电子学报 ›› 2019, Vol. 47 ›› Issue (10): 2126-2133.DOI: 10.3969/j.issn.0372-2112.2019.10.014

所属专题: 机器学习之图像处理

• 学术论文 • 上一篇    下一篇

基于超像素多特征融合的快速图像分割算法

侯小刚1, 赵海英2, 马严1   

  1. 1. 北京邮电大学网络技术研究院, 北京 100876;
    2. 北京邮电大学计算机学院, 北京 100876
  • 收稿日期:2018-10-11 修回日期:2019-04-29 出版日期:2019-10-25 发布日期:2019-10-25
  • 通讯作者: 赵海英
  • 作者简介:侯小刚 男,1984年生,甘肃天水人,北京邮电大学博士研究生.主要研究方向为数字图像处理与文化计算等.E-mail:houxiaogang05@bupt.edu.cn;马严 男,1955年生,北京人,北京邮电大学教授,博士生导师.主要研究方向为计算机网络及其应用、网络管理和网络安全等.E-mail:mayan@bupt.edu.cn
  • 基金资助:
    北京市科技计划(No.D171100003717003);国家社会科学基金重大研究专项(No.18VDL001);语义资源高精尖创新(No.060344)

Fast Image Segmentation Algorithm Based on Superpixel Multi-feature Fusion

HOU Xiao-gang1, ZHAO Hai-ying2, MA Yan1   

  1. 1. Institute of Network Technology, Beijing University of Posts and Telecommunications, Beijing 100876, China;
    2. School of Computer Science, Beijing University of Posts and Telecommunications, Beijing 100876, China
  • Received:2018-10-11 Revised:2019-04-29 Online:2019-10-25 Published:2019-10-25

摘要: 为了提高高分辨率图像分割效率,解决复杂图案中待分割目标边缘附近前景与背景区分度小而造成的分割目标不完整问题,本文通过引入超像素HOG特征,提出了一种基于超像素多特征融合(superpixel multi-feature fusion,SMFF)的快速图像分割算法.首先采用目前最有效的超像素算法对待分割图像进行超像素预分割,然后提取基于超像素的HOG特征、Lab颜色特征和空间位置特征,设计基于超像素的多特征度量算法,最终采用图割理论实现了基于超像素多特征融合的快速图像分割.实验结果验证了本文算法的有效性,其算法性能接近于目前最经典图像分割算法,且本文算法的时间性能要明显优于其它对比算法.

关键词: 图像分割, 多特征融合, HOG特征, 超像素

Abstract: In order to improve the efficiency of high-resolution image segmentation and solve the problem of incomplete segmentation caused by small discrimination of foreground and background in the complex pattern near the edge of the target to be segmented,we propose a fast image segmentation algorithm based on superpixel multi-feature fusion (SMFF).Firstly,the most effective superpixel algorithm is used for superpixel processing,and then the superpixel-based HOG feature,laboratory color feature and spatial position feature are extracted.Lastly,by designing a multi-feature measurement algorithm,the fast image segmentation algorithm based on superpixel multi-feature fusion is implemented.Experimental results verify the effectiveness of the proposed algorithm,which is close to the most classical image segmentation algorithm,and the time performance of the proposed algorithm is significantly better than other comparison algorithms.

Key words: image segmentation, multi-feature fusion, HOG features, superpixel

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