控制理论与应用2026，Vol.43Issue(3)：480-490,11.DOI:10.7641/CTA.2024.30821
增强空间信息的快速自适应模糊聚类图像分割算法
Fast adaptive fuzzy clustering image segmentation algorithm with enhanced spatial information
摘要
Abstract
Addressing the inadequate utilization of image spatial information by the fuzzy C-means(FCM)algorithm,leading to poor robustness against noise and increased complexity in the adaptive parameter setting for the objective func-tion,a rapid adaptive fuzzy clustering image segmentation algorithm enhancing spatial information is proposed.Firstly,a novel operation based on enhanced spatial information is defined.This operation combines local and non-local spatial information of the image into the FCM clustering using harmonic coefficients to enhance the algorithm's robustness.Sec-ondly,a method for rapid adaptive parameter setting is proposed,which efficiently assigns adaptive weights to the original image and enhanced spatial information.This will enable the swift adaptive computation of key parameters for the ob-jective function.Finally,sparse regularization is introduced into the FCM objective function,reduced algorithm runtime.Additionally,a three-step iterative algorithm is designed to solve the sparse regularized FCM model,composed of Lagrange multipliers,hard threshold operators,and normalization operators.Experiments on synthetic images and real images from various datasets demonstrate that the proposed algorithm exhibits superior segmentation performance and computational efficiency compared to other algorithms of similar nature under simulated noise conditions.关键词
图像分割/模糊C-均值聚类/增强空间信息/稀疏正则化/自适应参数Key words
image segmentation/fuzzy C-means clustering/enhanced spatial information/sparse regularization/adap-tive parameters引用本文复制引用
吴嘉昕,王小鹏,焦建军,陈浩然..增强空间信息的快速自适应模糊聚类图像分割算法[J].控制理论与应用,2026,43(3):480-490,11.基金项目
国家自然科学基金项目(61761027),兰州市科技计划项目(2023-3-104),甘肃省优秀研究生"创新之星"项目(2023CXZX-510)资助.Supported by the National Natural Science Foundation of China(61761027),the Lanzhou Science and Technology Programme(2023-3-104)and the Gansu Province Excellent Graduate Student"Innovation Star"Project(2023CXZX-510). （61761027）
