Self-adaptive differential evolution methods for unsupervised image classification

A clustering method that is based on Self-Adaptive Differential Evolution (SDE) is developed in this paper. SDE is a self-adaptive version of DE where parameter tuning is not required. The proposed algorithm finds the centroids of a user specified number of clusters, where each cluster groups together similar patterns. The application of the proposed clustering algorithm to the problem of unsupervised classification and segmentation of images is investigated. To illustrate its wide applicability, the proposed algorithm is then applied to synthetic, MRI and satellite images. Experimental results show that the SDE clustering algorithm performs very well compared to other state-of-the-art clustering algorithms in all measured criteria.