Handling Fuzzy Image Clustering with a Modified ABC Algorithm

Image segmentation can be cast as a clustering task where the image is partitioned into clusters. Pixels within the same cluster are as homogenous as possible whereas pixels belonging to different clusters are not similar in terms of an appropriate similarity measure. Several clustering methods have been proposed for image segmentation purpose among which the Fuzzy C-Means clustering algorithm. However this algorithm still suffers from some drawbacks, such as local optima and sensitivity to initialization. Artificial Bees Colony algorithm is a recent population-based optimization method which has been successfully used in many complex problems. In this paper, we propose a new fuzzy clustering algorithm based on a modified Artificial Bees Colony algorithm, in which a new mutation strategy inspired from the Differential Evolution is introduced in order to improve the exploitation process. Experimental results show that our proposed approach improves the performance of the basic fuzzy C-Means clustering algorithm and outperforms other population based optimization methods.