Multi-objective evolutionary fuzzy clustering for image segmentation with MOEA/D
Multi-objective evolutionary fuzzy clustering for image segmentation with MOEA/D
复制标题
使用 MOEA/D 进行图像分割的多目标进化模糊聚类
DOI：
10.1016/j.asoc.2016.07.051
复制
发表时间：
2016
影响因子：
8.7
通讯作者：
Gong Maoguo
中科院分区：
文献类型：
--
作者：
Zhang Mengxuan;Jiao Licheng;Ma Wenping;Ma Jingjing;Gong Maoguo
为了在去除噪声的同时保持图像分割的鲁棒性，提出了一种多目标进化模糊聚类（MOEFC）算法，将图像分割的模糊聚类问题转化为多目标问题。多目标优化问题采用多目标分解进化算法。采用分解策略将多目标问题分解为若干个子问题。每个子问题代表了一个模糊聚类问题，结合图像分割的局部信息。基于反对的学习被用来提高所提出的算法的搜索能力。两个特定问题的技术，自适应加权模糊因子和混合人口初始化，引入到提高算法的性能。在合成图像和真实的图像上的实验结果表明，该算法能够在图像分割中兼顾图像细节和噪声的去除。
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.