摘要
将基于模拟退火算法的改进模糊C均值聚类应用到储粮害虫图像分割中.针对模糊C均值聚类算法中计算量大和易陷入局部最优的问题,引入模拟退火算法,开始时以较快的速度找到最优区域,最终找到全局最优解,可以提高收敛速度和计算的精度.Matlab仿真实验表明该算法在储粮害虫图像分割中比模糊C均值聚类更有效.
The improved fuzzy C-means clustering based on simulated annealing algorithm was applied to the stored-grain image segmentation. The computation load which exists in view of the fuzzy C- means value cluster algorithm in big and easy to fall into was partially most superior, the introduction simulation annealing algorithm, this algorithm starts when found the most superior region by the quick speed, found the globally optimal solution finally, might raise the convergence rate and the computation precision. The Matlab simulation experiments demonstrated that this method is more effective than fuzzy C-means clustering.
出处
《华中科技大学学报（自然科学版）》
EI
CAS
CSCD
北大核心
2010年第5期72-74,共3页
Journal of Huazhong University of Science and Technology(Natural Science Edition)
基金
湖北省自然科学基金资助项目(2003ABA053)
武汉市青年科技晨光计划资助项目(20035002016-09)
湖北省教育厅青年基金资助项目(Q200618002)
关键词
图像分割
粮虫图像
模拟退火算法
模糊C均值聚类
局部最优
image segmentation
stored-grain image
simulated annealing algorithm
fuzzy C-means clustering
partially nost superior
