Spatial structure in a color image can be represented using correlation functions defined within and between sensor bands. Using a linear model for surface spectral reflectance with the same number of parameters as the number of classes of photoreceptors, we show that illumination changes correspond to linear transformations of a surface correlation matrix. From this relationship, we derive a distance function for comparing sets of spatial correlation functions that can be used for illumination-invariant recognition. We demonstrate using a large body of experiments that this distance function can be used for accurate texture classification in the presence of large changes in illumination spectral distribution.
Glenn Healey, Lizhi Wang, "Using Linear Models for the Illumination-Invariant Classification of Color Textures" in Proc. IS&T 3rd Color and Imaging Conf., 1995, pp 123 - 125, https://doi.org/10.2352/CIC.1995.3.1.art00032