Model order selection and cue combination for image segmentation
Research output: Contribution to journal › Conference article › Research › peer-review
Standard
Model order selection and cue combination for image segmentation. / Rabinovich, Andrew; Lange, Tilman; Buhmann, Joachim M.; Belongie, Serge.
In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2006, p. 1130-1137.Research output: Contribution to journal › Conference article › Research › peer-review
Harvard
APA
Vancouver
Author
Bibtex
}
RIS
TY - GEN
T1 - Model order selection and cue combination for image segmentation
AU - Rabinovich, Andrew
AU - Lange, Tilman
AU - Buhmann, Joachim M.
AU - Belongie, Serge
PY - 2006
Y1 - 2006
N2 - Model order selection and cue combination are both difficult open problems in the area of clustering. In this work we build upon stability-based approaches to develop a new method for automatic model order selection and cue combination with applications to visual grouping. Novel features of our approach include the ability to detect multiple stable clusterings (instead of only one), a simpler means of calculating stability that does not require training a classifier, and a new characterization of the space of stabilities for a continuum of segmentations that provides for an efficient sampling scheme. Our contribution is a framework for visual grouping that frees the user from the hassles of parameter tuning and model order selection: the input is an image, the output is a shortlist of segmentations.
AB - Model order selection and cue combination are both difficult open problems in the area of clustering. In this work we build upon stability-based approaches to develop a new method for automatic model order selection and cue combination with applications to visual grouping. Novel features of our approach include the ability to detect multiple stable clusterings (instead of only one), a simpler means of calculating stability that does not require training a classifier, and a new characterization of the space of stabilities for a continuum of segmentations that provides for an efficient sampling scheme. Our contribution is a framework for visual grouping that frees the user from the hassles of parameter tuning and model order selection: the input is an image, the output is a shortlist of segmentations.
UR - http://www.scopus.com/inward/record.url?scp=33845598103&partnerID=8YFLogxK
U2 - 10.1109/CVPR.2006.186
DO - 10.1109/CVPR.2006.186
M3 - Conference article
AN - SCOPUS:33845598103
SP - 1130
EP - 1137
JO - I E E E Conference on Computer Vision and Pattern Recognition. Proceedings
JF - I E E E Conference on Computer Vision and Pattern Recognition. Proceedings
SN - 1063-6919
T2 - 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR 2006
Y2 - 17 June 2006 through 22 June 2006
ER -
ID: 302053587