title: Isoperimetric graph partitioning for image segmentation.
abstract: 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.
authors.affiliation: Department of Imaging and Visualization, Siemens Corporate Research, Princeton, NJ 08540, USA. leo.grady@siemens.com
authors.forename: Leo
authors.initials: L
authors.lastname: Grady
authors.forename: Eric L
authors.initials: EL
authors.lastname: Schwartz
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D003196:Computer Graphics; D007089:Image Enhancement / Q000379:methods*; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D016247:Information Storage and Retrieval / Q000379:methods; D010363:Pattern Recognition, Automated / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D013382:Subtraction Technique