基于变分水平集的图像模糊聚类分割  

Image Fuzzy Clustering Segmentation Based on Variational Level Set

作　　者：唐利明[1,2] 王洪珂[1] 陈照辉[1] 黄大荣[3] 

机构地区：[1]重庆科技学院数理学院,重庆401331 [2]重庆大学数学与统计学院,重庆401331 [3]山区桥梁与隧道工程国家重点实验室培育基地(重庆交通大学),重庆400074

出　　处：《软件学报》2014年第7期1570-1582,共13页Journal of Software

基　　金：国家自然科学基金(61004118)

摘　　要：结合变分水平集方法和模糊聚类,提出了一个基于变分水平集的图像聚类分割模型.该模型引入了一个基于图像局部信息的外部模糊聚类能量和一个新的关于零水平集的正则化能量,使得该模型对噪声图像的聚类分割更具鲁棒性.通过在能量泛函中加入一个内部约束能量约束水平集函数为符号距离函数,可以使水平集演化过程无需重新初始化.进一步提出了一种变分形式的聚类中心更新方法,实现了半监督的图像聚类分割.实验中采用不同类型的图像与FCM聚类模型、CV模型、Samson模型进行了对比实验,实验结果显示,该模型能够克服图像中噪声的影响,取得较满意的聚类分割效果.An image clustering segmentation model combined with variational level set and fuzzy clustering is proposed in this paper. An external fuzzy clustering energy based on the local image information and a new regularization energy with respect to the zero level set are introduced in the energy functional, which makes the proposed model robust in noisy image segmentation. An internal energy that forces the level set function to be close to a signed distance function is introduced in the energy functional, which can completely eliminate the need of the expensive periodical re-initialization procedure for level set function during its evolution. Furthermore, this paper proposes a variational formulation to update the cluster centers in the procedure of clustering, which realizes the semi-supervised clustering segmentation. The experimental results show that the proposed model, compared with the FCM clustering model, CV model and Samson model, can reduce the influence of noise and get better segmentation results for different kinds of images.

关 键 词：变分水平集 图像聚类 图像分割 模糊聚类 聚类中心 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]