Beyond pairwise clustering

Research output: Contribution to journal › Conference article › Research › peer-review

Standard

Beyond pairwise clustering. / Agarwal, Sameer; Lim, Jongwoo; Zelnik-Manor, Lihi; Perona, Pietro; Kriegman, David; Belongie, Serge.

In: Proceedings - 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR 2005, 2005, p. 838-845.

Research output: Contribution to journal › Conference article › Research › peer-review

Harvard

Agarwal, S, Lim, J, Zelnik-Manor, L, Perona, P, Kriegman, D & Belongie, S 2005, 'Beyond pairwise clustering', Proceedings - 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR 2005, pp. 838-845. https://doi.org/10.1109/CVPR.2005.89

APA

Agarwal, S., Lim, J., Zelnik-Manor, L., Perona, P., Kriegman, D., & Belongie, S. (2005). Beyond pairwise clustering. Proceedings - 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR 2005, 838-845. https://doi.org/10.1109/CVPR.2005.89

Vancouver

Agarwal S, Lim J, Zelnik-Manor L, Perona P, Kriegman D, Belongie S. Beyond pairwise clustering. Proceedings - 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR 2005. 2005;838-845. https://doi.org/10.1109/CVPR.2005.89

Author

Agarwal, Sameer ; Lim, Jongwoo ; Zelnik-Manor, Lihi ; Perona, Pietro ; Kriegman, David ; Belongie, Serge. / Beyond pairwise clustering. In: Proceedings - 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR 2005. 2005 ; pp. 838-845.

Bibtex

@inproceedings{1c8d51a598bc4b11bb51a40d218c65b9,
title = "Beyond pairwise clustering",
abstract = "We consider the problem of clustering in domains where the affinity relations are not dyadic (pairwise), but rather triadic, tetradic or higher. The problem is an instance of the hypergraph partitioning problem. We propose a two-step algorithm for solving this problem. In the first step we use a novel scheme to approximate the hypergraph using a weighted graph. In the second step a spectral partitioning algorithm is used to partition the vertices of this graph. The algorithm is capable of handling hyperedges of all orders including order two, thus incorporating information of all orders simultaneously. We present a theoretical analysis that relates our algorithm to an existing hypergraph partitioning algorithm and explain the reasons for its superior performance. We report the performance of our algorithm on a variety of computer vision problems and compare it to several existing hypergraph partitioning algorithms.",
author = "Sameer Agarwal and Jongwoo Lim and Lihi Zelnik-Manor and Pietro Perona and David Kriegman and Serge Belongie",
year = "2005",
doi = "10.1109/CVPR.2005.89",
language = "English",
pages = "838--845",
journal = "Proceedings - 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR 2005",
note = "2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR 2005 ; Conference date: 20-06-2005 Through 25-06-2005",

}

RIS

TY - GEN

T1 - Beyond pairwise clustering

AU - Agarwal, Sameer

AU - Lim, Jongwoo

AU - Zelnik-Manor, Lihi

AU - Perona, Pietro

AU - Kriegman, David

AU - Belongie, Serge

PY - 2005

Y1 - 2005

N2 - We consider the problem of clustering in domains where the affinity relations are not dyadic (pairwise), but rather triadic, tetradic or higher. The problem is an instance of the hypergraph partitioning problem. We propose a two-step algorithm for solving this problem. In the first step we use a novel scheme to approximate the hypergraph using a weighted graph. In the second step a spectral partitioning algorithm is used to partition the vertices of this graph. The algorithm is capable of handling hyperedges of all orders including order two, thus incorporating information of all orders simultaneously. We present a theoretical analysis that relates our algorithm to an existing hypergraph partitioning algorithm and explain the reasons for its superior performance. We report the performance of our algorithm on a variety of computer vision problems and compare it to several existing hypergraph partitioning algorithms.

AB - We consider the problem of clustering in domains where the affinity relations are not dyadic (pairwise), but rather triadic, tetradic or higher. The problem is an instance of the hypergraph partitioning problem. We propose a two-step algorithm for solving this problem. In the first step we use a novel scheme to approximate the hypergraph using a weighted graph. In the second step a spectral partitioning algorithm is used to partition the vertices of this graph. The algorithm is capable of handling hyperedges of all orders including order two, thus incorporating information of all orders simultaneously. We present a theoretical analysis that relates our algorithm to an existing hypergraph partitioning algorithm and explain the reasons for its superior performance. We report the performance of our algorithm on a variety of computer vision problems and compare it to several existing hypergraph partitioning algorithms.

UR - http://www.scopus.com/inward/record.url?scp=24644523726&partnerID=8YFLogxK

U2 - 10.1109/CVPR.2005.89

DO - 10.1109/CVPR.2005.89

M3 - Conference article

AN - SCOPUS:24644523726

SP - 838

EP - 845

JO - Proceedings - 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR 2005

JF - Proceedings - 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR 2005

T2 - 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR 2005

Y2 - 20 June 2005 through 25 June 2005

ER -

ID: 302055002