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Conference Paper/Proceeding/Abstract 1340 views 318 downloads

Local Representation Learning with A Convolutional Autoencoder

Michael P. Kenning, Xianghua Xie Orcid Logo, Michael Edwards, Jingjing Deng

2018 25th IEEE International Conference on Image Processing (ICIP), Pages: 3239 - 3243

Swansea University Authors: Xianghua Xie Orcid Logo, Jingjing Deng

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DOI (Published version): 10.1109/ICIP.2018.8451233

Abstract

We propose a clustering approach embedded in deep convolutional auto-encoder. In contrast to conventional clustering approaches, our method simultaneously learns feature representation and cluster assignment through deep convolutional auto-encoder.

Published in: 2018 25th IEEE International Conference on Image Processing (ICIP)
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/cronfa40806
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first_indexed 2018-06-23T19:33:39Z
last_indexed 2018-09-24T18:51:22Z
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spelling 2018-09-24T16:20:42.6328723 v2 40806 2018-06-23 Local Representation Learning with A Convolutional Autoencoder b334d40963c7a2f435f06d2c26c74e11 0000-0002-2701-8660 Xianghua Xie Xianghua Xie true false 6f6d01d585363d6dc1622640bb4fcb3f Jingjing Deng Jingjing Deng true false 2018-06-23 SCS We propose a clustering approach embedded in deep convolutional auto-encoder. In contrast to conventional clustering approaches, our method simultaneously learns feature representation and cluster assignment through deep convolutional auto-encoder. Conference Paper/Proceeding/Abstract 2018 25th IEEE International Conference on Image Processing (ICIP) 3239 3243 2018 IEEE International Conference on Image Processing Athens, Greece 2381-8549 7 10 2018 2018-10-07 10.1109/ICIP.2018.8451233 https://ieeexplore.ieee.org/document/8451233/ COLLEGE NANME Computer Science COLLEGE CODE SCS Swansea University 2018-09-24T16:20:42.6328723 2018-06-23T15:46:38.4597825 Faculty of Science and Engineering School of Mathematics and Computer Science - Computer Science Michael P. Kenning 1 Xianghua Xie 0000-0002-2701-8660 2 Michael Edwards 3 Jingjing Deng 4 0040806-23062018154737.pdf conference_071817.pdf 2018-06-23T15:47:37.2100000 Output 921994 application/pdf Accepted Manuscript true 2019-10-07T00:00:00.0000000 true eng
title Local Representation Learning with A Convolutional Autoencoder
spellingShingle Local Representation Learning with A Convolutional Autoencoder
Xianghua Xie
Jingjing Deng
title_short Local Representation Learning with A Convolutional Autoencoder
title_full Local Representation Learning with A Convolutional Autoencoder
title_fullStr Local Representation Learning with A Convolutional Autoencoder
title_full_unstemmed Local Representation Learning with A Convolutional Autoencoder
title_sort Local Representation Learning with A Convolutional Autoencoder
author_id_str_mv b334d40963c7a2f435f06d2c26c74e11
6f6d01d585363d6dc1622640bb4fcb3f
author_id_fullname_str_mv b334d40963c7a2f435f06d2c26c74e11_***_Xianghua Xie
6f6d01d585363d6dc1622640bb4fcb3f_***_Jingjing Deng
author Xianghua Xie
Jingjing Deng
author2 Michael P. Kenning
Xianghua Xie
Michael Edwards
Jingjing Deng
format Conference Paper/Proceeding/Abstract
container_title 2018 25th IEEE International Conference on Image Processing (ICIP)
container_start_page 3239
publishDate 2018
institution Swansea University
issn 2381-8549
doi_str_mv 10.1109/ICIP.2018.8451233
publisher 2018 IEEE International Conference on Image Processing
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/8451233/
document_store_str 1
active_str 0
description We propose a clustering approach embedded in deep convolutional auto-encoder. In contrast to conventional clustering approaches, our method simultaneously learns feature representation and cluster assignment through deep convolutional auto-encoder.
published_date 2018-10-07T03:51:57Z
_version_ 1763752560889430016
score 11.037166

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