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Normalized Cuts Revisited: A Reformulation for Segmentation with Linear Grouping Constraints

Eriksson, Anders P LU ; Olsson, Carl LU and Kahl, Fredrik LU (2011) In Journal of Mathematical Imaging and Vision 39(1). p.45-61
Abstract
Indisputably Normalized Cuts is one of the most popular segmentation algorithms in pattern recognition and computer vision. It has been applied to a wide range of segmentation tasks with great success. A number of extensions to this approach have also been proposed, including ones that can deal with multiple classes or that can incorporate a priori information in the form of grouping constraints. However, what is common for all these methods is that they are noticeably limited in the type of constraints that can be incorporated and can only address segmentation problems on a very specific form. In this paper, we present a reformulation of Normalized Cut segmentation that in a unified way can handle linear equality constraints for an... (More)
Indisputably Normalized Cuts is one of the most popular segmentation algorithms in pattern recognition and computer vision. It has been applied to a wide range of segmentation tasks with great success. A number of extensions to this approach have also been proposed, including ones that can deal with multiple classes or that can incorporate a priori information in the form of grouping constraints. However, what is common for all these methods is that they are noticeably limited in the type of constraints that can be incorporated and can only address segmentation problems on a very specific form. In this paper, we present a reformulation of Normalized Cut segmentation that in a unified way can handle linear equality constraints for an arbitrary number of classes. This is done by restating the problem and showing how linear constraints can be enforced exactly in the optimization scheme through duality. This allows us to add group priors, for example, that certain pixels should belong to a given class. In addition, it provides a principled way to perform multi-class segmentation for tasks like interactive segmentation. The method has been tested on real data showing good performance and improvements compared to standard normalized cuts. (Less)
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author
organization
publishing date
type
Contribution to journal
publication status
published
subject
in
Journal of Mathematical Imaging and Vision
volume
39
issue
1
pages
45 - 61
publisher
Springer
external identifiers
  • wos:000285974800004
  • scopus:79952002461
ISSN
0924-9907
DOI
10.1007/s10851-010-0223-5
language
English
LU publication?
yes
id
e427cc05-0af6-400e-84c2-b73b87a86453 (old id 1687606)
date added to LUP
2010-11-04 17:01:13
date last changed
2017-07-30 03:04:44
@article{e427cc05-0af6-400e-84c2-b73b87a86453,
  abstract     = {Indisputably Normalized Cuts is one of the most popular segmentation algorithms in pattern recognition and computer vision. It has been applied to a wide range of segmentation tasks with great success. A number of extensions to this approach have also been proposed, including ones that can deal with multiple classes or that can incorporate a priori information in the form of grouping constraints. However, what is common for all these methods is that they are noticeably limited in the type of constraints that can be incorporated and can only address segmentation problems on a very specific form. In this paper, we present a reformulation of Normalized Cut segmentation that in a unified way can handle linear equality constraints for an arbitrary number of classes. This is done by restating the problem and showing how linear constraints can be enforced exactly in the optimization scheme through duality. This allows us to add group priors, for example, that certain pixels should belong to a given class. In addition, it provides a principled way to perform multi-class segmentation for tasks like interactive segmentation. The method has been tested on real data showing good performance and improvements compared to standard normalized cuts.},
  author       = {Eriksson, Anders P and Olsson, Carl and Kahl, Fredrik},
  issn         = {0924-9907},
  language     = {eng},
  number       = {1},
  pages        = {45--61},
  publisher    = {Springer},
  series       = {Journal of Mathematical Imaging and Vision},
  title        = {Normalized Cuts Revisited: A Reformulation for Segmentation with Linear Grouping Constraints},
  url          = {http://dx.doi.org/10.1007/s10851-010-0223-5},
  volume       = {39},
  year         = {2011},
}