Optimized graph learning using partial tags and multiple features for image and video annotation

Song, Jingkuan, Gao, Lianli, Nie, Feiping, Shen, Heng Tao, Yan, Yan and Sebe, Nicu (2016) Optimized graph learning using partial tags and multiple features for image and video annotation. IEEE Transactions on Image Processing, 25 11: 4999-5011. doi:10.1109/TIP.2016.2601260

Author Song, Jingkuan
Gao, Lianli
Nie, Feiping
Shen, Heng Tao
Yan, Yan
Sebe, Nicu
Title Optimized graph learning using partial tags and multiple features for image and video annotation
Journal name IEEE Transactions on Image Processing   Check publisher's open access policy
ISSN 1057-7149
Publication date 2016-11-01
Sub-type Article (original research)
DOI 10.1109/TIP.2016.2601260
Open Access Status Not yet assessed
Volume 25
Issue 11
Start page 4999
End page 5011
Total pages 13
Place of publication Piscataway, NJ 08854 United States
Publisher Institute of Electrical and Electronics Engineers
Language eng
Abstract In multimedia annotation, due to the time constraints and the tediousness of manual tagging, it is quite common to utilize both tagged and untagged data to improve the performance of supervised learning when only limited tagged training data are available. This is often done by adding a geometry-based regularization term in the objective function of a supervised learning model. In this case, a similarity graph is indispensable to exploit the geometrical relationships among the training data points, and the graph construction scheme essentially determines the performance of these graph-based learning algorithms. However, most of the existing works construct the graph empirically and are usually based on a single feature without using the label information. In this paper, we propose a semi-supervised annotation approach by learning an optimized graph (OGL) from multi-cues (i.e., partial tags and multiple features), which can more accurately embed the relationships among the data points. Since OGL is a transductive method and cannot deal with novel data points, we further extend our model to address the out-of-sample issue. Extensive experiments on image and video annotation show the consistent superiority of OGL over the state-of-the-art methods.
Keyword Graph learning
Image and video annotation
Semi-supervised learning
Q-Index Code C1
Q-Index Status Provisional Code
Institutional Status UQ

Document type: Journal Article
Sub-type: Article (original research)
Collections: HERDC Pre-Audit
School of Information Technology and Electrical Engineering Publications
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Citation counts: TR Web of Science Citation Count  Cited 7 times in Thomson Reuters Web of Science Article | Citations
Scopus Citation Count Cited 14 times in Scopus Article | Citations
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