Automatic cluster evolution using gravitational search algorithm and its application on image segmentation

In real life problems, prior information about the number of clusters is not known. In this paper, an attempt has been made to determine the number of clusters using automatic clustering using gravitational search algorithm (ACGSA). Based on the statistical property of datasets, two new concepts are proposed to efficiently find the optimal number of clusters. Within the ACGSA, a variable chromosome representation is used to encode the cluster centers with different number of clusters. In order to refine cluster centroids, two new operations namely threshold setting and weighted cluster centroid computation are also introduced. Finally, a new fitness function is proposed to make the search more efficient. A comparison of the proposed technique is also carried out with automatic clustering techniques developed recently. The proposed technique is further applied for automatic segmentation of both grayscale and color images and its performance is compared with other techniques. Experimental results demonstrate the efficiency and efficacy of the proposed clustering technique over other existing techniques.