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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Series: | Mathematical Problems in Engineering |
Online Access: | http://dx.doi.org/10.1155/2014/974024 |
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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 |
work_keys_str_mv |
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