Isoperimetric graph partitioning for image segmentation

被引：167

作者：

Grady, L

Schwartz, EL

机构：

[1] Siemens Corp Res, Dept Imaging & Visualizat, Princeton, NJ 08540 USA

[2] Boston Univ, Dept Cognit & Neural Syst, Boston, MA 02215 USA

[3] Boston Univ, Dept Elect & Comp Engn, Boston, MA 02215 USA

来源：

IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE | 2006年 / 28卷 / 03期

关键词：

graph-theoretic methods; graphs and networks; graph algorithms; image representation; special architectures; algorithms; computer vision; applications;

D O I：

10.1109/TPAMI.2006.57

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

Spectral graph partitioning provides a powerful approach to image segmentation. We introduce an alternate idea that finds partitions with a small isoperimetric constant, requiring solution to a linear system rather than an eigenvector problem. This approach produces the high quality segmentations of spectral methods, but with improved speed and stability.

引用

页码：469 / 475

页数：7

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