A Dataset for Camera Independent Color Constancy

In this paper, we provide a novel dataset designed for camera independent color constancy research. Camera independence corresponds to the robustness of an algorithm’s performance when run on images of the same scene taken by different cameras. Accordingly, the images in our database correspond to several lab and field scenes each of which is captured by three different cameras with minimal registration errors. The lab scenes are also captured under five different illuminations. The spectral responses of cameras and the spectral power distributions of the lab light sources are also provided, as they may prove beneficial for training future algorithms to achieve color constancy. For a fair evaluation of future methods, we provide guidelines for supervised methods with indicated training, validation and testing partitions. Accordingly, we evaluate two recently proposed convolutional neural network based color constancy algorithms as baselines for future research. As a side contribution, this dataset also includes images taken by a mobile camera with color shading corrected and uncorrected results. This allows research on the effect of color shading as well.