Artificial Color Constancy via GoogLeNet with Angular Loss Function

ABSTRACT

Color constancy is the ability of the human visual system to perceive colors unchanged independently of illumination. Giving a machine this feature will be beneficial in many fields where chromatic information is used. Particularly, it significantly improves scene understanding and object recognition.In this article, we propose a transfer learning-based algorithm, which has two main features: accuracy higher than many state-of-the-art algorithms and simplicity of implementation. Despite the fact that GoogLeNet was used in the experiments, the given approach may be applied to any convolutional neural networks. Additionally, we discuss the design of a new loss function oriented specifically to this problem and propose a few of the most suitable options.