Colour quantisation as a preprocessing step for image segmentation

Abstract

Colour quantisation is very often used as an auxiliary operation in colour image processing, e.g. this operation can reduce the complexity of image segmentation process. In this chapter the results of segmentation preceded by a colour quantisation have been compared with segmentation without such preprocessing step. The choice of tools for the experiment was, for obvious reasons, limited to some colour quantisation and image segmentation methods. The colour quantisation techniques based on clustering of pixels, i.e. the classic k - means technique (KM) and new k - harmonicmeans technique (KHM) were considered. For image segmentation the unseeded region growing (USRG) technique has been selected from a variety of known techniques. Evaluation of the results was based on empirically defined quality function used for segmentation results. Not every method of colour quantisation, carried out as preprocessing step in the process of segmentation, leads to improved segmentation result. Therefore, our approach needs a good quantisation technique, e.g. researched segmentation technique works better for KHM quantisation technique than KM technique. This study uses different images acquired from relatively simple scenes without significant highlights and shadows. An interesting open question is what kind of colour images needs to be quantised before the segmentation. Perhaps an estimation of image segmentation difficulty will help to answer this question. The further research should be focused on establishing the conditions and parameters of additional improvement in image segmentation preceded by a colour quantisation.