A Global Multilevel Thresholding Using Differential Evolution Approach

Otsu’s function measures the properness of threshold values in multilevel image thresholding. Optimal threshold values are necessary for some applications and a global search algorithm is required. Differential evolution (DE) is an algorithm that has been used successfully for solving this problem....

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Main Authors: Kanjana Charansiriphaisan, Sirapat Chiewchanwattana, Khamron Sunat
Format: Article
Language:English
Published: Hindawi Limited 2014-01-01
Series:Mathematical Problems in Engineering
Online Access:http://dx.doi.org/10.1155/2014/974024
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spelling doaj-9c07b802e39a412c9404aa71562629ee2020-11-25T00:33:46ZengHindawi LimitedMathematical Problems in Engineering1024-123X1563-51472014-01-01201410.1155/2014/974024974024A Global Multilevel Thresholding Using Differential Evolution ApproachKanjana Charansiriphaisan0Sirapat Chiewchanwattana1Khamron Sunat2Department of Computer Science, Faculty of Science, Khon Kaen University, Khon Kaen 40002, ThailandDepartment of Computer Science, Faculty of Science, Khon Kaen University, Khon Kaen 40002, ThailandDepartment of Computer Science, Faculty of Science, Khon Kaen University, Khon Kaen 40002, ThailandOtsu’s function measures the properness of threshold values in multilevel image thresholding. Optimal threshold values are necessary for some applications and a global search algorithm is required. Differential evolution (DE) is an algorithm that has been used successfully for solving this problem. Because the difficulty of a problem grows exponentially when the number of thresholds increases, the ordinary DE fails when the number of thresholds is greater than 12. An improved DE, using a new mutation strategy, is proposed to overcome this problem. Experiments were conducted on 20 real images and the number of thresholds varied from 2 to 16. Existing global optimization algorithms were compared with the proposed algorithms, that is, DE, rank-DE, artificial bee colony (ABC), particle swarm optimization (PSO), DPSO, and FODPSO. The experimental results show that the proposed algorithm not only achieves a more successful rate but also yields a lower threshold value distortion than its competitors in the search for optimal threshold values, especially when the number of thresholds is large.http://dx.doi.org/10.1155/2014/974024
collection DOAJ
language English
format Article
sources DOAJ
author Kanjana Charansiriphaisan
Sirapat Chiewchanwattana
Khamron Sunat
spellingShingle Kanjana Charansiriphaisan
Sirapat Chiewchanwattana
Khamron Sunat
A Global Multilevel Thresholding Using Differential Evolution Approach
Mathematical Problems in Engineering
author_facet Kanjana Charansiriphaisan
Sirapat Chiewchanwattana
Khamron Sunat
author_sort Kanjana Charansiriphaisan
title A Global Multilevel Thresholding Using Differential Evolution Approach
title_short A Global Multilevel Thresholding Using Differential Evolution Approach
title_full A Global Multilevel Thresholding Using Differential Evolution Approach
title_fullStr A Global Multilevel Thresholding Using Differential Evolution Approach
title_full_unstemmed A Global Multilevel Thresholding Using Differential Evolution Approach
title_sort global multilevel thresholding using differential evolution approach
publisher Hindawi Limited
series Mathematical Problems in Engineering
issn 1024-123X
1563-5147
publishDate 2014-01-01
description Otsu’s function measures the properness of threshold values in multilevel image thresholding. Optimal threshold values are necessary for some applications and a global search algorithm is required. Differential evolution (DE) is an algorithm that has been used successfully for solving this problem. Because the difficulty of a problem grows exponentially when the number of thresholds increases, the ordinary DE fails when the number of thresholds is greater than 12. An improved DE, using a new mutation strategy, is proposed to overcome this problem. Experiments were conducted on 20 real images and the number of thresholds varied from 2 to 16. Existing global optimization algorithms were compared with the proposed algorithms, that is, DE, rank-DE, artificial bee colony (ABC), particle swarm optimization (PSO), DPSO, and FODPSO. The experimental results show that the proposed algorithm not only achieves a more successful rate but also yields a lower threshold value distortion than its competitors in the search for optimal threshold values, especially when the number of thresholds is large.
url http://dx.doi.org/10.1155/2014/974024
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