Hash graph based semi-supervised learning method and its application in image segmentation - 文章详情页

作者：

Zhang, Chen-Guang (Zhang, Chen-Guang.) | Li, Yu-Jian (Li, Yu-Jian.)

收录：

EI Scopus PKU CSCD

摘要：

Graph　based　semi-supervised　learning　(GSL)　method　runs　slowly　because　of　the　need　of　much　time　to　construct　a　neghbor　graph.　This　paper　presents　a　hash　graph　based　semi-supervised　learning　(HGSL)　method,　which　can　search　neighbors　by　locality　sensitive　hashing　function　and　efficently　reduce　the　time　for　GSL　to　construct　a　neighbor　graph.　Image　segmentation　experiments　show　that　HGSL　has　an　improvement　of　0.47%　in　average　segmenting　accuracy,　and　can　geatly　reduce　the　segmenting　time,　e.　g.,　it　takes　about　28.5%　of　the　time　for　GSL　to　segent　an　image　with　size　of　300　×　800.　Copyright　©　2010　Acta　Automatica　Sinica.　All　rights　reserved.

关键词：

Graphic methods Image enhancement Image segmentation Machine learning Supervised learning

作者机构：

[ 1 ] [Zhang, Chen-Guang]College of Computer Science and Technology, Beijing University of Technology, Beijing 100124, China
[ 2 ] [Zhang, Chen-Guang]College of Information Science and Technology, Hainan University, Haikou 571737, China
[ 3 ] [Li, Yu-Jian]College of Computer Science and Technology, Beijing University of Technology, Beijing 100124, China

通讯作者信息：

电子邮件地址：

liyujian@bjut.edu.cn

查看成果更多字段

成果类型
所属机构

所有年份指定年份从至