摘要
快速广义模糊C均值聚类(FGFCM)在对高噪声图像进行聚类分割时,噪声容易导致聚类中心发生偏移,影响图像分割结果.为此,文中提出基于自适应滤波的快速广义模糊C均值聚类算法,用于图像分割.首先根据非局部像素的噪声概率自适应确定参数平衡因子,更准确地反映图像包含的空间结构信息.然后利用该平衡因子有效结合FGFCM中的线性加权和滤波图像与原始图像的中值滤波图像,由于得到的自适应滤波图像根据图像中像素为噪声的概率自适应确定滤波程度,因此可以提高算法对噪声的动态抑制能力.实验表明,相比模糊C均值聚类和FGFCM,文中算法在对噪声含量较高的图像进行聚类分割时,可以得到更准确的结果.
When the fast generalized fuzzy C-means clustering(FGFCM)is directly used to segment serious noise images,the clustering center offset,inaccurate results and error of the image segmentation are easily caused due to the noise.Therefore,a fast generalized fuzzy C-means clustering algorithm based on adaptive filtering,is proposed.Firstly,the parameter balance factor is adaptively determined according to the noise probability of nonlocal pixels to reflect the spatial structure information in the image more accurately.Then,the balance factor is used to effectively combine the linear weighted sum filtered image in the FGFCM algorithm with the median filtered image of the original image to create the adaptive filtered image.Since the filtering degree of the filtered image depends on the probability that the pixel is noise in the image,the dynamic noise suppression performance of the proposed method can be greatly improved. The experimental results show that compared with FCM and FGFCM,the proposed method obtains more accurate results in clustering segmentation of images with serious noise.
作者
王小鹏
张永芳
王伟
文昊天
WANG Xiaopeng;ZHANG Yongfang;WANG Wei;WEN Haotian(School of Electronic and Information Engineering,Lanzhou Jiaotong University,Lanzhou 730070)
出处
《模式识别与人工智能》
EI
CSCD
北大核心
2018年第11期1040-1046,共7页
Pattern Recognition and Artificial Intelligence
基金
国家自然科学基金项目(No.61761027)资助~~
关键词
模糊C均值聚类
图像分割
自适应滤波
图像噪声
Fuzzy C-means Clustering
Image Segmentation
Adaptive Filtering
Image Noise
