Modified dragonfly algorithm based multilevel thresholding method for color images segmentation
Accurate image segmentation is the preprocessing step of image processing. Multi-level threshold segmentation has important research value in image segmentation, which can effectively solve the problem of region analysis of complex images, but the computational complexity increases accordingly. In o...
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| Format: | Article |
| Language: | English |
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AIMS Press
2019-07-01
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| Series: | Mathematical Biosciences and Engineering |
| Subjects: | |
| Online Access: | https://www.aimspress.com/article/10.3934/mbe.2019324?viewType=HTML |
| _version_ | 1818214531029008384 |
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| author | Xiaoxu Peng Heming Jia Chunbo Lang |
| author_facet | Xiaoxu Peng Heming Jia Chunbo Lang |
| author_sort | Xiaoxu Peng |
| collection | DOAJ |
| description | Accurate image segmentation is the preprocessing step of image processing. Multi-level threshold segmentation has important research value in image segmentation, which can effectively solve the problem of region analysis of complex images, but the computational complexity increases accordingly. In order to overcome this problem, an modified Dragonfly algorithm (MDA) is proposed to determine the optimal combination of different levels of thresholds for color images. Chaotic mapping and elite opposition-based learning strategies (EOBL) are used to improve the randomness of the initial population. The hybrid algorithm of Dragonfly Algorithms (DA) and Differential Evolution (DE) is used to balance the two basic stages of optimization: exploration and development. Kapur entropy, minimum cross-entropy and Otsu method are used as fitness functions of image segmentation. The performance of 10 test color images is evaluated and compared with 9 different meta-heuristic algorithms. The results show that the color image segmentation method based on MDA is more effective and accurate than other competitors in average fitness value (AF), standard deviation (STD), peak signal-to-noise ratio (PSNR), structural similarity index (SSIM) and feature similarity index (FSIM). Friedman test and Wilcoxon's rank sum test are also performed to assess the significant difference between the algorithms. |
| first_indexed | 2024-12-12T06:21:39Z |
| format | Article |
| id | doaj.art-2199d62240684e04a555e8602ad59faa |
| institution | Directory Open Access Journal |
| issn | 1551-0018 |
| language | English |
| last_indexed | 2024-12-12T06:21:39Z |
| publishDate | 2019-07-01 |
| publisher | AIMS Press |
| record_format | Article |
| series | Mathematical Biosciences and Engineering |
| spelling | doaj.art-2199d62240684e04a555e8602ad59faa2022-12-22T00:34:53ZengAIMS PressMathematical Biosciences and Engineering1551-00182019-07-011666467651110.3934/mbe.2019324Modified dragonfly algorithm based multilevel thresholding method for color images segmentationXiaoxu Peng0Heming Jia1Chunbo Lang2College 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, ChinaAccurate image segmentation is the preprocessing step of image processing. Multi-level threshold segmentation has important research value in image segmentation, which can effectively solve the problem of region analysis of complex images, but the computational complexity increases accordingly. In order to overcome this problem, an modified Dragonfly algorithm (MDA) is proposed to determine the optimal combination of different levels of thresholds for color images. Chaotic mapping and elite opposition-based learning strategies (EOBL) are used to improve the randomness of the initial population. The hybrid algorithm of Dragonfly Algorithms (DA) and Differential Evolution (DE) is used to balance the two basic stages of optimization: exploration and development. Kapur entropy, minimum cross-entropy and Otsu method are used as fitness functions of image segmentation. The performance of 10 test color images is evaluated and compared with 9 different meta-heuristic algorithms. The results show that the color image segmentation method based on MDA is more effective and accurate than other competitors in average fitness value (AF), standard deviation (STD), peak signal-to-noise ratio (PSNR), structural similarity index (SSIM) and feature similarity index (FSIM). Friedman test and Wilcoxon's rank sum test are also performed to assess the significant difference between the algorithms.https://www.aimspress.com/article/10.3934/mbe.2019324?viewType=HTMLdragonfly algorithmmultilevel thresholdingkapur's entropyminimum cross entropyotsu methodelite opposition-based learningdifferential evolution |
| spellingShingle | Xiaoxu Peng Heming Jia Chunbo Lang Modified dragonfly algorithm based multilevel thresholding method for color images segmentation Mathematical Biosciences and Engineering dragonfly algorithm multilevel thresholding kapur's entropy minimum cross entropy otsu method elite opposition-based learning differential evolution |
| title | Modified dragonfly algorithm based multilevel thresholding method for color images segmentation |
| title_full | Modified dragonfly algorithm based multilevel thresholding method for color images segmentation |
| title_fullStr | Modified dragonfly algorithm based multilevel thresholding method for color images segmentation |
| title_full_unstemmed | Modified dragonfly algorithm based multilevel thresholding method for color images segmentation |
| title_short | Modified dragonfly algorithm based multilevel thresholding method for color images segmentation |
| title_sort | modified dragonfly algorithm based multilevel thresholding method for color images segmentation |
| topic | dragonfly algorithm multilevel thresholding kapur's entropy minimum cross entropy otsu method elite opposition-based learning differential evolution |
| url | https://www.aimspress.com/article/10.3934/mbe.2019324?viewType=HTML |
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