机械与电子2019，Vol.37Issue(2)：40-44,5.
结合密度峰聚类的K均值图像分割算法
K-means Image Segmentation Algorithm Combined with Density Peak Clustering
王鹏宇 1游有鹏 1杨雪峰1
作者信息
- 1. 南京航空航天大学机电学院, 江苏 南京 210001
- 折叠
摘要
Abstract
Aiming at the deficiency of K-means clustering algorithm in image segmentation, an improved K-means algorithm with better image segmentation effect was proposed by combining density peak clustering algorithm to improve the original algorithm.The K-means algorithm needs to specify the number of clustering centers manually, and the initialization of clustering centers has a great impact on the final image segmentation results.In view of the above shortcomings, the K mean algorithm was improved.The number of clustering centers and the more accurate initial clustering centers for image segmentation were automatically determined by density peak algorithm.In order to measure the perception of chromatic aberration in human eyes, NBS distance was introduced into the algorithm as distance measure.Experimental results show that the improved image segmentation algorithm has stable performance and good effect in segmentation of images.关键词
K均值聚类/密度峰聚类/NSB距离/图像分割Key words
K-means clustering/density peak clustering/NBS distance/image segmentation分类
信息技术与安全科学引用本文复制引用
王鹏宇,游有鹏,杨雪峰..结合密度峰聚类的K均值图像分割算法[J].机械与电子,2019,37(2):40-44,5.
