吉林大学学报（理学版）Issue(4)：680-686,7.DOI:10.13413/j.cnki.jdxblxb.2015.04.17
改进遗传算法优化模糊均值聚类中心的图像分割
Image Segmentation Based on Improved Genetic Algorithm Optimizing Fuzzy Means Clustering Center
摘要
Abstract
In order to improve the image segmentation accuracy,in view of the problems in the traditional fuzzy clustering algorithm,the author proposed an image segmentation algorithm based on improved genetic algorithm optimizing fuzzy means clustering center.First of all,the direction factor was introduced into the crossover operation of standard genetic algorithm to make individual in cross approach to the best individual so as to accelerate the convergence speed,and inter group information sharing mechanism was enhanced to improve the algorithm’s global search capability and avoid the premature convergence so as to improve the accuracy of global solution.Then the initial cluster centers of fuzzy k-means clustering algorithm were selected by improved genetic algorithm to realize image segmentation. Finally the performance was tested by simulation experiments. The experimental results show that compared with the traditional fuzzy C-means clustering algorithm and other images segmentation algorithm,the proposed algorithm is better in segmentation accuracy rate, the segmentation speed and robustness.关键词
图像分割/模糊均值聚类算法/遗传算法/引向因子/信息共享Key words
image segmentation/fuzzy means clustering algorithm/genetic algorithm/orientation factor/information sharing分类
信息技术与安全科学引用本文复制引用
董倩..改进遗传算法优化模糊均值聚类中心的图像分割[J].吉林大学学报（理学版）,2015,(4):680-686,7.基金项目
河北省教育厅科研基金(批准号:13JY0138)和石家庄市科学技术研究与发展计划项目(批准号:131130452A) （批准号:13JY0138）
