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

Rabinovich, A, Lange, T, Buhmann, JM & Belongie, S 2006, 'Model order selection and cue combination for image segmentation', Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 1130-1137. https://doi.org/10.1109/CVPR.2006.186

APA

Rabinovich, A., Lange, T., Buhmann, J. M., & Belongie, S. (2006). Model order selection and cue combination for image segmentation. Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 1130-1137. https://doi.org/10.1109/CVPR.2006.186

Vancouver

Rabinovich A, Lange T, Buhmann JM, Belongie S. Model order selection and cue combination for image segmentation. Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition. 2006;1130-1137. https://doi.org/10.1109/CVPR.2006.186

Author

Rabinovich, Andrew ; Lange, Tilman ; Buhmann, Joachim M. ; Belongie, Serge. / Model order selection and cue combination for image segmentation. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition. 2006 ; pp. 1130-1137.

Bibtex

@inproceedings{c8e6bad40bf04347bda5dd5e8390a5fd,
title = "Model order selection and cue combination for image segmentation",
abstract = "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.",
author = "Andrew Rabinovich and Tilman Lange and Buhmann, {Joachim M.} and Serge Belongie",
year = "2006",
doi = "10.1109/CVPR.2006.186",
language = "English",
pages = "1130--1137",
journal = "I E E E Conference on Computer Vision and Pattern Recognition. Proceedings",
issn = "1063-6919",
publisher = "Institute of Electrical and Electronics Engineers",
note = "2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR 2006 ; Conference date: 17-06-2006 Through 22-06-2006",

}

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