A Supervised Combination Strategy for Illumination Chromaticity Estimation

Color constancy is an important perceptual ability of humans to recover the color of objects invariant of light information. It is also necessary for a robust machine vision system. Until now, a number of color constancy algorithms have been proposed in the literature. In particular, the edge-based color constancy uses the edge of an image to estimate light color. It is shown to be a rich framework that can represent many existing illumination estimation solutions with various parameter settings. However, color constancy is an ill-posed problem; every algorithm is always given out under some assumptions and can only produce the best performance when these assumptions are satisfied. In this article, we have investigated a combination strategy relying on the Extreme Learning Machine (ELM) technique that integrates the output of edge-based color constancy with multiple parameters. Experiments on real image data sets show that the proposed method works better than most single-color constancy methods and even some current state-of-the-art color constancy combination strategies.