赵甜雨1,
冯国政1,
欧世峰2
1.烟台大学计算机与控制工程学院 烟台 264005
2.烟台大学光电信息科学与技术学院 烟台 264005
基金项目:国家自然科学基金(62072391, 62066013)，山东省自然科学基金(ZR2019MF060, ZR2017MF008)，山东省高等教育科学技术重点计划(J18KZ016)，烟台市科技计划(2018YT06000271)
详细信息
作者简介:徐金东：男，1980年生，副教授，硕士生导师，研究方向为图像处理、模式识别、盲源分离
赵甜雨：女，1996年生，硕士生，研究方向为图像聚类、计算机视觉、模式识别和人工智能
冯国政：男，1996年生，博士生，研究方向为图像分类、模式识别和机器学习
欧世峰：男，1979年生，教授，硕士生导师，研究方向为信号处理、盲信号分析
通讯作者:徐金东 jindong.xu@nlpr.ia.ac.cn
中图分类号:TN911.73计量
文章访问数:254
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PDF下载量:60
被引次数:0
出版历程
收稿日期:2020-04-10
修回日期:2020-10-23
网络出版日期:2021-03-30
刊出日期:2021-07-10
Image Segmentation Algorithm Based on Context Fuzzy C-Means Clustering
Jindong XU1,,,Tianyu ZHAO1,
Guozheng FENG1,
Shifeng OU2
1. School of Computer and Control Engineering, Yantai University, Yantai 264005, China
2. School of Opto-Electronic Information Science and Technology, Yantai University, Yantai 264005, China
Funds:The National Natural Science Foundation of China (62072391, 62066013), The Natural Science Foundation of Shandong Province (ZR2019MF060, ZR2017MF008), The Project of Shandong Province Higher Educational Science and Technology Key Program (J18KZ016), The Yantai Science and Technology Plan (2018YT06000271)
摘要
摘要:像素间的上下文相关信息对图像分割算法的抗噪性和准确性具有重要意义，现有的模糊C均值(FCM)聚类算法对此缺乏充分考虑。该文基于对空间上下文的可靠性度量，提出一种模糊C均值聚类算法(RSFCM)应用于图像分割：通过对空间上下文有效建模来提高聚类算法的抗噪声干扰性能，并研究了一种新的可靠性模糊度量指标，使聚类算法能更好地平衡细节保留和去噪，从而获得更加准确的分割结果。实验选取人工合成图像、交通标志图像和遥感图像3类数据测试聚类算法性能，结果表明，RSFCM在图像分割过程中能有效地抑制椒盐噪声和高斯噪声引起的类内异构及类间同构问题，能提高图像的像素可分性，并有效地保留了图像的边缘细节。
关键词:图像分割/
聚类/
模糊C均值/
空间上下文
Abstract:The correlation information between pixels is of great significance for image segmentation. The existing Fuzzy C-Means (FCM) clustering algorithm lacks sufficient consideration for it. Based on the reliability measure of spatial context, this paper proposes a Reliability-based Spatial context Fuzzy C-Means (RSFCM) clustering algorithm: The clustering algorithm anti-noise performance is improved by effectively modeling the spatial neighborhood; A new reliability fuzzy metric is proposed, which balances the relationship between detail retention and anti-noise, so that the clustering results are more accurate. A synthetic image, a traffic sign image and a remote sensing image are used to test the algorithms performance. The results show, compared with the existing FCM algorithm, RSFCM can effectively suppress heterogeneity of intra-class objects caused by Salt & Pepper noise and Gaussian noise for the image segmentation, improve pixels separability and preserve the edge details of the image greatly.
Key words:Image segmentation/
Clustering/
Fuzzy C-Means (FCM)/
Spatial context
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https://jeit.ac.cn/article/exportPdf?id=d50f9fe4-cc0c-49e6-9861-8985f0d06bb1
