<title>Perfect color constancy vs. color normalization for object recognition</title>

Abstract

Colors recorded in an image depend on the color of the capture illuminant. As such image colors are not stable features for object recognition but we wish they were stable since perceived colors (the colors we see) are illuminant independent and do correlate with object identity. Color constancy algorithms attempt to infer and remove the illuminant color through image analysis. Over the last two decades, various models for color constancy have been developed. Unfortunately, color constancy algorithms are still not good enough to support object recognition. In this paper, we evaluate optimal color constancy procedures against color normalization. Two perfect color constancy algorithms are described. One is perfect color constancy by the scene, which arrives at an estimate of the illuminant not through algorithmic inference, but through measurement: the light source is measured using a spectraradiometer, assuming the reflectances of object surface are known. The other is perfect color color constancy by the illuminant, which arrives at an estimate of the illuminant through measurement, assuming the reflectances of object surface are unknown. Instead of color constancy, color normalization normalizes color images in terms of the context to remove illumination. To remove dependency due to illumination, images in a calibrated dataset are preprocessed using either the color constancy or color invariant normalization. Two experiments are reported in the paper. In the first experiment, the optimal algorithms of perfect color constancy based on measurement were tested using a calibrated image dataset. In the second experiment, the performances of the optimal color constancy algorithms are compared with color invariant normalization. Unfortunately, measurement driven color constancy by the illuminant does not support perfect recognition. However, color constancy preprocessing based on a scene dependent 'effective illuminant' facilitates near-perfect recognition. In comparison the color invariant normalization also deliver near-perfect recognition. The failure of color constancy by the illuminant is understandable because the measured illuminant doesn't correspond to the actual effective illuminant. Rather, we found illumination to depend both on the light source and characteristics of the scene.