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A Deep Convolutional Auto-Encoder with Embedded Clustering

A. Alqahtani, X. Xie, J. Deng, M.W. Jones, Mark Jones Orcid Logo, Xianghua Xie Orcid Logo, Jingjing Deng

2018 25th IEEE International Conference on Image Processing (ICIP), Pages: 4058 - 4062

Swansea University Authors: Mark Jones Orcid Logo, Xianghua Xie Orcid Logo, Jingjing Deng

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DOI (Published version): 10.1109/ICIP.2018.8451506

Abstract

In this paper, we propose a clustering approach embedded in a deep convolutional auto-encoder (DCAE). In contrast to conventional clustering approaches, our method simultaneously learns feature representations and cluster assignments through DCAEs. DCAEs have been effective in image processing as it...

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Published in: 2018 25th IEEE International Conference on Image Processing (ICIP)
ISBN: 978-1-4799-7062-9 978-1-4799-7061-2
ISSN: 2381-8549
Published: Megaron Athens International Conference Centre, Athens, Greece 25th IEEE International Conference on Image Processing (ICIP) 2018
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URI: https://cronfa.swan.ac.uk/Record/cronfa40682
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spelling 2019-03-28T12:53:26.2078458 v2 40682 2018-06-08 A Deep Convolutional Auto-Encoder with Embedded Clustering 2e1030b6e14fc9debd5d5ae7cc335562 0000-0001-8991-1190 Mark Jones Mark Jones true false b334d40963c7a2f435f06d2c26c74e11 0000-0002-2701-8660 Xianghua Xie Xianghua Xie true false 6f6d01d585363d6dc1622640bb4fcb3f Jingjing Deng Jingjing Deng true false 2018-06-08 SCS In this paper, we propose a clustering approach embedded in a deep convolutional auto-encoder (DCAE). In contrast to conventional clustering approaches, our method simultaneously learns feature representations and cluster assignments through DCAEs. DCAEs have been effective in image processing as it fully utilizes the properties of convolutional neural networks. Our method consists of clustering and reconstruction objective functions. All data points are assigned to their new corresponding cluster centers during the optimization, after that, clustering centers are iteratively updated to obtain a stable performance of clustering. The experimental results on the MNIST dataset show that the proposed method substantially outperforms deep clustering models in term of clustering quality. Conference Paper/Proceeding/Abstract 2018 25th IEEE International Conference on Image Processing (ICIP) 4058 4062 25th IEEE International Conference on Image Processing (ICIP) Megaron Athens International Conference Centre, Athens, Greece 978-1-4799-7062-9 978-1-4799-7061-2 2381-8549 Deep Learning, Deep Convolutional Auto-Encoder, Embedded Clustering 7 10 2018 2018-10-07 10.1109/ICIP.2018.8451506 https://ieeexplore.ieee.org/document/8451506 COLLEGE NANME Computer Science COLLEGE CODE SCS Swansea University 2019-03-28T12:53:26.2078458 2018-06-08T12:33:46.4447847 Faculty of Science and Engineering School of Mathematics and Computer Science - Computer Science A. Alqahtani 1 X. Xie 2 J. Deng 3 M.W. Jones 4 Mark Jones 0000-0001-8991-1190 5 Xianghua Xie 0000-0002-2701-8660 6 Jingjing Deng 7 0040682-08062018123741.pdf 2018_DeepCAE.pdf 2018-06-08T12:37:41.8830000 Output 773745 application/pdf Accepted Manuscript true 2019-09-06T00:00:00.0000000 © 2019 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. true eng
title A Deep Convolutional Auto-Encoder with Embedded Clustering
spellingShingle A Deep Convolutional Auto-Encoder with Embedded Clustering
Mark Jones
Xianghua Xie
Jingjing Deng
title_short A Deep Convolutional Auto-Encoder with Embedded Clustering
title_full A Deep Convolutional Auto-Encoder with Embedded Clustering
title_fullStr A Deep Convolutional Auto-Encoder with Embedded Clustering
title_full_unstemmed A Deep Convolutional Auto-Encoder with Embedded Clustering
title_sort A Deep Convolutional Auto-Encoder with Embedded Clustering
author_id_str_mv 2e1030b6e14fc9debd5d5ae7cc335562
b334d40963c7a2f435f06d2c26c74e11
6f6d01d585363d6dc1622640bb4fcb3f
author_id_fullname_str_mv 2e1030b6e14fc9debd5d5ae7cc335562_***_Mark Jones
b334d40963c7a2f435f06d2c26c74e11_***_Xianghua Xie
6f6d01d585363d6dc1622640bb4fcb3f_***_Jingjing Deng
author Mark Jones
Xianghua Xie
Jingjing Deng
author2 A. Alqahtani
X. Xie
J. Deng
M.W. Jones
Mark Jones
Xianghua Xie
Jingjing Deng
format Conference Paper/Proceeding/Abstract
container_title 2018 25th IEEE International Conference on Image Processing (ICIP)
container_start_page 4058
publishDate 2018
institution Swansea University
isbn 978-1-4799-7062-9
978-1-4799-7061-2
issn 2381-8549
doi_str_mv 10.1109/ICIP.2018.8451506
publisher 25th IEEE International Conference on Image Processing (ICIP)
college_str Faculty of Science and Engineering
hierarchytype
hierarchy_top_id facultyofscienceandengineering
hierarchy_top_title Faculty of Science and Engineering
hierarchy_parent_id facultyofscienceandengineering
hierarchy_parent_title Faculty of Science and Engineering
department_str School of Mathematics and Computer Science - Computer Science{{{_:::_}}}Faculty of Science and Engineering{{{_:::_}}}School of Mathematics and Computer Science - Computer Science
url https://ieeexplore.ieee.org/document/8451506
document_store_str 1
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description In this paper, we propose a clustering approach embedded in a deep convolutional auto-encoder (DCAE). In contrast to conventional clustering approaches, our method simultaneously learns feature representations and cluster assignments through DCAEs. DCAEs have been effective in image processing as it fully utilizes the properties of convolutional neural networks. Our method consists of clustering and reconstruction objective functions. All data points are assigned to their new corresponding cluster centers during the optimization, after that, clustering centers are iteratively updated to obtain a stable performance of clustering. The experimental results on the MNIST dataset show that the proposed method substantially outperforms deep clustering models in term of clustering quality.
published_date 2018-10-07T03:51:47Z
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score 11.013015

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