Kernel Space Local Adartive Fuzzy C-means Clustering Image Segmentation

LIANG Dan; YU Hai-yan; FAN Jiu-lun; LUO Xi

Chinese

您当前的位置：

首页 >

文章列表页 >

Kernel Space Local Adartive Fuzzy C-means Clustering Image Segmentation

更新时间：2026-09-04

- Kernel Space Local Adartive Fuzzy C-means Clustering Image Segmentation
- Vol. 36, Issue 2, Pages: 21-25(2019)
- 作者机构：
  
  1. 西安邮电大学机构通信与信息工程学院,陕西,西安,710121
  2. 电子信息勘验应用技术公安部重点实验室,陕西,西安,710121
  3. 陕西省无线通信与信息处理技术国际合作研究中心,陕西,西安,710121
- 作者简介：
- 基金信息：
- DOI：
  CLC：
- Published：2019
- 稿件说明：
移动端阅览
梁丹, 于海燕, 范九伦, 雒僖. 核空间局部自适应模糊C-均值聚类图像分割算法[J]. 微电子学与计算机, 2019, 36(2): 21-25.

LIANG Dan, YU Hai-yan, FAN Jiu-lun, et al. Kernel Space Local Adartive Fuzzy C-means Clustering Image Segmentation[J]. 2019, 36(2): 21-25.
梁丹, 于海燕, 范九伦, 雒僖. 核空间局部自适应模糊C-均值聚类图像分割算法[J]. 微电子学与计算机, 2019, 36(2): 21-25. DOI：

LIANG Dan, YU Hai-yan, FAN Jiu-lun, et al. Kernel Space Local Adartive Fuzzy C-means Clustering Image Segmentation[J]. 2019, 36(2): 21-25. DOI：

摘要

针对传统模糊C-均值聚类算法对在噪声干扰下图像的分割效果不理想问题

提出一种核空间与自适应中值滤波相结合的改进算法.算法利用自适应中值滤波获得像素的局部空间信息

并由此生成一种新的模糊因子加入到目标函数中

然后在核空间中对目标函数进行优化求解

得到最优聚类中心和隶属度.实验结果表明

该算法对被高椒盐噪声污染的图像具有较高的精度和鲁棒性.

Abstract

Aiming at the segmentation effect of traditional fuzzy c-means algorithm on the image under noise interference

an improved algorithm combining the kernel space and adaptive median is proposed. The algorithm used adaptive median filter to obtain the local spatial information of the pixel

and then generates a new fuzzy factor to add to the objective function. Finally

the objective function is optimized and solved in kernel space to obtain the optimal cluster center and membership degree. Experimental results show that the algorithm has high accuracy and robustness to images polluted by high salt and pepper noise.

关键词

Keywords

references

冈萨雷斯.数字图像处理[M].北京:电子工业出版社, 2006.

Chen S, Zhan D. Robust image segmentation using FCM with spatial constraints based on new kernel-induced distance measure[J]. IEEE Trans. Syst. Man, Cybern. B, Cybern. 2004, 34(4): 1907-1916. DOI: 1907

Krinidis S, Chatzis V. A robustf fuzzy local information c-means clustering algorithm[J]. IEEE Transactions on Image Processing, 2010, 19(5): 1328-1337. DOI: 1328

Kwan R K S, Evans A C, Pike G B. MRI simulation-based evaluation of image-processing and classification methods[J]. IEEE Transactions on Medical Imaging, 1999, 18(11): 1085-1097. DOI: 1085

Fang R, Lu Y, Liu X, et al. Segmentation of brain MR images using an adaptively regularized kernel FCM algorithm with spatial constraints[C]. 2017 10th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics (CISP-BMEI), Shanghai, 2017: 1-6.

Xie X L, Beni G. A validity measure for fuzzy clustering[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 1991, 13(8): 841-847. DOI: 841

朱占龙, 郑一博, 王远.模糊聚类图像分割后处理[J].微电子学与计算机, 2018, 35(03):68-73. DOI: 68

朱占龙.融入邻域距离与隶属度模糊聚类图像分割算法[J].微电子学与计算机, 2018, 35(04):93-97. DOI: 93

朱红霞, 王黎明.改进的自适应模糊C均值聚类图像分割算法[J].微电子学与计算机, 2010, 27(12):87-89. DOI: 87

Views

下载量

CSCD

Alert me when the article has been cited

提交

Tools

Publicity Resources

Microservices anomaly detection based on the correlation of monitoring metrics

Aerial small object detection algorithm based on improved RT-DETR

Aerial vehicle detection algorithm based on multi-scale fusion and small-object enhancement

Monte carlo analysis of V-layout trench SiC neutron detector

Design of ultra-low power real-time clock circuits

Related Author

No data

Related Institution

No data

AI问答

Postal code：100079
Tel：（010）53879206 Email：tmw@bjxintong.com.cn
Technical support is provided by Beijing Founder electronics co., LTD 京ICP备09082226号-64 京公网安备11010602201714号
It is recommended to read the content of this site in Chrome&IE9+. Please switch to extreme mode in browser 360.
Cookies We use cookies to help provide and enhance our service and tailor content. By continuing, you agree to the use of cookies.

⁰