Xingsheng YUAN^*, Fengtao XIANG, and Zhengzhi WANG

College of Electronics and Automation, National University of Defense Technology, Changsha 410073, China

(Received December 18, 2012; Accepted May 16, 2013)

In this paper we propose a new gamut-constrained illuminant estimation framework using an improved category correlation method. Firstly, we obtain a set of feasible illuminations by the original gamut mapping method. Then, the probability of each feasible illumination as the ground truth illuminant is calculated according to its ability to map the corrected image onto specific colors using the improved category correlation method. Differently from the original category correlation method, to decrease the effect of image noise and the computation complexity, instead of using an entire pixel set for estimating the probability of portable illuminant, superpixel segments of an input image are used in our improved method. And the best illuminant estimate is given on the basis of the measure of the degree to which each feasible illumination is consistent with the image data. Experiment results prove that our improved method shows better current state-of-the-art performance Gamut mapping methods.

Key words: color constancy, color categories, color name, illuminant estimation

^*E-mail address: yuanxingsheng@nudt.edu.cn