Video-to-shot tag propagation by Graph Sparse Group Lasso

Zhu, Xiaofeng, Huang, Zi, Cui, Jiangtao and Shen, Heng Tao (2013) Video-to-shot tag propagation by Graph Sparse Group Lasso. IEEE Transactions On Multimedia, 15 3: 633-646. doi:10.1109/TMM.2012.2233723

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Author Zhu, Xiaofeng
Huang, Zi
Cui, Jiangtao
Shen, Heng Tao
Title Video-to-shot tag propagation by Graph Sparse Group Lasso
Journal name IEEE Transactions On Multimedia   Check publisher's open access policy
ISSN 1520-9210
1941-0077
Publication date 2013-04
Sub-type Article (original research)
DOI 10.1109/TMM.2012.2233723
Open Access Status
Volume 15
Issue 3
Start page 633
End page 646
Total pages 14
Place of publication Piscataway, NJ, United States
Publisher Institute of Electrical and Electronics Engineers
Collection year 2014
Language eng
Abstract Traditional approaches to video tagging are designed to propagate tags at the same level, such as assigning the tags of training videos (or shots) to the test videos (or shots), such as generating tags for the test video when the training videos are associated with the tags at the video-level or assigning tags to the test shot when given a collection of annotated shots. This paper focuses on automatical shot tagging given a collection of videoswith the tags at the video-level. In other words, we aim to assign specific tags from the training videos to the test shot. The paper solves the V2S issue by assigning the test shot with the tags deriving from parts of the tags in a part of training videos. To achieve the goal, the paper first proposes a novel Graph Sparse Group Lasso (shorted for GSGL) model to linearly reconstruct the visual feature of the test shot with the visual features of the training videos, i.e., finding the correlation between the test shot and the training videos. The paper then proposes a new tagging propagation rule to assign the video-level tags to the test shot by the learnt correlations. Moreover, to effectively build the reconstruction model, the proposed GSGL simultaneously takes several constraints into account, such as the inter-group sparsity, the intra-group sparsity, the temporal-spatial prior knowledge in the training videos and the local structure of the test shot. Extensive experiments on public video datasets are conducted, which clearly demonstrate the effectiveness of the proposed method for dealing with the video-to-shot tag propagation.
Keyword Manifold learning
Sparse coding
Sparse group lasso
Structure sparsity
Video annotation
Video tagging
Q-Index Code C1
Q-Index Status Confirmed Code
Institutional Status UQ

Document type: Journal Article
Sub-type: Article (original research)
Collections: Official 2014 Collection
School of Information Technology and Electrical Engineering Publications
 
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Citation counts: TR Web of Science Citation Count  Cited 18 times in Thomson Reuters Web of Science Article | Citations
Scopus Citation Count Cited 22 times in Scopus Article | Citations
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