Colour space fusion for texture recognition
Samuel Chindaro, Konstantinos Sirlantzis, Farzin Deravi · 2004
In this paper we propose a novel approach to colour texture classification based on fusion of the information contained in different colour spaces. In colour texture classification the choice of the most effective colour space to use is still an open issue. However, combining the strengths of different colour spaces may offer an alternative solution to the problem of robust texture discrimination. The principal aim of the work presented here is to study the performance of such decision combination approaches using classifiers obtained through training on features extracted from a number of colour space and subspace representations of the same texture classes. To this end we performed a number of cross-validation experiments involving six different colour spaces and their chromatic subspaces. Our results strongly suggest that colour texture classification can benefit significantly from techniques based on multiple classifier combination strategies.