A Kriging-Assisted Reference Vector Guided Multi-Objective Evolutionary Fuzzy Clustering Algorithm for Image Segmentation
In order to reduce the computational complexity of multi-objective evolutionary optimization-based clustering algorithms, a Kriging-assisted reference vector guided multi-objective robust spatial fuzzy clustering algorithm (KRV-MRSFC) is proposed and then successfully applied to image segmentation....
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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IEEE
2019-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/8635556/ |
| _version_ | 1828885048206557184 |
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| author | Feng Zhao Zhe Zeng Han Qiang Liu Jiu Lun Fan |
| author_facet | Feng Zhao Zhe Zeng Han Qiang Liu Jiu Lun Fan |
| author_sort | Feng Zhao |
| collection | DOAJ |
| description | In order to reduce the computational complexity of multi-objective evolutionary optimization-based clustering algorithms, a Kriging-assisted reference vector guided multi-objective robust spatial fuzzy clustering algorithm (KRV-MRSFC) is proposed and then successfully applied to image segmentation. We first construct objective functions with noise robust local spatial information derived from the image to improve the robustness to noise and then use the Kriging model to approximate each objective function to decrease the computational cost. Meanwhile, in order to improve the approximation accuracy of the Kriging model, an angle-penalized distance-based expected improvement sampling criterion is presented in the KRV-MRSFC, which can select individuals with better exploitation and exploration to update the Kriging model. In addition, KRV-MRSFC adopts a clustering validity index with noise robust local image spatial information to select the optimal solution from the final non-dominated solution set to perform image segmentation. The experiments performed on Berkeley and real magnetic resonance images indicate that the proposed method not only achieves satisfactory segmentation performance on noisy images but also requires a low time cost. |
| first_indexed | 2024-12-13T11:19:26Z |
| format | Article |
| id | doaj.art-30593ee771474fc2b7a885fcd21e0fab |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-12-13T11:19:26Z |
| publishDate | 2019-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-30593ee771474fc2b7a885fcd21e0fab2022-12-21T23:48:31ZengIEEEIEEE Access2169-35362019-01-017214652148110.1109/ACCESS.2019.28975978635556A Kriging-Assisted Reference Vector Guided Multi-Objective Evolutionary Fuzzy Clustering Algorithm for Image SegmentationFeng Zhao0https://orcid.org/0000-0002-0323-9573Zhe Zeng1Han Qiang Liu2Jiu Lun Fan3Key Laboratory of Electronic Information Application Technology for Scene Investigation, Ministry of Public Security, Xi’an University of Posts and Telecommunications, Xi’an, ChinaKey Laboratory of Electronic Information Application Technology for Scene Investigation, Ministry of Public Security, Xi’an University of Posts and Telecommunications, Xi’an, ChinaSchool of Computer Science, Shaanxi Normal University, Xi’an, ChinaKey Laboratory of Electronic Information Application Technology for Scene Investigation, Ministry of Public Security, Xi’an University of Posts and Telecommunications, Xi’an, ChinaIn order to reduce the computational complexity of multi-objective evolutionary optimization-based clustering algorithms, a Kriging-assisted reference vector guided multi-objective robust spatial fuzzy clustering algorithm (KRV-MRSFC) is proposed and then successfully applied to image segmentation. We first construct objective functions with noise robust local spatial information derived from the image to improve the robustness to noise and then use the Kriging model to approximate each objective function to decrease the computational cost. Meanwhile, in order to improve the approximation accuracy of the Kriging model, an angle-penalized distance-based expected improvement sampling criterion is presented in the KRV-MRSFC, which can select individuals with better exploitation and exploration to update the Kriging model. In addition, KRV-MRSFC adopts a clustering validity index with noise robust local image spatial information to select the optimal solution from the final non-dominated solution set to perform image segmentation. The experiments performed on Berkeley and real magnetic resonance images indicate that the proposed method not only achieves satisfactory segmentation performance on noisy images but also requires a low time cost.https://ieeexplore.ieee.org/document/8635556/Image segmentationmulti-objective optimizationfuzzy clusteringKriging modelreference vector guided evolutionary algorithm |
| spellingShingle | Feng Zhao Zhe Zeng Han Qiang Liu Jiu Lun Fan A Kriging-Assisted Reference Vector Guided Multi-Objective Evolutionary Fuzzy Clustering Algorithm for Image Segmentation IEEE Access Image segmentation multi-objective optimization fuzzy clustering Kriging model reference vector guided evolutionary algorithm |
| title | A Kriging-Assisted Reference Vector Guided Multi-Objective Evolutionary Fuzzy Clustering Algorithm for Image Segmentation |
| title_full | A Kriging-Assisted Reference Vector Guided Multi-Objective Evolutionary Fuzzy Clustering Algorithm for Image Segmentation |
| title_fullStr | A Kriging-Assisted Reference Vector Guided Multi-Objective Evolutionary Fuzzy Clustering Algorithm for Image Segmentation |
| title_full_unstemmed | A Kriging-Assisted Reference Vector Guided Multi-Objective Evolutionary Fuzzy Clustering Algorithm for Image Segmentation |
| title_short | A Kriging-Assisted Reference Vector Guided Multi-Objective Evolutionary Fuzzy Clustering Algorithm for Image Segmentation |
| title_sort | kriging assisted reference vector guided multi objective evolutionary fuzzy clustering algorithm for image segmentation |
| topic | Image segmentation multi-objective optimization fuzzy clustering Kriging model reference vector guided evolutionary algorithm |
| url | https://ieeexplore.ieee.org/document/8635556/ |
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