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A Deep Convolutional Auto-Encoder with Embedded Clustering / A. Alqahtani; X. Xie; J. Deng; M.W. Jones

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

Swansea University Author: Xie, Xianghua

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Abstract

We present a graph-based convolutional autoencoder and assess the contribution of four components towards encoding quality. A graph-based convolution-operator is used to learn localised filtering operations for graph-wise encoding. An evaluation of the proposed method is provided on a topologically-...

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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: Athens, Greece 2018 IEEE International Conference on Image Processing 2018
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URI: https://cronfa.swan.ac.uk/Record/cronfa40805
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last_indexed 2019-03-29T12:26:49Z
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spelling 2019-03-28T12:52:14Z v2 40805 2018-06-23 A Deep Convolutional Auto-Encoder with Embedded Clustering Xianghua Xie Xianghua Xie true 0000-0002-2701-8660 false b334d40963c7a2f435f06d2c26c74e11 53b7e8cec1e3c035df428f36f80bdea5 ulOdsUw0nzyNlMFzZoDyVp320YwKTXZRCaAvm14NMEw= 2018-06-23 SCS We present a graph-based convolutional autoencoder and assess the contribution of four components towards encoding quality. A graph-based convolution-operator is used to learn localised filtering operations for graph-wise encoding. An evaluation of the proposed method is provided on a topologically-irregular version of MNIST that violates the assumption made by conventional convolutional autoencoder methods of the structure of its input-data. Conference contribution 2018 25th IEEE International Conference on Image Processing (ICIP) 4058 4062 2018 IEEE International Conference on Image Processing Athens, Greece 978-1-4799-7062-9 978-1-4799-7061-2 2381-8549 Graph learning, deep learning, autoencoder, irregular domain learning 7 10 2018 2018-10-07 10.1109/ICIP.2018.8451506 https://ieeexplore.ieee.org/document/8451506/ College of Science Computer Science CSCI SCS Visual Computing None 2019-03-28T12:52:14Z 2018-06-23T15:41:38Z College of Science Computer Science A. Alqahtani 1 X. Xie 2 J. Deng 3 M.W. Jones 4 0040805-23062018154319.pdf post-publication-revision.pdf 2018-06-23T15:43:19Z Output 271473 application/pdf AM true Updated Embargo 28/03/2019 2019-09-06T00:00:00 © 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
Xie, Xianghua
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 b334d40963c7a2f435f06d2c26c74e11
author_id_fullname_str_mv b334d40963c7a2f435f06d2c26c74e11_***_Xie, Xianghua
author Xie, Xianghua
author2 A. Alqahtani
X. Xie
J. Deng
M.W. Jones
format Conference contribution
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 2018 IEEE International Conference on Image Processing
college_str College of Science
hierarchytype
hierarchy_top_id collegeofscience
hierarchy_top_title College of Science
hierarchy_parent_id collegeofscience
hierarchy_parent_title College of Science
department_str Computer Science{{{_:::_}}}College of Science{{{_:::_}}}Computer Science
url https://ieeexplore.ieee.org/document/8451506/
document_store_str 1
active_str 1
researchgroup_str Visual Computing
description We present a graph-based convolutional autoencoder and assess the contribution of four components towards encoding quality. A graph-based convolution-operator is used to learn localised filtering operations for graph-wise encoding. An evaluation of the proposed method is provided on a topologically-irregular version of MNIST that violates the assumption made by conventional convolutional autoencoder methods of the structure of its input-data.
published_date 2018-10-07T05:05:23Z
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score 10.884177