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A Multimodal Particle Swarm Optimization-based Approach for Image Segmentation

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journal contribution
posted on 15.04.2020, 09:03 by John H. Drake, Taymaz R. Farshi, Ender Özcan
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.

History

Citation

Expert Systems with Applications, Volume 149, 2020, 113233

Author affiliation

Department of Informatics

Version

AM (Accepted Manuscript)

Published in

Expert Systems with Applications

Volume

149

Publisher

Elsevier

issn

0957-4174

Acceptance date

22/01/2020

Copyright date

2020

Available date

27/01/2020

Publisher version

https://www.sciencedirect.com/science/article/abs/pii/S0957417420300592

Language

en