Learning Color Constancy: 30 Years Later

The first paper investigating the use of machine learning to learn the relationship between an image of a scene and the color of the scene illuminant was published by Funt et al. in 1996. Specifically, they investigated if such a relationship could be learned by a neural network. During the last 30 years we have witnessed a remarkable series of advancements in machine learning, and in particular deep learning approaches based on artificial neural networks. In this paper we want to update the method by Funt et al. by including recent techniques introduced to train deep neural networks. Experimental results on a standard dataset show how the updated version can improve the median angular error in illuminant estimation by almost 51% with respect to its original formulation, even outperforming recent illuminant estimation methods.