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....

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Vydáno v:IEEE access Ročník 7; s. 21465 - 21481
Hlavní autoři: Zhao, Feng, Zeng, Zhe, Liu, Han Qiang, Fan, Jiu Lun
Médium: Journal Article
Jazyk:angličtina
Vydáno: Piscataway IEEE 2019
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Témata:
Approximation algorithms
Clustering
Clustering algorithms
Computing costs
Evolutionary algorithms
Evolutionary computation
fuzzy clustering
Image segmentation
Kriging model
Linear programming
Magnetic resonance imaging
Model accuracy
multi-objective optimization
Multiple objective analysis
Noise
Optimization
reference vector guided evolutionary algorithm
Sociology
Spatial data
Statistics
ISSN:2169-3536, 2169-3536
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Shrnutí: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.
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ISSN:2169-3536
2169-3536
DOI:10.1109/ACCESS.2019.2897597