基于加权质量评价函数的K-means图像分割算法

计算机科学 ›› 2019, Vol. 46 ›› Issue (6A): 158-160.

基于加权质量评价函数的K-means图像分割算法

刘长齐¹, 邵堃¹, 霍星², 范冬阳¹, 檀结庆²

合肥工业大学计算机与信息学院合肥230000¹;
合肥工业大学数学学院合肥230000²

出版日期:2019-06-14 发布日期:2019-07-02
通讯作者: 霍星(1979-),女,博士,副教授,CCF会员,主要研究方向为图形图像处理,E-mail:huoxing@hfut.edu.cn
作者简介:刘长齐(1996-),男,主要研究方向为图形图像处理;邵堃(1967-),男,博士,副教授,CCF会员,主要研究方向为软件开发与理论,E-mail:2015211730@mail.hfut.edu.cn;范冬阳(1997-),男,主要研究方向为图形图像处理;檀结庆(1962-),男,博士,教授,主要研究方向为非线性计算。
基金资助:
本文受国家自然科学基金面上项目(61872407,61572167)资助。

K-means Image Segmentation Algorithm Based on Weighted Quality Evaluation Function

LIU Chang-qi¹, SHAO Kun¹, HUO Xing², FAN Dong-yang¹, TAN Jie-qing²

School of Computer and Information,Hefei University of Technology,Hefei 230000,China¹;
School of Mathematics,Hefei University of Technology,Hefei 230000,China²

Online:2019-06-14 Published:2019-07-02

摘要/Abstract

摘要： K-means聚类算法是图像分割中比较常见的一种方式。它是一种无监督学习方法,能从图像的灰度值特征中发现关联规则,因而具有比较强的分割能力。但是,由于该算法使用的分类依据比较单一,且初始聚簇中心具有不确定性,其在图像分割上仍存在一定的缺陷。针对此问题,提出了一种改进的K-means算法用于图像分割。此方法使用基于信息熵的迭代改进算法为K-means算法选取初始聚类中心,然后对K-means算法提出新的加权质量评价函数用于更好地选取图像分割阈值。实验结果表明:改进后的算法在图像分割上的准确率和稳定性都要优于OTSU算法和传统的K-means算法。

关键词: K-means算法, 模式识别, 图像分割, 阈值选取

Abstract: K-means clustering algorithm is a common way in image segmentation.As an unsupervised learning method,it can find the association rules from characteristics of grey levels,thus has a great capability of segmentation.However,due to its single classification basis and uncertainty of the initial cluster centers,this algorithm still has some defects in image segmentation.Aiming at this problem,this paper proposed a modified K-means algorithm for image segmentation.The new algorithm uses the improved iterative algorithm based on information entropy to select thresholds as the initial K-means clustering centers,and then puts forward a new weighted quality evaluation function for K-means algorithm to get better segmentation thresholds.The experimental results show that the improved algorithm has higher accuracy and stability than OTSU algorithm and traditional K-means algorithm in image segmentation.

Key words: Image segmentation, K-means algorithm, Pattern recognition, Threshold value selection

中图分类号:

TP391

刘长齐, 邵堃, 霍星, 范冬阳, 檀结庆. 基于加权质量评价函数的K-means图像分割算法[J]. 计算机科学, 2019, 46(6A): 158-160. https://doi.org/

LIU Chang-qi, SHAO Kun, HUO Xing, FAN Dong-yang, TAN Jie-qing. K-means Image Segmentation Algorithm Based on Weighted Quality Evaluation Function[J]. Computer Science, 2019, 46(6A): 158-160. https://doi.org/

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