Title :
Artificial Bee Colony based image clustering method
Author :
Hancer, Emrah ; Ozturk, Celal ; Karaboga, Dervis
Author_Institution :
Comput. Eng. Dept., Erciyes Univ., Kayseri, Turkey
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.
Keywords :
image processing; learning (artificial intelligence); particle swarm optimisation; pattern clustering; ABC based image clustering method; PSO algorithm; artificial bee colony; image analysis; image content; image description; image understanding; k-means clustering; medical application; particle swarm optimization; pattern recognition; statistical data analysis; Algorithm design and analysis; Clustering algorithms; Heuristic algorithms; Magnetic resonance imaging; Optimization; Particle swarm optimization; Partitioning algorithms; Artificial bee colony algorithm; Image clustering; K-means; Particle swarm optimization;
Conference_Titel :
Evolutionary Computation (CEC), 2012 IEEE Congress on
Conference_Location :
Brisbane, QLD
Print_ISBN :
978-1-4673-1510-4
Electronic_ISBN :
978-1-4673-1508-1
DOI :
10.1109/CEC.2012.6252919