Image segmentation as a clustering problem is to identify pixel groups on an image without any preliminary labels available. It remains a challenge in machine vision because of the variations in size and shape of image segments. Furthermore, determining the segment number in an image is NP-hard without prior knowledge of the image content. This paper presents an automatic color image pixel clustering scheme based on mussels wandering optimization. By applying an activation variable to determine the number of clusters along with the cluster centers optimization, an image is segmented with minimal prior knowledge and human intervention. By revising the within- and between-class sum of squares ratio for random natural image contents, we provide a novel fitness function for image pixel clustering tasks. Comprehensive empirical studies of the proposed scheme against other state-of-the-art competitors on synthetic data and the ASD dataset have demonstrated the promising performance of the proposed scheme.
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