  In this paper, we propose a novel color constancy approach, called Bag of
Color Features (BoCF), building upon Bag-of-Features pooling. The proposed
method substantially reduces the number of parameters needed for illumination
estimation. At the same time, the proposed method is consistent with the color
constancy assumption stating that global spatial information is not relevant
for illumination estimation and local information ( edges, etc.) is sufficient.
Furthermore, BoCF is consistent with color constancy statistical approaches and
can be interpreted as a learning-based generalization of many statistical
approaches. To further improve the illumination estimation accuracy, we propose
a novel attention mechanism for the BoCF model with two variants based on
self-attention. BoCF approach and its variants achieve competitive, compared to
the state of the art, results while requiring much fewer parameters on three
benchmark datasets: ColorChecker RECommended, INTEL-TUT version 2, and NUS8.
