MSM: Multi-stage Multicuts for Scalable Image Clustering
- Correlation Clustering, also called the minimum cost Multicut problem, is the process of grouping data by pairwise similarities. It has proven to be effective on clustering problems, where the number of classes is unknown. However, not only is the Multicut problem NP-hard, an undirected graph G with n vertices representing single images has at most edges, thus making it challenging to implementCorrelation Clustering, also called the minimum cost Multicut problem, is the process of grouping data by pairwise similarities. It has proven to be effective on clustering problems, where the number of classes is unknown. However, not only is the Multicut problem NP-hard, an undirected graph G with n vertices representing single images has at most edges, thus making it challenging to implement correlation clustering for large datasets. In this work, we propose Multi-Stage Multicuts (MSM) as a scalable approach for image clustering. Specifically, we solve minimum cost Multicut problems across multiple distributed compute units. Our approach not only allows to solve problem instances which are too large to fit into the shared memory of a single compute node, but it also achieves significant speedups while preserving the clustering accuracy at the same time. We evaluate our proposed method on the CIFAR10 ……
Document Type: | Conference Proceeding |
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Conference Type: | Konferenzartikel |
Zitierlink: | https://opus.hs-offenburg.de/5296 | Bibliografische Angaben |
Title (English): | MSM: Multi-stage Multicuts for Scalable Image Clustering |
Conference: | ISC High Performance Digital 2021 International Workshops, Frankfurt am Main, Germany, June 24-July 2 2021 |
Author: | Kalun Ho, Avraam Chatzimichailidis, Margret Keuper, Janis KeuperStaff MemberORCiDGND |
Edition: | 1. |
Year of Publication: | 2021 |
Place of publication: | Cham |
Publisher: | Springer |
First Page: | 267 |
Last Page: | 284 |
Parent Title (English): | High Performance Computing |
Editor: | Heike Jagode, Hartwig Anzt, Hatem Ltaief, Piotr Luszczek |
Volume: | LNCS 12761 |
ISBN: | 978-3-030-90538-5 (Print) |
ISBN: | 978-3-030-90539-2 (Online) |
ISSN: | 0302-9743 (Print) |
ISSN: | 1611-3349 (Online) |
DOI: | https://doi.org/10.1007/978-3-030-90539-2_18 |
URL: | https://link.springer.com/chapter/10.1007/978-3-030-90539-2_18 |
Language: | English | Inhaltliche Informationen |
Institutes: | Fakultät Elektrotechnik, Medizintechnik und Informatik (EMI) (ab 04/2019) |
Forschung / IMLA - Institute for Machine Learning and Analytics | |
Institutes: | Bibliografie | Formale Angaben |
Open Access: | Closed Access |
Licence (German): | Urheberrechtlich geschützt |