摘要
针对传统的模糊C均值聚类算法在进行图像分割时对孤立点、噪声点敏感性较强,聚类耗时随图像变大而快速增长等缺陷,基于临近元素空间距离的模糊C均值聚类算法即SFGFCM算法,采用核化的空间距离公式,计算出空间临近像素与考察像素的相似度Sij,然后用邻近像素灰度加权和计算出邻近信息制约图像,并进一步在邻近信息制约图像的灰度级统计的基础上进行聚类。该算法考察了临近像素灰度和位置等信息,并且它们之间取得了很好的平衡;不仅表现出较强的鲁棒性且很好地保留了原图像边缘等细节信息,提高了聚类精度,同时大大缩短了大幅图像的聚类时间。通过在合成图像、医学图像及自然图像上的大量实验,与传统算法对比该算法聚类性能明显提高,在图像分割上体现出了较好的分割效果。
To deal with the traditional image segmentation algorithms＇ sensitive to noises and outliers, and the segmenting time increasing with the image size, the fast and Fuzzy C-Means clustering algorithm based on the space distance of near- est neighbors （SFGFCM） uses a nuclear space distance formula to calculate the similarity measure So between the query pixel and its nearest neighbors, further more to calculate the local information constrains image with the adjacent pixels weighted sum, at last sum the local information image＇ gray levels and local information, provides robustness s grey level to cluster, So that it well balances the query pixel＇ s to noisy images and guaranteed image details presentation, im- proves the accuracy of clustering, at the same time reduces the clustering time remarkably for the large size images. Exper- iments performed on a large number of synthetic and real-world images show that SFGFCM is more effective and effi- cient in contrast to traditional algorithm on image segmentation
出处
《计算机工程与应用》
CSCD
北大核心
2015年第1期177-183,188,共8页
Computer Engineering and Applications
基金
江苏省自然科学青年基金(No.BK2012128)
国家自然科学基金(No.61170122)
关键词
模糊C均值聚类
空间距离
鲁棒性
Fuzzy C-Means clustering
space distance
robustness
