Hostname: page-component-77f85d65b8-6bnxx Total loading time: 0 Render date: 2026-04-12T07:04:06.442Z Has data issue: false hasContentIssue false
  • English
  • Français

Color constancy in natural scenes explained by global image statistics

Published online by Cambridge University Press:  06 September 2006

DAVID H. FOSTER ,
KINJIRO AMANO  and
SÉRGIO M.C. NASCIMENTO
DAVID H. FOSTER
Affiliation:
Sensing, Imaging, and Signal Processing Group, School of Electrical and Electronic Engineering, University of Manchester, Manchester, United Kingdom
KINJIRO AMANO
Affiliation:
Sensing, Imaging, and Signal Processing Group, School of Electrical and Electronic Engineering, University of Manchester, Manchester, United Kingdom
SÉRGIO M.C. NASCIMENTO
Affiliation:
Department of Physics, Gualtar Campus, University of Minho, Braga, Portugal
Get access Rights & Permissions [Opens in a new window]

Abstract

To what extent do observers' judgments of surface color with natural scenes depend on global image statistics? To address this question, a psychophysical experiment was performed in which images of natural scenes under two successive daylights were presented on a computer-controlled high-resolution color monitor. Observers reported whether there was a change in reflectance of a test surface in the scene. The scenes were obtained with a hyperspectral imaging system and included variously trees, shrubs, grasses, ferns, flowers, rocks, and buildings. Discrimination performance, quantified on a scale of 0 to 1 with a color-constancy index, varied from 0.69 to 0.97 over 21 scenes and two illuminant changes, from a correlated color temperature of 25,000 K to 6700 K and from 4000 K to 6700 K. The best account of these effects was provided by receptor-based rather than colorimetric properties of the images. Thus, in a linear regression, 43% of the variance in constancy index was explained by the log of the mean relative deviation in spatial cone-excitation ratios evaluated globally across the two images of a scene. A further 20% was explained by including the mean chroma of the first image and its difference from that of the second image and a further 7% by the mean difference in hue. Together, all four global color properties accounted for 70% of the variance and provided a good fit to the effects of scene and of illuminant change on color constancy, and, additionally, of changing test-surface position. By contrast, a spatial-frequency analysis of the images showed that the gradient of the luminance amplitude spectrum accounted for only 5% of the variance.

Keywords

Natural scenesColor constancyImage statisticsSpatial cone-excitation ratiosSpatial-frequency analysis

Information

Type
SURFACE COLOR PERCEPTION
Information
Visual Neuroscience , Volume 23 , Issue 3-4 , May 2006 , pp. 341 - 349
Copyright
© 2006 Cambridge University Press

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

Article purchase

Temporarily unavailable

References

REFERENCES

Amano, K. & Foster, D.H. (2004). Colour constancy under simultaneous changes in surface position and illuminant. Proceedings of the Royal Society of London Series B—Biological Sciences 271, 2319–2326.Google Scholar
Arend, L.E., Jr., Reeves, A., Schirillo, J., & Goldstein, R. (1991). Simultaneous color constancy: Papers with diverse Munsell values. Journal of the Optical Society of America A. Optics, Image Science, and Vision 8, 661–672.Google Scholar
Baraas, R., Foster, D.H., Amano, K., & Nascimento, S.M.C. (2006). Anomalous trichromats' judgments of surface color in natural scenes under different daylight. Visual Neuroscience 23, 629–635.Google Scholar
Bramwell, D.I. & Hurlbert, A.C. (1996). Measurements of colour constancy by using a forced-choice matching technique. Perception 25, 229–241.Google Scholar
Brenner, E. & Cornelissen, F.W. (1998). When is a background equivalent? Sparse chromatic context revisited. Vision Research 38, 1789–1793.Google Scholar
Brenner, E., Ruiz, J.S., Herráiz, E.M., Cornelissen, F.W., & Smeets, J.B.J. (2003). Chromatic induction and the layout of colours within a complex scene. Vision Research 43, 1413–1421.Google Scholar
Brown, R.O. & MacLeod, D.I.A. (1997). Color appearance depends on the variance of surround colors. Current Biology 7, 844–849.Google Scholar
Ciurea, F. & Funt, B. (2003). A large image database for color constancy research. In Eleventh Color Imaging Conference: Color Science and Engineering Systems, Technologies, and Applications, pp. 160–164. Scottsdale, AZ: Society for Imaging Science and Technology.
Cleveland, W.S. & Devlin, S.J. (1988). Locally weighted regression: An approach to regression analysis by local fitting. Journal of the American Statistical Association 83, 596–610.Google Scholar
Courtney, S.M., Finkel, L.H., & Buchsbaum, G. (1995). Network simulations of retinal and cortical contributions to color constancy. Vision Research 35, 413–434.Google Scholar
Craven, B.J. & Foster, D.H. (1992). An operational approach to colour constancy. Vision Research 32, 1359–1366.Google Scholar
Draper, N.R. & Smith, H. (1998). Applied Regression Analysis, 3rd ed. New York: Wiley.
Efron, B. & Tibshirani, R.J. (1993). An Introduction to the Bootstrap. New York: Chapman & Hall.
Fairchild, M.D. (2005). Color Appearance Models. Chichester: John Wiley & Sons, Ltd.
Federal Geographic Data Committee. (1997). National Vegetation Classification Standard. FGDC-STD-005. U.S. Geological Survey, Reston, Virginia.
Field, D.J. (1987). Relations between the statistics of natural images and the response properties of cortical cells. Journal of the Optical Society of America A. Optics, Image Science, and Vision 4, 2379–2394.Google Scholar
Foster, D.H. (2003). Does colour constancy exist? Trends in Cognitive Sciences 7, 439–443.Google Scholar
Foster, D.H., Amano, K., & Nascimento, S.M.C. (2001a). Colour constancy from temporal cues: Better matches with less variability under fast illuminant changes. Vision Research 41, 285–293.Google Scholar
Foster, D.H., Amano, K., & Nascimento, S.M.C. (2003). Tritanopic colour constancy under daylight changes? In Normal & Defective Colour Vision, eds. Mollon, J.D., Pokorny, J. & Knoblauch, K., pp. 218–224. Oxford, UK: Oxford University Press.
Foster, D.H., Amano, K., & Nascimento, S.M.C. (2006). Frequency of metamerism in natural scenes. Journal of the Optical Society of America A. Optics, Image Science, and Vision (in press).Google Scholar
Foster, D.H. & Nascimento, S.M.C. (1994). Relational colour constancy from invariant cone-excitation ratios. Proceedings of the Royal Society of London Series B—Biological Sciences 257, 115–121.Google Scholar
Foster, D.H., Nascimento, S.M.C., & Amano, K. (2004). Information limits on neural identification of colored surfaces in natural scenes. Visual Neuroscience 21, 331–336.Google Scholar
Foster, D.H., Nascimento, S.M.C., Amano, K., Arend, L., Linnell, K.J., Nieves, J.L., Plet, S., & Foster, J.S. (2001b). Parallel detection of violations of color constancy. Proceedings of the National Academy of Sciences of the United States of America 98, 8151–8156.Google Scholar
Hurlbert, A. & Wolf, K. (2004). Color contrast: A contributory mechanism to color constancy. Progress in Brain Research 144, 147–160.Google Scholar
Jenness, J.W. & Shevell, S.K. (1995). Color appearance with sparse chromatic context. Vision Research 35, 797–805.Google Scholar
Judd, D.B., MacAdam, D.L., & Wyszecki, G. (1964). Spectral distribution of typical daylight as a function of correlated color temperature. Journal of the Optical Society of America 54, 1031–1040.Google Scholar
Knill, D.C., Field, D., & Kersten, D. (1990). Human discrimination of fractal images. Journal of the Optical Society of America A. Optics, Image Science, and Vision 7, 1113–1123.Google Scholar
Kraft, J.M. & Brainard, D.H. (1999). Mechanisms of color constancy under nearly natural viewing. Proceedings of the National Academy of Sciences of the United States of America 96, 307–312.Google Scholar
Kulikowski, J.J., Stanikunas, R., Jurkutaitis, M., Vaitkevicius, H., & Murray, I.J. (2001). Colour and brightness shifts for isoluminant samples and backgrounds. Color Research and Application 26, S205–S208.Google Scholar
Li, C.-J., Luo, M.R., Rigg, B., & Hunt, R.W.G. (2002). CMC 2000 chromatic adaptation transform: CMCCAT2000. Color Research and Application 27, 49–58.Google Scholar
Lucassen, M.P. & Walraven, J. (1993). Quantifying color constancy: Evidence for nonlinear processing of cone-specific contrast. Vision Research 33, 739–757.Google Scholar
Lucassen, M.P. & Walraven, J. (2005). Separate processing of chromatic and achromatic contrast in color constancy. Color Research and Application 30, 172–185.Google Scholar
Luo, M.R., Cui, G., & Rigg, B. (2001). The development of the CIE 2000 colour-difference formula: CIEDE2000. Color Research and Application 26, 340–350.Google Scholar
Morovič, J. & Morovič, P. (2005). Can highly chromatic stimuli have a low color inconstancy index? In Thirteenth Color Imaging Conference: Color Science and Engineering Systems, Technologies, and Applications, pp. 321–325. Scottsdale, AZ: Society for Imaging Science and Technology.
Nascimento, S.M.C., de Almeida, V.M.N., Fiadeiro, P.T., & Foster, D.H. (2004). Minimum-variance cone-excitation ratios and the limits of relational color constancy. Visual Neuroscience 21, 337–340.Google Scholar
Nascimento, S.M.C., Ferreira, F.P., & Foster, D.H. (2002). Statistics of spatial cone-excitation ratios in natural scenes. Journal of the Optical Society of America A. Optics, Image Science, and Vision 19, 1484–1490.Google Scholar
Nascimento, S.M.C. & Foster, D.H. (1997). Detecting natural changes of cone-excitation ratios in simple and complex coloured images. Proceedings of the Royal Society of London Series B—Biological Sciences 264, 1395–1402.Google Scholar
Párraga, C.A., Troscianko, T., & Tolhurst, D.J. (2005). The effects of amplitude-spectrum statistics on foveal and peripheral discrimination of changes in natural images, and a multi-resolution model. Vision Research 45, 3145–3168.Google Scholar
Shevell, S.K. & Wei, J. (1998). Chromatic induction: Border contrast or adaptation to surrounding light? Vision Research 38, 1561–1566.Google Scholar
Smithson, H.E. (2005). Sensory, computational and cognitive components of human colour constancy. Philosophical Transactions of the Royal Society B—Biological Sciences 360, 1329–1346.Google Scholar
Thomson, M.G.A. & Foster, D.H. (1997). Role of second- and third-order statistics in the discriminability of natural images. Journal of the Optical Society of America A. Optics, Image Science, and Vision 14, 2081–2090.Google Scholar
Tiplitz Blackwell, K. & Buchsbaum, G. (1988). Quantitative studies of color constancy. Journal of the Optical Society of America A. Optics, Image Science, and Vision 5, 1772–1780.Google Scholar
Tolhurst, D.J., Tadmor, Y., & Chao, T. (1992). Amplitude spectra of natural images. Ophthalmic and Physiological Optics 12, 229–232.Google Scholar
UNESCO. (1973). International classification and mapping of vegetation. Paris, France: UNESCO Publishing.
Wachtler, T., Albright, T.D., & Sejnowski, T.J. (2001). Nonlocal interactions in color perception: Nonlinear processing of chromatic signals from remote inducers. Vision Research 41, 1535–1546.Google Scholar
Walsh, V. (1999). How does the cortex construct color? Proceedings of the National Academy of Sciences of the United States of America 96, 13594–13596.Google Scholar
Webster, M.A. & Mollon, J.D. (1995). Color constancy influenced by contrast adaptation. Nature 373, 694–698.Google Scholar
Werner, A. (2003). The spatial tuning of chromatic adaptation. Vision Research 43, 1611–1623.Google Scholar
Westland, S. & Ripamonti, C. (2000). Invariant cone-excitation ratios may predict transparency. Journal of the Optical Society of America A. Optics, Image Science, and Vision 17, 255–264.Google Scholar
Wyszecki, G. & Stiles, W.S. (1982). Color Science: Concepts and Methods, Quantitative Data and Formulae. New York: John Wiley & Sons.
Zaidi, Q. (2001). Color constancy in a rough world. Color Research and Application 26, S192–S200.Google Scholar
Zaidi, Q., Spehar, B., & DeBonet, J. (1997). Color constancy in variegated scenes: Role of low-level mechanisms in discounting illumination changes. Journal of the Optical Society of America A. Optics, Image Science, and Vision 14, 2608–2621.Google Scholar