A Novel Approach to Image Clustering using Elitist Genetic Algorithm

Genetic Algorithm (GA) is a stochastic randomized blind search and optimization technique based on evolutionary computing that has already been proved to be robust and effective from its outcome in solving problems from variety of application domains. Clustering is a vital technique to extract meaningful and hidden information from the datasets. Clustering techniques have a broad field of application including bioinformatics, image processing and data mining. In order to the find the close association between the densities of data points, in the given dataset of pixels of an image, clustering provides an easy analysis and proper validation. In this paper, we propose an evolutionary computing based approach for unsupervised image clustering using elitist GA (EGA) – a efficient variant of GA that segments an image into its constituent parts automatically. The aim of this algorithm is to produce precise segmentation of images using intensity information along with their neighbourhood relationships. Experimental results from simulation study reveal that the algorithm generates good quality segmented image.