Tag localization with spatial correlations and joint group sparsity

Yang, Yang, Yang, Yi, Huang, Zi, Shen, Heng Tao and Nie, Feiping (2011). Tag localization with spatial correlations and joint group sparsity. In: 2011 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Colorado Springs, CO, United States, (881-888). 20-25 June 2011. doi:10.1109/CVPR.2011.5995499

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Author Yang, Yang
Yang, Yi
Huang, Zi
Shen, Heng Tao
Nie, Feiping
Title of paper Tag localization with spatial correlations and joint group sparsity
Conference name IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
Conference location Colorado Springs, CO, United States
Conference dates 20-25 June 2011
Proceedings title 2011 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)   Check publisher's open access policy
Journal name 2011 Ieee Conference On Computer Vision and Pattern Recognition (cvpr)   Check publisher's open access policy
Place of Publication Piscataway, NJ, United States
Publisher IEEE
Publication Year 2011
Sub-type Fully published paper
DOI 10.1109/CVPR.2011.5995499
Open Access Status
ISBN 9781457703942
ISSN 1063-6919
Start page 881
End page 888
Total pages 8
Collection year 2012
Language eng
Abstract/Summary Nowadays numerous social images have been emerging on the Web. How to precisely label these images is critical to image retrieval. However, traditional image-level tagging methods may become less effective because global image matching approaches can hardly cope with the diversity and arbitrariness of Web image content. This raises an urgent need for the fine-grained tagging schemes. In this work, we study how to establish mapping between tags and image regions, i.e. localize tags to image regions, so as to better depict and index the content of images. We propose the spatial group sparse coding (SGSC) by extending the robust encoding ability of group sparse coding with spatial correlations among training regions. We present spatial correlations in a two-dimensional image space and design group-specific spatial kernels to produce a more interpretable regularizer. Further we propose a joint version of the SGSC model which is able to simultaneously encode a group of intrinsically related regions within a test image. An effective algorithm is developed to optimize the objective function of the Joint SGSC. The tag localization task is conducted by propagating tags from sparsely selected groups of regions to the target regions according to the reconstruction coefficients. Extensive experiments on three public image datasets illustrate that our proposed models achieve great performance improvements over the state-of-the-art method in the tag localization task.
Keyword Correlation
Encoding
Image coding
Image reconstruction
Image segmentation
Joints
Kernel
Q-Index Code E1
Q-Index Status Confirmed Code
Institutional Status UQ

 
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Created: Tue, 06 Mar 2012, 10:03:46 EST by Dr Helen Huang on behalf of School of Information Technol and Elec Engineering