  The goal of computational color constancy is to preserve the perceptive
colors of objects under different lighting conditions by removing the effect of
color casts caused by the scene's illumination. With the rapid development of
deep learning based techniques, significant progress has been made in image
semantic segmentation. In this work, we exploit the semantic information
together with the color and spatial information of the input image in order to
remove color casts. We train a convolutional neural network (CNN) model that
learns to estimate the illuminant color and gamma correction parameters based
on the semantic information of the given image. Experimental results show that
feeding the CNN with the semantic information leads to a significant
improvement in the results by reducing the error by more than 40%.
