[1]张小凤,刘向阳.基于图像超像素分析的图像分割方法[J].计算机技术与发展,2018,28(07):25-28.[doi:10.3969/ j. issn.1673-629X.2018.07.006]
　ZHANG Xiao-feng,LIU Xiang-yang.Image Segmentation Based on Image Superpixel Analysis[J].,2018,28(07):25-28.[doi:10.3969/ j. issn.1673-629X.2018.07.006]
点击复制

基于图像超像素分析的图像分割方法()
分享到：

《计算机技术与发展》[ISSN:1006-6977/CN:61-1281/TN]

卷:
28
期数:
2018年07期
页码:
25-28
栏目:
智能、算法、系统工程
出版日期:
2018-07-10

文章信息/Info

Title:
Image Segmentation Based on Image Superpixel Analysis
文章编号:
1673-629X(2018)07-0025-04
作者:
张小凤; 刘向阳
河海大学 理学院,江苏 南京 211100
Author(s):
ZHANG Xiao-feng; LIU Xiang-yang
School of Science,Hohai University,Nanjing 211100,China
关键词:
图像分割; 超像素; SLIC 算法; 密度峰值聚类算法
Keywords:
image segmentation; superpixel; SLIC algorithm; density peak clustering algorithm
分类号:
TP391.41
DOI:
10.3969/ j. issn.1673-629X.2018.07.006
文献标志码:
A
摘要:
图像分割是计算机视觉领域的传统问题,也是图像分析和模式识别的关键组成部分。 传统的聚类图像分割方法是基于单个像素属性进行的图像分割方法,分割的结果有很大的噪声且具有不稳定性。 针对以上不足,考虑超像素能够较好地描述区域信息,且有利于图像的局部特征的提取与结构信息的表达,提出了基于图像超像素分析的图像分割方法。首先利用 SLIC 算法将单个像素点聚类为超像素块,其次通过密度峰值聚类算法(DPCA)对超像素块进行聚类,将基于单个像素属性的图像聚类分析改变为基于超像素的分析,可以提高分割结果的稳定性及准确性。 仿真结果表明,与 SLIC 算法和 DPCA 进行对比,发现该方法比另外两种方法更稳定且分割效果更好。
Abstract:
Image segmentation is a traditional problem in the field of computer vision and also a key component of image analysis and pattern recognition. The traditional clustering image segmentation method is based on the single pixel attribute,and its segmentation result has great noise and is unstable. To resolve the above shortcomings,in view of the superpixel with better description of the region information,which is beneficial to extract the local feature of image and to express the structural information,we propose an image segmentation meth-
od based on image superpixel analysis. Firstly,single pixel is clustered into superpixel block by SLIC algorithm,and then the superpixel block is clustered by the density peak clustering algorithm (DPCA). Changing the image clustering analysis based on the single pixel attribute to the analysis based on the superpixel can improve the stability and accuracy of the segmentation result. After the simulation test,compared with the SLIC algorithm and DPCA,it is found that the proposed algorithm is more stable and has better segmentation than the
other two methods.

相似文献/References:

[1]蒋璐璐 王适 王宝成 李慧敏 李鑫慧.一种改进的标记分水岭遥感图像分割方法[J].计算机技术与发展,2010,(01):36.
　JIANG Lu-lu,WANG Shi,WANG Bao-cheng,et al.Segmentation of Remote Sensing Image Based on an Improved Labeling Watershed Algorithm[J].,2010,(07):36.
[2]张少娴 俞琼.基于时空相关性预测的运动估计的优化[J].计算机技术与发展,2010,(01):100.
　ZHANG Shao-xian,YU Qiong.An Optimization Method for Spatiotemporal Predictive Motion Estimation[J].,2010,(07):100.
[3]王兴 冯子亮.基于自适应初始值的FCM聚类图像分割[J].计算机技术与发展,2010,(03):101.
　WANG Xing,FENG Zi-liang.An Image Segmentation Algorithm Based on Adaptive Initialization FCM Clustering[J].,2010,(07):101.
[4]何小娜 逄焕利.基于二维直方图和改进蚁群聚类的图像分割[J].计算机技术与发展,2010,(03):128.
　HE Xiao-na,PANG Huan-li.Image Segmentation Based on Improved Ant Colony Clustering and Two- Dimensional Histogram[J].,2010,(07):128.
[5]宋淑娜 李金霞 胡学坤 高尚.一种自适应模糊阈值区间的图像分割方法[J].计算机技术与发展,2010,(05):121.
　SONG Shu-na,LI Jin-xia,HU Xue-kun,et al.A Method of Adaptive Fuzzy Threshold Region for Image Segmentation[J].,2010,(07):121.
[6]来磊 卢文科 邓开连.基于二维Tsallis交叉熵直线型图像阈值分割方法[J].计算机技术与发展,2010,(06):105.
　LAI Lei,LU Wen-ke,DENG Kai-lian.New Image Thresholding Segmentation Methods Based on Two-Dimensional Tsallis Cross-Entropy Liner-Type[J].,2010,(07):105.
[7]黄长专 王彪 杨忠.图像分割方法研究[J].计算机技术与发展,2009,(06):76.
　HUANG Chang-zhuan,WANG Biao,YANG Zhong.A Study on Image Segmentation Techniques[J].,2009,(07):76.
[8]李光耀 聂诗良.基于小波分解和模糊聚类的图像分割方法[J].计算机技术与发展,2009,(06):121.
　LI Guang-yao,NIE Shi-liang.Image Segment Algorithm Based on Wavelet Decomposition and Fuzzy Clustering Theory[J].,2009,(07):121.
[9]吴亚 汪继文.水平集图像分割中重新初始化规避的探索[J].计算机技术与发展,2009,(09):69.
　WU Ya,WANG Ji-wen.Avoidance of Re- Initialization in Level Set Image Segmentation[J].,2009,(07):69.
[10]李鑫环 陈立潮 赵红艳 赵勇.基于多小波分析与SOFM的MR图像分割算法研究[J].计算机技术与发展,2009,(09):104.
　LI Xin-huan,CHEN Li-chao,ZHAO Hong-yan,et al.Research on MR Image Segmentation Based on Multi- wavelet Analysis and SOFM[J].,2009,(07):104.
[11]李 普,陈 黎.基于超像素随机游走的自然场景图像分割方法[J].计算机技术与发展,2021,31(12):61.[doi:10. 3969 / j. issn. 1673-629X. 2021. 12. 011]
　LI Pu,CHEN Li.Natural Scene Image Segmentation Method Based on Super-pixel Random Walk[J].,2021,31(07):61.[doi:10. 3969 / j. issn. 1673-629X. 2021. 12. 011]

更新日期/Last Update: 2018-08-24