基于改进k-means算法的数字图像聚类

高西; 胡子牧

doi:10.3788/YJYXS20203502.0173

您当前的位置：

首页 >

文章列表页 >

基于改进k-means算法的数字图像聚类

图像处理 | 更新时间：2020-07-16

- 基于改进k-means算法的数字图像聚类
- Digital image clustering based on improved k-means algorithm
- 液晶与显示 2020年35卷第2期页码：173-179
- 作者机构：
  
  重庆医科大学附属大学城医院, 重庆 401331
- 作者简介：
  
  [ "高西(1976-), 女, 四川内江人, 学士, 2009年于重庆医科大学获得学士学位, 主要从事皮肤性病学暨医学美容、皮肤影像及图像处理方面的研究。E-mail:gaoxi0627@hotmail.com" ]
  [ "胡子牧(1973-), 男, 湖北沔阳人, 学士, 2004年于中央党校获得学士学位, 主要从事医院管理、图像处理方面的研究。E-mail:huzimu0627@hotmail.com" ]
- 基金信息：
- DOI：10.3788/YJYXS20203502.0173
  中图分类号： TP394.1;TH691.9
- 收稿日期：2019-07-22，
  
  修回日期：2019-10-21，
  
  录用日期：2019-10-21，
  
  纸质出版日期：2020-02-05
- 稿件说明：
移动端阅览
高西, 胡子牧. 基于改进k-means算法的数字图像聚类[J]. 液晶与显示, 2020,35(2):173-179.

Xi GAO, Zi-mu HU. Digital image clustering based on improved k-means algorithm[J]. Chinese journal of liquid crystals and displays, 2020, 35(2): 173-179.
高西, 胡子牧. 基于改进k-means算法的数字图像聚类[J]. 液晶与显示, 2020,35(2):173-179. DOI： 10.3788/YJYXS20203502.0173.

Xi GAO, Zi-mu HU. Digital image clustering based on improved k-means algorithm[J]. Chinese journal of liquid crystals and displays, 2020, 35(2): 173-179. DOI： 10.3788/YJYXS20203502.0173.

摘要

针对海量彩色图像聚类问题，本文引入改进型k-means算法并将其应用于彩色图像聚类中。该算法由类内-类间距离加权k-means算法和基于近邻传播聚类算法的类数量上界确定方法组成。在实验中，彩色图像的亮度分量的局部二值模式（Local Binary Pattern，LBP）图被重组成行向量，然后构成样本集，本文所提出的改进型k-means算法被用于对样本集进行聚类处理。实验结果显示，在多个聚类方法常用的评价指标上，本方法相比于传统方法达到了更高的聚类准确度。同时，相比于传统方法，本方法也更具有执行效率。

Abstract

For the mass image clustering problem

the improved k-means algorithm is proposed and applied to the color image clustering. The algorithm consists of intraclass-interclass distance weighted k-means algorithm and nearest neighbor propagation clustering algorithm. In the experiment

the LBP map of the luminance component of the color image is reconstructed into a row vector and then constitutes a sample set. The improved k-means algorithm proposed in this paper is used to cluster the sample set. The experimental results show that the proposed method achieves higher clustering accuracy than the traditional methods in the evaluation indicators commonly used in multiple clustering methods. At the same time

the method is more efficient than traditional methods.

关键词

Keywords

references

D G LOWE . Distinctive image features from scale-invariant keypoints . International Journal of Computer Vision , 2004 . 60 ( 2 ): 91 - 110 . http://d.old.wanfangdata.com.cn/NSTLQK/NSTL_QKJJ025429678/ http://d.old.wanfangdata.com.cn/NSTLQK/NSTL_QKJJ025429678/ .

R DIXIT , R NASKAR . Region duplication detection in digital images based on centroid linkage clustering of key-points and graph similarity matching . Multimedia Tools and Applications , 2019 . 78 ( 10 ): 13819 - 13840 . DOI: 10.1007/s11042-018-6666-1 http://doi.org/10.1007/s11042-018-6666-1 .

孟晓燕 , 段建民 , 刘丹 . 采用人体树图与混合粒子群聚类的行人检测 . 光学精密工程 , 2018 . 26 ( 7 ): 1802 - 1812 . http://d.old.wanfangdata.com.cn/Periodical/gxjmgc201807027 http://d.old.wanfangdata.com.cn/Periodical/gxjmgc201807027 .

X Y MENG , J M DUAN , D LIU . Pedestrian detection based on tree-structured graphical model of the human body and hybrid particle swarm clustering . Optics and Precision Engineering , 2018 . 26 ( 7 ): 1802 - 1812 . http://d.old.wanfangdata.com.cn/Periodical/gxjmgc201807027 http://d.old.wanfangdata.com.cn/Periodical/gxjmgc201807027 .

郑思凡 , 王卫星 , 吴永春 . 基于光流拓扑稀疏加权的同步运动去混叠 . 光学精密工程 , 2019 . 27 ( 5 ): 1188 - 1195 . http://d.old.wanfangdata.com.cn/Periodical/gxjmgc201905022 http://d.old.wanfangdata.com.cn/Periodical/gxjmgc201905022 .

S F ZHENG , W X WANG , Y C WU . Synchronous motion de-aliasing based on optical flow topological sparse weighting . Optics and Precision Engineering , 2019 . 27 ( 5 ): 1188 - 1195 . http://d.old.wanfangdata.com.cn/Periodical/gxjmgc201905022 http://d.old.wanfangdata.com.cn/Periodical/gxjmgc201905022 .

陈华伟 , 袁小翠 , 吴禄慎 , 等 . 基于曲率突变分析的点云特征线自动提取 . 光学精密工程 , 2019 . 27 ( 5 ): 1218 - 1228 . http://d.old.wanfangdata.com.cn/Periodical/gxjmgc201905025 http://d.old.wanfangdata.com.cn/Periodical/gxjmgc201905025 .

H W CHEN , X C YUAN , L S WU , 等 . Automatic point cloud feature-line extraction algorithm based on curvature-mutation analysis . Optics and Precision Engineering , 2019 . 27 ( 5 ): 1218 - 1228 . http://d.old.wanfangdata.com.cn/Periodical/gxjmgc201905025 http://d.old.wanfangdata.com.cn/Periodical/gxjmgc201905025 .

LU S Z, YU H L, WANG X H, et al . Clustering method of raw meal composition based on PCA and kmeans[C]// Proceedings of the 37 th Chinese Control Conference . Wuhan, China: IEEE, 2018: 121-135.

YIH J M. FCM algorithm besed on normalized mahalanobis distances in image clustering[C] // Proceedings of 2010 International Conference on Machine Learning and Cybernetics . Qingdao, China: IEEE, 2010: 2724-2729.

ZHANG Z Y, CHENG H M, ZHANG S G. Approach to SOM based correlation clustering[C]// Proceedings of 2008 Chinese Control and Decision Conference . Yantai, China: IEEE, 2008: 121-135.

B J FREY , D DUECK . Clustering by passing messages between data points . Science , 2007 . 315 ( 5814 ): 972 - 976 . DOI: 10.1126/science.1136800 http://doi.org/10.1126/science.1136800 .

ESTER M, KRIEGEL H P, SANDER J, et al . A density-based algorithm for discovering clusters in large spatial databases with noise[C]// Proceedings of the 2 nd International Conference on Knowledge Discovery and Data Mining . Portland, Oregon: ACM, 1996: 226-231.

J SANDER , M ESTER , H P KRIEGEL , 等 . Density-based clustering in spatial databases:the algorithm GDBSCAN and its applications . Data Mining and Knowledge Discovery , 1998 . 2 ( 2 ): 169 - 194 . http://d.old.wanfangdata.com.cn/NSTLQK/10.1023-A-1009745219419/ http://d.old.wanfangdata.com.cn/NSTLQK/10.1023-A-1009745219419/ .

ANKERST M, BREUNIG M M, KRIEGEL H P, et al . OPTICS: ordering points to identify the clustering structure[C]// Proceedings of 1999 ACM SIGMOD International Conference on Management of Data. Philadelphia, Pennsylvania, USA: ACM, 1999: 49-60.

X W XU , J JÄGER , H P KRIEGEL . A fast parallel clustering algorithm for large spatial databases . Data Mining and Knowledge Discovery , 1999 . 3 ( 3 ): 263 - 290 .

黄晓辉 , 王成 , 熊李艳 , 等 . 一种集成簇内和簇间距离的加权k-means聚类方法 . 计算机学报 , 2019 . 1 - 15 . http://d.old.wanfangdata.com.cn/Periodical/jsjxb201912014 http://d.old.wanfangdata.com.cn/Periodical/jsjxb201912014 .

X H HUANG , C WANG , L Y XIONG , 等 . A weighting k-means clustering approach by integrating intra-cluster and inter-cluster distances . Chinese Journal of Computers , 2019 . 1 - 15 . http://d.old.wanfangdata.com.cn/Periodical/jsjxb201912014 http://d.old.wanfangdata.com.cn/Periodical/jsjxb201912014 .

周世兵 , 徐振源 , 唐旭清 . 新的K-均值算法最佳聚类数确定方法 . 计算机工程与应用 , 2010 . 46 ( 16 ): 27 - 31 . http://d.old.wanfangdata.com.cn/Periodical/jsjgcyyy201016008 http://d.old.wanfangdata.com.cn/Periodical/jsjgcyyy201016008 .

S B ZHOU , Z Y XU , X Q TANG . New method for determining optimal number of clusters in K-means clustering algorithm . Computer Engineering and Applications , 2010 . 46 ( 16 ): 27 - 31 . http://d.old.wanfangdata.com.cn/Periodical/jsjgcyyy201016008 http://d.old.wanfangdata.com.cn/Periodical/jsjgcyyy201016008 .

S DUDOIT , J FRIDLYAND . A prediction-based resampling method for estimating the number of clusters in a dataset . Genome Biology , 2002 . 3 ( 7 ): research0036.1 http://d.old.wanfangdata.com.cn/NSTLQK/10.1186-gb-2002-3-7-research0036/ http://d.old.wanfangdata.com.cn/NSTLQK/10.1186-gb-2002-3-7-research0036/ .

P J QIAN , J X ZHOU , Y Z JIANG , 等 . Multi-view maximum entropy clustering by jointly leveraging inter-view collaborations and intra-view-weighted attributes . IEEE Access , 2018 . 6 28594 - 28610 . DOI: 10.1109/ACCESS.2018.2825352 http://doi.org/10.1109/ACCESS.2018.2825352 .

陶性留 , 俞璐 , 王晓莹 . 基于非负矩阵分解和模糊C均值的图像聚类方法 . 信息技术与网络安全 , 2019 . 38 ( 3 ): 44 - 48 . http://d.old.wanfangdata.com.cn/Periodical/wxjyyy201903011 http://d.old.wanfangdata.com.cn/Periodical/wxjyyy201903011 .

X L TAO , L YU , X Y WANG . One method based on non-negative matrix factorization and fuzzy C means for image clustering . Information Technology and Network Security , 2019 . 38 ( 3 ): 44 - 48 . http://d.old.wanfangdata.com.cn/Periodical/wxjyyy201903011 http://d.old.wanfangdata.com.cn/Periodical/wxjyyy201903011 .

H J JIA , S F DING , L H MENG , 等 . A density-adaptive affinity propagation clustering algorithm based on spectral dimension reduction . Neural Computing and Applications , 2014 . 25 ( 7/8 ): 1557 - 1567 . http://www.wanfangdata.com.cn/details/detail.do?_type=perio&id=bf51187e1914772b55a533488da148c5 http://www.wanfangdata.com.cn/details/detail.do?_type=perio&id=bf51187e1914772b55a533488da148c5 .

N R PAL , J C BEZDEK , R J HATHAWAY . Sequential competitive learning and the fuzzy c -means clustering algorithms . Neural Networks , 1996 . 9 ( 5 ): 787 - 796 . DOI: 10.1016/0893-6080(95)00094-1 http://doi.org/10.1016/0893-6080(95)00094-1 .

A K JAIN . Data clustering:50 years beyond K-means . Pattern Recognition Letters , 2010 . 31 ( 8 ): 651 - 666 . DOI: 10.1016/j.patrec.2009.09.011 http://doi.org/10.1016/j.patrec.2009.09.011 .

浏览量

下载量

CSCD

文章被引用时，请邮件提醒。

提交

工具集

关联资源

一种利用人眼视觉特性的数字图像水印技术