in illumination estimation using spatial-domain methods is quite different for the two images. The labels in the top corners of the images show the angular errors of the gray edge (GE) and weighted gray edge (WGE) algorithms.

Fig. 5.
Fig. 5.

Framework of the proposed method.

Fig. 6.
Fig. 6.

Illustration of the projection distance used in Eq. (5).

Fig. 7.
Fig. 7.

Effect of the control parameter n on the performance of the proposed method.

Fig. 8.
Fig. 8.

Strongly axial color distribution causes failure for most methods of color constancy. The black vectors in the bottom row are the actual illumination vectors (i.e., ground truth).

Fig. 9.
Fig. 9.

This figure shows some examples from the Color Checker dataset [40], in which the error in illumination estimation is high when only bright pixels (B) are used and reduces significantly when both bright and dark (BD) pixels are used.

Fig. 10.
Fig. 10.

Examples of images in our dataset.

Tables (4)

Tables Icon

Table 1. Control Parameters Used by Various Methodsa

Tables Icon

Table 2. Comparison of Mean, Median, Tri-Mean, and Maximum Angular Errors of Our Method with Those of Other Methods for Various Datasetsa

Tables Icon

Table 3. Comparison of Best 25% and Worst 25% of Images of Our Method with Those of Other Methods for Various Datasets

Tables Icon

Table 4. Training and Testing Time (in Minutes) for Our Canon 1Ds Mark III Dataset (Trends Are Similar for the Other Eight Cameras in our Dataset)

Equations (6)

Equations on this page are rendered with MathJax. Learn more.

tc=(x|Ic(x)|p)1pN,
J(x)=f(I(x)).
J(x)=w(x)nI(x),
εangle(eest)=cos1(eest·egteestegt).
dx=I(x)·I0I0,
I0=[tRtGtB],

Metrics