SLNSW-UTS: A Historical Image Dataset for Image Multi-Labeling and Retrieval

Zhang, J; Lu, J; Shen, C; Curr, K; Phua, R; Neville, R; Edmonds, E

SLNSW-UTS: A Historical Image Dataset for Image Multi-Labeling and Retrieval

Zhang, J

Lu, J Shen, C Curr, K Phua, R Neville, R Edmonds, E

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Publication Type:: Conference Proceeding
Citation:: 2016 International Conference on Digital Image Computing: Techniques and Applications, DICTA 2016, 2016
Issue Date:: 2016-12-22

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Field	Value	Language
dc.contributor.author	Zhang, J https://orcid.org/0000-0002-0033-0494	en_US
dc.contributor.author	Lu, J	en_US
dc.contributor.author	Shen, C	en_US
dc.contributor.author	Curr, K	en_US
dc.contributor.author	Phua, R	en_US
dc.contributor.author	Neville, R	en_US
dc.contributor.author	Edmonds, E	en_US
dc.date.issued	2016-12-22	en_US
dc.identifier.citation	2016 International Conference on Digital Image Computing: Techniques and Applications, DICTA 2016, 2016	en_US
dc.identifier.isbn	9781509028962	en_US
dc.identifier.uri	http://hdl.handle.net/10453/122836
dc.description.abstract	© 2016 IEEE. This paper introduces a dataset of historical images created by the State Library of New South Wales and the University of Technology Sydney (UTS). The dataset has a total of 29713 images with 119 unique labels. Each image contains multiple labels. We use a CNN-based framework to explore the feasibility of our dataset in image multi-labeling and retrieval research, and extract semantic level image features for future research use. The experiment results illustrate that effective deep learning models can be trained on our dataset. We also introduce five applications that can be studied on our historical image dataset.	en_US
dc.relation.ispartof	2016 International Conference on Digital Image Computing: Techniques and Applications, DICTA 2016	en_US
dc.relation.isbasedon	10.1109/DICTA.2016.7797084	en_US
dc.rights	info:eu-repo/semantics/closedAccess
dc.title	SLNSW-UTS: A Historical Image Dataset for Image Multi-Labeling and Retrieval	en_US
dc.type	Conference Proceeding
utslib.for	080101 Adaptive Agents and Intelligent Robotics	en_US
utslib.for	080110 Simulation and Modelling	en_US
utslib.for	080109 Pattern Recognition and Data Mining	en_US
pubs.embargo.period	Not known	en_US
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Electrical and Data Engineering
pubs.organisational-group	/University of Technology Sydney/Strength - GBDTC - Global Big Data Technologies
pubs.organisational-group	/University of Technology Sydney/Students
utslib.copyright.status	closed_access	*
pubs.publication-status	Published	en_US

Abstract:

© 2016 IEEE. This paper introduces a dataset of historical images created by the State Library of New South Wales and the University of Technology Sydney (UTS). The dataset has a total of 29713 images with 119 unique labels. Each image contains multiple labels. We use a CNN-based framework to explore the feasibility of our dataset in image multi-labeling and retrieval research, and extract semantic level image features for future research use. The experiment results illustrate that effective deep learning models can be trained on our dataset. We also introduce five applications that can be studied on our historical image dataset.

Please use this identifier to cite or link to this item:

http://hdl.handle.net/10453/122836