Evaluation of colour models for computer vision using cluster validation techniques

Abstract

Computer vision systems frequently employ colour segmentation as a step of feature extraction. This is particularly crucial in an environment where important features are colour-coded, such as robot soccer. This paper describes a method for determining an appropriate colour model by measuring the compactness and separation of clusters produced by the k-means algorithm. RGB, HSV, YC b Cr and CIE L*a*b* colour models are assessed for a selection of artificial and real images, utilising an implementation of the Dunn's-based cluster validation index. The effectiveness of the method is assessed by qualitatively comparing the relative correctness of the segmentation to the results of the cluster validation. Results demonstrate a significant variation in segmentation quality among colour spaces, and that YC b Cr is the best choice for the DARwIn-OP platform tested. © 2013 Springer-Verlag.