Please use this identifier to cite or link to this item: http://hdl.handle.net/2440/108536
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Type: Conference paper
Title: SLNSW-UTS: a historical image dataset for image multi-labeling and retrieval
Author: Zhang, J.
Zhang, J.
Lu, J.
Shen, C.
Curr, K.
Phua, R.
Neville, R.
Edmonds, E.
Citation: Proceedings of the International Conference on Digital Image Computing: Techniques and Applications, 2016 / pp.1-6
Publisher: IEEE
Issue Date: 2016
ISBN: 9781509028962
Conference Name: International Conference on Digital Image Computing Techniques and Applications (DICTA) (30 Nov 2016 - 02 Dec 2016 : Gold Coast, Qld)
Statement of
Responsibility: 
Junjie Zhang, Jian Zhang, Jianfeng Lu, Chunhua Shen, Kate Curr, Robin Phua, Richard Neville and Elise Edmonds
Abstract: 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.
Keywords: Historical image; multi-labeling; retrieval; dataset construction
Rights: © 2016 IEEE
RMID: 0030068875
DOI: 10.1109/DICTA.2016.7797084
Appears in Collections:Computer Science publications

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