Supervised Object Class Colour Normalisation

Colour is an important cue in many applications of computer vision and image processing, but robust usage often requires estimation of the unknown illuminant colour. Usually, to obtain images invariant to the illumination conditions under which they were taken, color normalisation is used. In this work, we develop a such colour normalisation technique, where true colours are not important per se but where examples of same classes have photometrically consistent appearance. This is achieved by supervised estimation of a class specific canonical colour space where the examples have minimal variation in their colours. We demonstrate the effectiveness of our method with qualitative and quantitative examples from the Caltech-101 data set and a real application of 3D pose estimation for robot grasping.