摘要
针对核空间模糊局部信息C-均值聚类算法(KWFLICM)对低对比度图像抗噪性差的不足,提出一种基于噪声距离的核空间模糊局部信息C-均值聚类算法。该算法在KWFLICM算法的基础上改变隶属度约束条件并引入噪声距离δ获得一种改进的聚类目标函数,并借鉴现有噪声聚类思想构造出具有良好抗噪性的模糊聚类迭代隶属度和聚类中心表达式,最后给出相应的聚类分割算法。实验结果表明,该改进算法对于椒盐噪声干扰的对比度较弱的灰度图像比KWFLICM聚类分割算法更有优势。
A fuzzy C-means clustering algorithm with local information based on the noise distance algorithm is proposed in this paper to tackle at the problem that the fuzzy C-Means clustering with local information and Kernel metric （KWFLICM） algorithm has poor noise-robust for low contrast image. On the basis of the existing KWFLICM algorithm, this algorithm changes constraints on membership and introduces noise distance 3 to get an improved noise-robust clustering objective function. Based on existing noise clustering idea, this algorithm constructs fuzzy clustering iterative expressions of membership and cluster centers with good noise resistance. Then it gives the corresponding clustering segmentation algorithm. Experimental results show that the improved algorithm for low-contrast image by salt and pepper noise interference is more superiority than that of the existing KWFLICM clustering segmentation algorithm.
出处
《西安邮电大学学报》
2015年第4期32-37,共6页
Journal of Xi’an University of Posts and Telecommunications
基金
国家自然科学基金重点资助项目(61136002)
陕西省自然科学基金资助项目(2014JM8331
2014JQ5138
2014JM8307)
关键词
图像分割
模糊聚类
噪声距离
核函数
image segmentation , fuzzy clustering, noise distance, kernel functions
