Local image tagging via graph regularized joint group sparsity

Yang, Yang, Huang, Zi, Yang, Yi, Liu, Jiajun, Shen, Heng Tao and Luo, Jiebo (2013) Local image tagging via graph regularized joint group sparsity. Pattern Recognition, 46 5: 1358-1368. doi:10.1016/j.patcog.2012.10.026

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Author Yang, Yang
Huang, Zi
Yang, Yi
Liu, Jiajun
Shen, Heng Tao
Luo, Jiebo
Title Local image tagging via graph regularized joint group sparsity
Journal name Pattern Recognition   Check publisher's open access policy
ISSN 0031-3203
Publication date 2013-05
Year available 2012
Sub-type Article (original research)
DOI 10.1016/j.patcog.2012.10.026
Open Access Status
Volume 46
Issue 5
Start page 1358
End page 1368
Total pages 11
Place of publication Oxford, United Kingdom
Publisher Pergamon
Collection year 2013
Language eng
Formatted abstract
In recent years, massive amounts of web image data have been emerging on the web. How to precisely label these images is critical and challenging to modern image search engines. Due to the fact that web image contents are more and more complex, existing image-level tagging methods may become less effective and hardly achieve satisfactory performance. This raises an urgent need for the fine-grained tagging, e.g., region-level tagging. In this work, we study how to establish mapping between tags and image regions. In particular, a novel hierarchical local image tagging method is proposed to simultaneously assign tags to all the regions within the same image. We propose a Laplacian Joint Group Lasso (LJGL) model to jointly reconstruct the regions within a test image with a set of labeled training data. The LJGL model not only considers the robust encoding ability of joint group lasso but also preserves local structural information embedded in test regions. Besides, we extend the LJGL model to a kernel version in order to achieve the non-linear reconstruction. An effective algorithm is devised to optimize the objective function of the proposed model. Tags of training data are propagated to the reconstructed regions according to the reconstruction coefficients. Extensive experiments on four public image datasets demonstrate that our proposed models achieve significant performance improvements over the state-of-the-art methods in local image tagging.
Keyword Local image tagging
Group sparse coding
Graph regularization
Tag propagation
Q-Index Code C1
Q-Index Status Confirmed Code
Institutional Status UQ
Additional Notes Published online: 20 November 2012.

Document type: Journal Article
Sub-type: Article (original research)
Collections: Official 2013 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 28 times in Scopus Article | Citations
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