A Chaotic Electromagnetic Field Optimization Algorithm Based on Fuzzy Entropy for Multilevel Thresholding Color Image Segmentation
Multilevel thresholding segmentation of color images is an important technology in various applications which has received more attention in recent years. The process of determining the optimal threshold values in the case of traditional methods is time-consuming. In order to mitigate the above prob...
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MDPI AG
2019-04-01
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Online Access: | https://www.mdpi.com/1099-4300/21/4/398 |
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author | Suhang Song Heming Jia Jun Ma |
author_facet | Suhang Song Heming Jia Jun Ma |
author_sort | Suhang Song |
collection | DOAJ |
description | Multilevel thresholding segmentation of color images is an important technology in various applications which has received more attention in recent years. The process of determining the optimal threshold values in the case of traditional methods is time-consuming. In order to mitigate the above problem, meta-heuristic algorithms have been employed in this field for searching the optima during the past few years. In this paper, an effective technique of Electromagnetic Field Optimization (EFO) algorithm based on a fuzzy entropy criterion is proposed, and in addition, a novel chaotic strategy is embedded into EFO to develop a new algorithm named CEFO. To evaluate the robustness of the proposed algorithm, other competitive algorithms such as Artificial Bee Colony (ABC), Bat Algorithm (BA), Wind Driven Optimization (WDO), and Bird Swarm Algorithm (BSA) are compared using fuzzy entropy as the fitness function. Furthermore, the proposed segmentation method is also compared with the most widely used approaches of Otsu’s variance and Kapur’s entropy to verify its segmentation accuracy and efficiency. Experiments are conducted on ten Berkeley benchmark images and the simulation results are presented in terms of peak signal to noise ratio (PSNR), mean structural similarity (MSSIM), feature similarity (FSIM), and computational time (CPU Time) at different threshold levels of 4, 6, 8, and 10 for each test image. A series of experiments can significantly demonstrate the superior performance of the proposed technique, which can deal with multilevel thresholding color image segmentation excellently. |
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issn | 1099-4300 |
language | English |
last_indexed | 2024-04-11T22:51:20Z |
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spelling | doaj.art-f77bce0cdca3496195cd25f1abd5f81f2022-12-22T03:58:35ZengMDPI AGEntropy1099-43002019-04-0121439810.3390/e21040398e21040398A Chaotic Electromagnetic Field Optimization Algorithm Based on Fuzzy Entropy for Multilevel Thresholding Color Image SegmentationSuhang Song0Heming Jia1Jun Ma2College of Mechanical and Electrical Engineering, Northeast Forestry University, Harbin 150040, ChinaCollege of Mechanical and Electrical Engineering, Northeast Forestry University, Harbin 150040, ChinaCollege of Mechanical and Electrical Engineering, Northeast Forestry University, Harbin 150040, ChinaMultilevel thresholding segmentation of color images is an important technology in various applications which has received more attention in recent years. The process of determining the optimal threshold values in the case of traditional methods is time-consuming. In order to mitigate the above problem, meta-heuristic algorithms have been employed in this field for searching the optima during the past few years. In this paper, an effective technique of Electromagnetic Field Optimization (EFO) algorithm based on a fuzzy entropy criterion is proposed, and in addition, a novel chaotic strategy is embedded into EFO to develop a new algorithm named CEFO. To evaluate the robustness of the proposed algorithm, other competitive algorithms such as Artificial Bee Colony (ABC), Bat Algorithm (BA), Wind Driven Optimization (WDO), and Bird Swarm Algorithm (BSA) are compared using fuzzy entropy as the fitness function. Furthermore, the proposed segmentation method is also compared with the most widely used approaches of Otsu’s variance and Kapur’s entropy to verify its segmentation accuracy and efficiency. Experiments are conducted on ten Berkeley benchmark images and the simulation results are presented in terms of peak signal to noise ratio (PSNR), mean structural similarity (MSSIM), feature similarity (FSIM), and computational time (CPU Time) at different threshold levels of 4, 6, 8, and 10 for each test image. A series of experiments can significantly demonstrate the superior performance of the proposed technique, which can deal with multilevel thresholding color image segmentation excellently.https://www.mdpi.com/1099-4300/21/4/398fuzzy entropyelectromagnetic field optimizationchaotic strategycolor image segmentationmultilevel thresholding |
spellingShingle | Suhang Song Heming Jia Jun Ma A Chaotic Electromagnetic Field Optimization Algorithm Based on Fuzzy Entropy for Multilevel Thresholding Color Image Segmentation Entropy fuzzy entropy electromagnetic field optimization chaotic strategy color image segmentation multilevel thresholding |
title | A Chaotic Electromagnetic Field Optimization Algorithm Based on Fuzzy Entropy for Multilevel Thresholding Color Image Segmentation |
title_full | A Chaotic Electromagnetic Field Optimization Algorithm Based on Fuzzy Entropy for Multilevel Thresholding Color Image Segmentation |
title_fullStr | A Chaotic Electromagnetic Field Optimization Algorithm Based on Fuzzy Entropy for Multilevel Thresholding Color Image Segmentation |
title_full_unstemmed | A Chaotic Electromagnetic Field Optimization Algorithm Based on Fuzzy Entropy for Multilevel Thresholding Color Image Segmentation |
title_short | A Chaotic Electromagnetic Field Optimization Algorithm Based on Fuzzy Entropy for Multilevel Thresholding Color Image Segmentation |
title_sort | chaotic electromagnetic field optimization algorithm based on fuzzy entropy for multilevel thresholding color image segmentation |
topic | fuzzy entropy electromagnetic field optimization chaotic strategy color image segmentation multilevel thresholding |
url | https://www.mdpi.com/1099-4300/21/4/398 |
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