October 2003
Volume 3, Issue 9
Free
Vision Sciences Society Annual Meeting Abstract  |   October 2003
Color constancy as probabilistic inference: managing the tradeoff between the illuminant prior and scene evidence
Author Affiliations
  • Eric T Ortega
    University of Southern California, United States of America
Journal of Vision October 2003, Vol.3, 706. doi:10.1167/3.9.706
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      Eric T Ortega, Bartlett W Mel; Color constancy as probabilistic inference: managing the tradeoff between the illuminant prior and scene evidence. Journal of Vision 2003;3(9):706. doi: 10.1167/3.9.706.

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      © ARVO (1962-2015); The Authors (2016-present)

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Abstract

Color is a powerful cue for object recognition. To maximize the utility of color cues, however, a vision system must try to eliminate the colorizing effects of the illuminant to access the true underlying colors of object surfaces. Starting with a foundation in Bayesian statistics, we have developed a heuristic approach to color constancy which combines an illuminant prior with evidence from the scene to estimate the most probable lighting. This estimate is a linear combination of the a priori most probable illuminant and the average chromaticity over the set of S surfaces in the scene. To determine the scene's average surface color we preprocess using anisotropic diffusion, determine the distribution of chromaticities in the scene, and then fit with a constrained mixture of gaussians using S as a parameter. The mean color of the S gaussian generators is taken as the average scene color. A weighting factor B(S) sets the relative contributions of prior vs. evidence: the more colorful the scene, the heavier the weighting of the evidence. The function B is a saturating curve whose basic form emerged from monte carlo simulations using gaussian distributions for the illuminant and reflectance priors. To benchmark our algorithm, we developed a difficult color-based recognition task using 1,500 total images of 100 objects using 3 backgrounds and 5 lighting conditions. We analyze and discuss the relative strengths and weaknesses of our algorithm in comparison to grey world, brightest-is-whitest, and several other published methods including Retinex in Matlab (Funt, et al, 2000), Comprehensive Color Image Normalization (Finlayson, et al, 1998), and Multi-Scale Retinex (Jobson, et al, 1997). We end by speculating as to the neural basis for the operations involved in illuminant color estimation and color constant visual perception.

Ortega, E. T., Mel, B. W.(2003). Color constancy as probabilistic inference: managing the tradeoff between the illuminant prior and scene evidence [Abstract]. Journal of Vision, 3( 9): 706, 706a, http://journalofvision.org/3/9/706/, doi:10.1167/3.9.706. [CrossRef]
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