Multi-objective evolutionary fuzzy clustering for image segmentation with MOEA/D

[Display omitted] •The proposed algorithm can preserve image details while removing noise for image segmentation.•Two problem-specific techniques are introduced to achieve well performance for image segmentation.•OBL is used in multi-objective optimization to achieve optimal solutions with a better...

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Bibliographic Details
Published in:Applied soft computing Vol. 48; pp. 621 - 637
Main Authors: Zhang, Mengxuan, Jiao, Licheng, Ma, Wenping, Ma, Jingjing, Gong, Maoguo
Format: Journal Article
Language:English
Published: Elsevier B.V 01.11.2016
Subjects:
Fuzzy clustering
Image segmentation
Local information
Multi-objective evolutionary algorithm with decomposition
Opposition-based learning
Multi-objective evolutionary algorithm with decomposition
Image segmentation
Fuzzy clustering
Local information
Opposition-based learning
ISSN:1568-4946, 1872-9681
Online Access:Get full text
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Summary:[Display omitted] •The proposed algorithm can preserve image details while removing noise for image segmentation.•Two problem-specific techniques are introduced to achieve well performance for image segmentation.•OBL is used in multi-objective optimization to achieve optimal solutions with a better convergence speed. In order to achieve robust performance of preserving significant image details while removing noise for image segmentation, this paper presents a multi-objective evolutionary fuzzy clustering (MOEFC) algorithm to convert fuzzy clustering problems for image segmentation into multi-objective problems. The multi-objective problems are optimized by multi-objective evolutionary algorithm with decomposition. The decomposition strategy is adopted to project the multi-objective problem into a number of sub-problems. Each sub-problem represents a fuzzy clustering problem incorporating local information for image segmentation. Opposition-based learning is utilized to improve search capability of the proposed algorithm. Two problem-specific techniques, an adaptive weighted fuzzy factor and a mixed population initialization, are introduced to improve the performance of the algorithm. Experiment results on synthetic and real images illustrate that the proposed algorithm can achieve a trade-off between preserving image details and removing noise for image segmentation.
ISSN:1568-4946
1872-9681
DOI:10.1016/j.asoc.2016.07.051