  Segmentation of a colour image composed of different kinds of texture regions
can be a hard problem, namely to compute for an exact texture fields and a
decision of the optimum number of segmentation areas in an image when it
contains similar and/or unstationary texture fields. In this work, a method is
described for evolving adaptive procedures for these problems. In many real
world applications data clustering constitutes a fundamental issue whenever
behavioural or feature domains can be mapped into topological domains. We
formulate the segmentation problem upon such images as an optimisation problem
and adopt evolutionary strategy of Genetic Algorithms for the clustering of
small regions in colour feature space. The present approach uses k-Means
unsupervised clustering methods into Genetic Algorithms, namely for guiding
this last Evolutionary Algorithm in his search for finding the optimal or
sub-optimal data partition, task that as we know, requires a non-trivial search
because of its intrinsic NP-complete nature. To solve this task, the
appropriate genetic coding is also discussed, since this is a key aspect in the
implementation. Our purpose is to demonstrate the efficiency of Genetic
Algorithms to automatic and unsupervised texture segmentation. Some examples in
Colour Maps, Ornamental Stones and in Human Skin Mark segmentation are
presented and overall results discussed. KEYWORDS: Genetic Algorithms, Colour
Image Segmentation, Classification, Clustering.
