A multistage neural network for color constancy and color induction

Abstract

A biologically-based multistage neural network is presented which produces color constant responses to a variety of color stimuli. The network takes advantage of several mechanisms in the human visual system, including retinal adaptation, spectral opponency, and spectrally-specific long-range inhibition. This last stage is a novel mechanism based on cells which have been described in cortical area V4. All stages include nonlinear response functions. The model emulates human performance in several psychophysical paradigms designed to test color constancy and color induction. We measured the amount of constancy achieved with both natural and artificial simulated illuminants, using homogeneous grey backgrounds and more complex backgrounds, such as Mondrians. On average, the model performs as well or better than the average human color constancy performance under similar conditions. The network simulation also displays color induction and assimilation behavior consistent with human perceptual data.