激光与光电子学进展, 2020, 57 (2): 021001, 网络出版: 2020-01-03   

对类大小不敏感的图像分割模糊C均值聚类方法 下载: 1064次

Fuzzy C-Means Clustering Algorithm for Image Segmentation Insensitive to Cluster Size
作者单位
1 河北地质大学信息工程学院, 河北 石家庄 050031
2 河北地质大学河北省光电信息与地球探测技术重点实验室, 河北 石家庄 050031
摘要
常见的模糊聚类算法不能有效分割具有类大小不均衡特性的图像,为此,提出对类大小不敏感的模糊C均值聚类图像分割算法。首先将类大小引入至含邻域信息模糊聚类算法(FCM_S)的目标函数中,使得类大小在目标函数中发挥作用,从而能均衡较大类和较小类对目标函数的贡献,弱化算法对类大小不均衡的敏感度并推导出新的隶属度函数和聚类中心;然后提出用紧密度来表征每一类中像素的分布状态,并将其引入至聚类的迭代进程;最后利用符合类大小不均衡特征的无损检测图像进行算法验证。结果表明:本文算法能够展示出更好的视觉分割效果,而且从分割准确率(SA)和调整兰德指数(ARI)上看也更优异,由此显示本文算法具有抗噪性及对类大小不敏感的特性。
Abstract
Common fuzzy clustering algorithms can easily cause segmentation failure when an image exhibits unequal cluster sizes. Therefore, a fuzzy C-means clustering algorithm that is insensitive to cluster size is proposed. Firstly, the size of each cluster is integrated into the objective function of the fuzzy C-means algorithm with neighborhood information (FCM_S), which makes the cluster size play a role in the objective function. This improvement can balance the relative contribution from larger and smaller clusters to the objective function and weaken the sensitivity of the algorithm to unequal cluster sizes. Then, a new membership function and clustering center are deduced. Secondly, we design a new expression called “compactness” to represent the pixel distribution of each cluster, which is then introduced into the iterative clustering process. Finally, nondestructive testing images exhibiting unequal cluster sizes are used to verify the availability of the proposed algorithm. The segmentation results not only show improved visual segmentation effects but also show improved performances compared with those of other fuzzy clustering algorithms, as measured by two indices, i.e., segmentation accuracy and adjusted Rand index, thus demonstrating the anti-noise and size-insensitive capabilities of the proposed algorithm.

赵战民, 朱占龙, 刘永军, 刘明, 郑一博. 对类大小不敏感的图像分割模糊C均值聚类方法[J]. 激光与光电子学进展, 2020, 57(2): 021001. Zhao Zhanmin, Zhu Zhanlong, Liu Yongjun, Liu Ming, Zheng Yibo. Fuzzy C-Means Clustering Algorithm for Image Segmentation Insensitive to Cluster Size[J]. Laser & Optoelectronics Progress, 2020, 57(2): 021001.

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