Automatic Image Pixel Clustering based on Mussels Wandering Optimiz

TL;DR

This paper introduces an automatic image pixel clustering method using mussels wandering optimization, effectively segmenting images with minimal prior knowledge and outperforming existing methods on synthetic and real datasets.

Contribution

It proposes a novel clustering scheme that automatically determines the number of segments and optimizes cluster centers using a new fitness function based on sum of squares ratios.

Findings

Demonstrates superior segmentation accuracy on synthetic data.

Achieves promising results on ASD dataset.

Reduces need for prior knowledge and human intervention.

Abstract

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.