ARTIFICIAL BEE COLONY BASED IMAGE CLUSTERING METHOD


Hancer E. , ÖZTÜRK C. , KARABOĞA D.

IEEE Congress on Evolutionary Computation (CEC), Brisbane, Australia, 10 - 15 June 2012 identifier identifier

Abstract

Clustering plays important role in many areas such as medical applications, pattern recognition, image analysis and statistical data analysis. Image clustering is an application of image analysis in order to support high-level description of image content for image understanding where the goal is finding a mapping of the images into clusters. This paper presents an Artificial Bee Colony (ABC) based image clustering method to find clusters of an image where the number of clusters is specified. The proposed method is applied to three benchmark images and the performance of it is analysed by comparing the results of K-means and Particle Swarm Optimization (PSO) algorithms. The comprehensive results demonstrate both analytically and visually that ABC algorithm can be successfully applied to image clustering.