Convolutional Color Constancy
Abstract
Color constancy is the problem of inferring the color of the light that illuminated
a scene, usually so that the illumination color can be removed. Because this
problem is underconstrained, it is often solved by modeling the statistical
regularities of the colors of natural objects and illumination. In contrast, in
this paper we reformulate the problem of color constancy as a 2D spatial
localization task in a log-chrominance space, thereby allowing us to apply
techniques from object detection and structured prediction to the color constancy
problem. By directly learning how to discriminate between correctly white-balanced
images and poorly white-balanced images, our model is able to improve performance
on standard benchmarks by nearly 40%.
