Please use this identifier to cite or link to this item:
https://hdl.handle.net/2440/90787
Citations | ||
Scopus | Web of Science® | Altmetric |
---|---|---|
?
|
?
|
Type: | Conference paper |
Title: | Clustering with hypergraphs: the case for large hyperedges |
Author: | Purkait, P. Chin, T. Ackermann, H. Suter, D. |
Citation: | Lecture Notes in Artificial Intelligence, 2014 / Fleet, D., Pajdla, T., Schiele, B., Tuytelaars, T. (ed./s), vol.8692 LNCS, iss.PART 4, pp.672-687 |
Publisher: | Springer International Publishing |
Issue Date: | 2014 |
Series/Report no.: | Lecture Notes in Computer Science; 8692 |
ISBN: | 9783319105925 |
ISSN: | 0302-9743 1611-3349 |
Conference Name: | 13th European Conference on Computer Vision (ECCV 2014) (6 Sep 2014 - 12 Sep 2014 : Zurich, Switzerland) |
Editor: | Fleet, D. Pajdla, T. Schiele, B. Tuytelaars, T. |
Statement of Responsibility: | Pulak Purkait, Tat-Jun Chin, Hanno Ackermann, David Suter |
Abstract: | The extension of conventional clustering to hypergraph clustering, which involves higher order similarities instead of pairwise similarities, is increasingly gaining attention in computer vision. This is due to the fact that many grouping problems require an affinity measure that must involve a subset of data of size more than two, i.e., a hyperedge. Almost all previous works, however, have considered the smallest possible hyperedge size, due to a lack of study into the potential benefits of large hyperedges and effective algorithms to generate them. In this paper, we show that large hyperedges are better from both theoretical and empirical standpoints. We then propose a novel guided sampling strategy for large hyperedges, based on the concept of random cluster models. Our method can generate pure large hyperedges that significantly improve grouping accuracy without exponential increases in sampling costs. In the important applications of face clustering and motion segmentation, our method demonstrates substantially better accuracy and efficiency. |
Keywords: | Hypergraph clustering; model fitting; guided sampling. |
Rights: | ©Springer International Publishing Switzerland 2014 |
DOI: | 10.1007/978-3-319-10593-2_44 |
Grant ID: | http://purl.org/au-research/grants/arc/DP130102524 |
Published version: | http://dx.doi.org/10.1007/978-3-319-10593-2_44 |
Appears in Collections: | Aurora harvest 2 Computer Science publications |
Files in This Item:
There are no files associated with this item.
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.