Automatic Image Pixel Clustering based on Mussels Wandering Optimization

Abstract

Image pixel clustering or segmentation intends to identify pixel groups on an image without any preliminary labels. It remains a challenging task in computer vision since the size and shape of object segments are varied. Moreover, determining the segment number in an image without prior knowledge of the image content is an NP-hard problem. In this paper, we present an automatic image pixel clustering scheme based on mussels wandering optimization. An activation variable is applied to determine the number of clusters automatically with the cluster centers optimization. We revise the within- and between-class sum of squares ratio for random natural image content and develop a novel fitness function for the image pixel clustering task. Our proposed scheme is compared against existing state-of-the-art techniques using both synthetic data and real ASD dataset. Experimental results show the superiority performance of the proposed scheme.