Map Segmentation by Colour Cube Genetic K-Mean Clustering

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 ...

• Type: Conference
• Year: 2000
• Where: ERCIMDL
• Authors: Vitorino Ramos, Fernando Muge
• Keywords: Education, ERCIMDL 2000, Genetic Algorithms, Texture Fields, Unsupervised Texture Segmentation