Color Constancy is the ability of the human visual system to perceive colors unchanged independently of the 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 paper, we propose 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, given approach may be applied to any CNN. Additionally, we discuss design of a new loss function oriented specifically to this problem, and propose a few the most suitable options.
@article{arxiv.1811.08456,
title = {Artificial Color Constancy via GoogLeNet with Angular Loss Function},
author = {Oleksii Sidorov},
journal= {arXiv preprint arXiv:1811.08456},
year = {2019}
}