  In this paper, we provide a novel dataset designed for camera invariant color
constancy research. Camera invariance corresponds to the robustness of an
algorithm's performance when run on images of the same scene taken by different
cameras. Accordingly, images in the database correspond to several lab and
field scenes each of which are 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 a recently proposed convolutional neural network based color constancy
algorithm as a baseline 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.
