摘要
传统模糊聚类算法(FCM)存在初始聚类中心不确定的问题,在图像分割中没有完全考虑到像素之间的灰度、空间信息.为解决此问题,提出了基于新距离矩阵方差的模糊聚类图像分割算法。用像素点生成一个改进的新距离矩阵,并根据此矩阵特点选取初始聚类中心;结合方差确定聚类类别数,并消除部分噪声;对聚类结果进行有效性判定,确定最佳的分割结果。与SPFCM算法相比,提出算法的平均准确率提高了4.55%。实验结果表明提出方法能有效提高图像分割的平均准确率,对处理噪声有更好的效果。
The traditional fuzzy clustering algorithm（FCM） has the problem of uncertain initial cluster centers, and the gray and spatial information between pixels is not fully considered in image segmentation.In order to solve the above problem, a new fuzzy clustering image segmentation algorithm is proposed based on new distance matrix variance. The pixels are used to generate an improved new distance matrix,and the initial cluster center is selected according to characteristics of the new distance matrix. The number of cluster categories is determined combined with the variance, and part of noise is eliminated.The effectiveness determination is carried out on clustering result, and the best segmentation results are determined. Compared with the contrast algorithms, the average accuracy of proposed algorithm is increased by 4.55%. Experimental results show that the proposed method can effectively improve the average accuracy of image segmentation, and has a better effect on noise treatment.
作者
胡婕
周跃跃
HU Jie;ZHOU Yueyue(School of Computer Science and Information Engineering, Hubei University, Wuhan 430062, Chin)
出处
《量子电子学报》
CAS
CSCD
北大核心
2018年第3期286-293,共8页
Chinese Journal of Quantum Electronics
基金
国家自然科学基金
61202100~~
关键词
图像处理
图像分割
模糊聚类算法
新距离矩阵
方差
image processing
image segmentation
fuzzy clustering algorithm
new distance matrix
variance
