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Symmetric cross-entropy multi-threshold color image segmentation based on improved pelican optimization algorithm

To address the problems of low accuracy and slow convergence of traditional multilevel image segmentation methods, a symmetric cross-entropy multilevel thresholding image segmentation method (MSIPOA) with multi-strategy improved pelican optimization algorithm is proposed for global optimization and...

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Autores principales: Zhang, Chuang, Pei, Yue-Han, Wang, Xiao-Xue, Hou, Hong-Yu, Fu, Li-Hua
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10309640/
https://www.ncbi.nlm.nih.gov/pubmed/37384625
http://dx.doi.org/10.1371/journal.pone.0287573
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author Zhang, Chuang
Pei, Yue-Han
Wang, Xiao-Xue
Hou, Hong-Yu
Fu, Li-Hua
author_facet Zhang, Chuang
Pei, Yue-Han
Wang, Xiao-Xue
Hou, Hong-Yu
Fu, Li-Hua
author_sort Zhang, Chuang
collection PubMed
description To address the problems of low accuracy and slow convergence of traditional multilevel image segmentation methods, a symmetric cross-entropy multilevel thresholding image segmentation method (MSIPOA) with multi-strategy improved pelican optimization algorithm is proposed for global optimization and image segmentation tasks. First, Sine chaotic mapping is used to improve the quality and distribution uniformity of the initial population. A spiral search mechanism incorporating a sine cosine optimization algorithm improves the algorithm’s search diversity, local pioneering ability, and convergence accuracy. A levy flight strategy further improves the algorithm’s ability to jump out of local minima. In this paper, 12 benchmark test functions and 8 other newer swarm intelligence algorithms are compared in terms of convergence speed and convergence accuracy to evaluate the performance of the MSIPOA algorithm. By non-parametric statistical analysis, MSIPOA shows a greater superiority over other optimization algorithms. The MSIPOA algorithm is then experimented with symmetric cross-entropy multilevel threshold image segmentation, and eight images from BSDS300 are selected as the test set to evaluate MSIPOA. According to different performance metrics and Fridman test, MSIPOA algorithm outperforms similar algorithms in global optimization and image segmentation, and the symmetric cross entropy of MSIPOA algorithm for multilevel thresholding image segmentation method can be effectively applied to multilevel thresholding image segmentation tasks.
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spelling pubmed-103096402023-06-30 Symmetric cross-entropy multi-threshold color image segmentation based on improved pelican optimization algorithm Zhang, Chuang Pei, Yue-Han Wang, Xiao-Xue Hou, Hong-Yu Fu, Li-Hua PLoS One Research Article To address the problems of low accuracy and slow convergence of traditional multilevel image segmentation methods, a symmetric cross-entropy multilevel thresholding image segmentation method (MSIPOA) with multi-strategy improved pelican optimization algorithm is proposed for global optimization and image segmentation tasks. First, Sine chaotic mapping is used to improve the quality and distribution uniformity of the initial population. A spiral search mechanism incorporating a sine cosine optimization algorithm improves the algorithm’s search diversity, local pioneering ability, and convergence accuracy. A levy flight strategy further improves the algorithm’s ability to jump out of local minima. In this paper, 12 benchmark test functions and 8 other newer swarm intelligence algorithms are compared in terms of convergence speed and convergence accuracy to evaluate the performance of the MSIPOA algorithm. By non-parametric statistical analysis, MSIPOA shows a greater superiority over other optimization algorithms. The MSIPOA algorithm is then experimented with symmetric cross-entropy multilevel threshold image segmentation, and eight images from BSDS300 are selected as the test set to evaluate MSIPOA. According to different performance metrics and Fridman test, MSIPOA algorithm outperforms similar algorithms in global optimization and image segmentation, and the symmetric cross entropy of MSIPOA algorithm for multilevel thresholding image segmentation method can be effectively applied to multilevel thresholding image segmentation tasks. Public Library of Science 2023-06-29 /pmc/articles/PMC10309640/ /pubmed/37384625 http://dx.doi.org/10.1371/journal.pone.0287573 Text en © 2023 Zhang et al https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
spellingShingle Research Article
Zhang, Chuang
Pei, Yue-Han
Wang, Xiao-Xue
Hou, Hong-Yu
Fu, Li-Hua
Symmetric cross-entropy multi-threshold color image segmentation based on improved pelican optimization algorithm
title Symmetric cross-entropy multi-threshold color image segmentation based on improved pelican optimization algorithm
title_full Symmetric cross-entropy multi-threshold color image segmentation based on improved pelican optimization algorithm
title_fullStr Symmetric cross-entropy multi-threshold color image segmentation based on improved pelican optimization algorithm
title_full_unstemmed Symmetric cross-entropy multi-threshold color image segmentation based on improved pelican optimization algorithm
title_short Symmetric cross-entropy multi-threshold color image segmentation based on improved pelican optimization algorithm
title_sort symmetric cross-entropy multi-threshold color image segmentation based on improved pelican optimization algorithm
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10309640/
https://www.ncbi.nlm.nih.gov/pubmed/37384625
http://dx.doi.org/10.1371/journal.pone.0287573
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