A Multimodal Particle Swarm Optimization-based Approach for Image Segmentation

Abstract

Color image segmentation is a fundamental challenge in the field of image analysis and pattern recognition. In this paper, a novel automated pixel clustering and color image segmentation algorithm is presented. The proposed method operates in three successive stages. In the first stage, a three-dimensional histogram of pixel colors based on the RGB model is smoothened using a Gaussian filter. This process helps to eliminate unreliable and non-dominating peaks that are too close to one another in the histogram. In the next stage, the peaks representing different clusters in the histogram are identified using a multimodal particle swarm optimization algorithm. Finally, pixels are assigned to the most appropriate cluster based on Euclidean distance. Determining the number of clusters to be used is often a manual process left for a user and represents a challenge for various segmentation algorithms. The proposed method is designed to determine an appropriate number of clusters, in addition to the actual peaks, automatically. Experiments confirm that the proposed approach yields desirable results, demonstrating that it can find an appropriate set of clusters for a set of well-known benchmark images.

Citation

Farshi, T. R., Drake, J. H., & Özcan, E. (2020). A Multimodal Particle Swarm Optimization-based Approach for Image Segmentation. Expert Systems with Applications, 149, Article 113233. https://doi.org/10.1016/j.eswa.2020.113233

• Journal Article Type: Article
• Acceptance Date: Jan 22, 2020
• Online Publication Date: Jan 27, 2020
• Publication Date: Jul 1, 2020
• Deposit Date: Feb 25, 2020
• Publicly Available Date: Jan 28, 2021
• Journal: Expert Systems with Applications
• Print ISSN: 0957-4174
• Publisher: Elsevier
• Peer Reviewed: Peer Reviewed
• Volume: 149
• Article Number: 113233
• DOI: https://doi.org/10.1016/j.eswa.2020.113233
• Keywords: Color image segmentation; Clustering; Particle Swarm Optimisation; Multimodal optimisation
• Public URL: https://nottingham-repository.worktribe.com/output/3842163
• Publisher URL: https://www.sciencedirect.com/science/article/pii/S0957417420300592