Normalized Gaussian Distance Graph Cuts for Image Segmentation

被引：0

作者：

Leng, Chengcai ^{[1
,2
,3
]}

Xu, Wei ^{[2
]}

Cheng, Irene ^{[3
]}

Xiong, Zhihui ^{[4
]}

Basu, Anup ^{[3
]}

机构：

[1] Nanchang Hangkong Univ, Sch Math & Informat Sci, Minist Educ, Key Lab Nondestruct Testing, Nanchang, Peoples R China

[2] Northwestern Polytech Univ, Dept Appl Math, Xian, Peoples R China

[3] Univ Alberta, Dept Comp Sci, Edmonton, AB T6G 2H1, Canada

[4] Natl Univ Def Technol, Coll Informat Syst & Management, Changsha, Hunan, Peoples R China

来源：

2015 IEEE INTERNATIONAL SYMPOSIUM ON MULTIMEDIA (ISM) | 2015年

关键词：

image segmentation; spectral clustering; normalized cuts; kernel k-means; normalized Gaussian distance;

D O I：

10.1109/ISM.2015.36

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

This paper presents a novel, fast image segmentation method based on normalized Gaussian distance on nodes in conjunction with normalized graph cuts. We review the equivalence between kernel k-means and normalized cuts. Then we extend the framework of efficient spectral clustering and avoid choosing weights in the weighted graph cuts approach. Experiments on synthetic data sets and real-world images demonstrate that the proposed method is effective and accurate.

引用

页码：523 / 528

页数：6

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