UvA-DARE (Digital Academic Repository) Color Constancy by Deep Learning Color Constancy by Deep Learning

Computational color constancy aims to estimate the color of the light source. The performance of many vision tasks, such as object detection and scene understanding, may benefit from color constancy by using the corrected object colors. Since traditional color constancy methods are based on specific assumptions, none of those methods can be used as a universal predictor. Further, shallow learning schemes are used for training-based color constancy, possibly suffering from limited learning capabilities. In this paper, we propose a new framework using Deep Neural Networks (DNNs) to obtain accurate light source estimation. We reformulate color constancy as a DNN-based regression approach to estimate the color of the light source. The model is trained using datasets of more than a million images. Experiments show that the proposed algorithm outperforms the state-of-the-art by 9%. Especially in cross dataset validation, our approach reduces the median angular error by 35%. Our algorithm operates at more than 100 fps during testing.