The choice of a colour space is of great importance for many computer vision algorithms (e.g. edge detection and object recognition). It induces the equivalence classes to the actual algorithms. Since there are many colour spaces available, the problem is how to automatically select the weighting to integrate the colour spaces in order to produce the best result for a particular task. In this paper we propose a method to learn these weights, while exploiting the non-perfect correlation between colour spaces of features through the principle of diversification. As a result an optimal trade-off is achieved between repeatability and distinctiveness. The resulting weighting scheme will ensure maximal feature discrimination.
The method is experimentally verified for three feature detection tasks: Skin colour detection, edge detection and corner detection. In all three tasks the method achieved an optimal trade-off between (colour) invariance (repeatability) and discriminative power (distinctiveness).