Title :
Self-Adaptive Differential Evolution Methods for Unsupervised Image Classification
Author :
Omran, Mahamed G H ; Engelbrecht, Andries P.
Author_Institution :
Fac. of Comput. & IT, Arab Open Univ., Kuwait
Abstract :
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
Keywords :
evolutionary computation; geometry; pattern clustering; MRI image; centroids; clustering algorithm; satellite image; self-adaptive differential evolution; unsupervised image classification; unsupervised image segmentation; Clustering algorithms; Clustering methods; Image classification; Image segmentation; Iterative algorithms; Magnetic resonance imaging; Partitioning algorithms; Performance evaluation; Pixel; Satellites; Differential Evolution; clustering; self-adaptation; unsupervised image classification;
Conference_Titel :
Cybernetics and Intelligent Systems, 2006 IEEE Conference on
Conference_Location :
Bangkok
Print_ISBN :
1-4244-0023-6
DOI :
10.1109/ICCIS.2006.252239