NDLI: Multi-layer group sparse coding — For concurrent image classification and annotation

Content Provider	IEEE Xplore Digital Library
Author	Shenghua Gao Liang-Tien Chia Tsang, I.W.-H.
Copyright Year	2011
Description	Author affiliation: School of Computer Engineering, Nanyang Technological University, Singapore (Shenghua Gao; Liang-Tien Chia; Tsang, I.W.-H.)
Abstract	We present a multi-layer group sparse coding framework for concurrent image classification and annotation. By leveraging the dependency between image class label and tags, we introduce a multi-layer group sparse structure of the reconstruction coefficients. Such structure fully encodes the mutual dependency between the class label, which describes the image content as a whole, and tags, which describe the components of the image content. Then we propose a multi-layer group based tag propagation method, which combines the class label and subgroups of instances with similar tag distribution to annotate test images. Moreover, we extend our multi-layer group sparse coding in the Reproducing Kernel Hilbert Space (RKHS) which captures the nonlinearity of features, and further improves performances of image classification and annotation. Experimental results on the LabelMe, UIUC-Sport and NUS-WIDE-Object databases show that our method outperforms the baseline methods, and achieves excellent performances in both image classification and annotation tasks.
Starting Page	2809
Ending Page	2816
File Size	5147736
Page Count	8
File Format	PDF
ISBN	9781457703942
ISSN	10636919
e-ISBN	9781457703959
DOI	10.1109/CVPR.2011.5995454
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2011-06-20
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Encoding Image reconstruction Kernel Image coding Equations Training data Rocks
Content Type	Text
Resource Type	Article
Subject	Computer Vision and Pattern Recognition Software

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