UM
Patch-Set-Based Representation for Alignment-Free Image Set Classification
Gao, Shenghua1; Zeng, Zinan2; Jia, Kui4; Chan, Tsung-Han3; Tang, Jinhui5
2016-09-01
Source PublicationIEEE Transactions on Circuits and Systems for Video Technology
ISSN10518215
Volume26Issue:9Pages:1646-1658
AbstractThis paper presents a patch-set-based sparse representation for image set classification. Compared with image-based image set representation, our patch-set-based representation is alignment free and thus has an advantage for tasks like video-based face recognition, image-set-based object recognition, and video-based hand gesture recognition, where precious alignment is usually difficult or even impossible due to large variance in view angle or pose. Specifically, to bypass the alignment issue, we propose to adopt the patch-based image set representation by dividing each image within each set into patches, then we cluster all the training patches into multiple clusters and classify the test patches based on the cluster centers of training patches. The labels of test patches within each cluster are inferred from a patch-set-based sparse representation for classification, and the labels of all test patches from all the clusters are then aggregated to predict a single label for the test set. Experimental results on video-based face recognition data sets (CMU-MoBo and YouTube Celebrities), image-set-based object recognition data set (ETH-80), and video-based hand gesture recognition data set (Kinect Hand Gestures) demonstrate that our proposed method consistently outperforms all existing ones, and the improvement is very significant on the YouTube Celebrities and Kinect Hand Gesture data sets. Moreover, we also quantitatively show the robustness of our method to misalignment on the Mutli-PIE data set. © 2016 IEEE.
DOI10.1109/TCSVT.2015.2469571
Language英语
The Source to ArticleEngineering Village
Fulltext Access
Citation statistics
Cited Times [WOS]:1   [WOS Record]     [Related Records in WOS]
Document TypeJournal article
CollectionUniversity of Macau
Affiliation1.Shanghai Tech University, Shanghai; 200031, China;
2.Advanced Digital Sciences Center, Singapore; 138632, Singapore;
3.MediaTek Inc, Hsinchu; 30078, Taiwan;
4.University of Macau, 999078, China;
5.Nanjing University of Science and Technology, Nanjing; 210094, China
Recommended Citation
GB/T 7714
Gao, Shenghua,Zeng, Zinan,Jia, Kui,et al. Patch-Set-Based Representation for Alignment-Free Image Set Classification[J]. IEEE Transactions on Circuits and Systems for Video Technology,2016,26(9):1646-1658.
APA Gao, Shenghua,Zeng, Zinan,Jia, Kui,Chan, Tsung-Han,&Tang, Jinhui.(2016).Patch-Set-Based Representation for Alignment-Free Image Set Classification.IEEE Transactions on Circuits and Systems for Video Technology,26(9),1646-1658.
MLA Gao, Shenghua,et al."Patch-Set-Based Representation for Alignment-Free Image Set Classification".IEEE Transactions on Circuits and Systems for Video Technology 26.9(2016):1646-1658.
Files in This Item:
There are no files associated with this item.
Related Services
Recommend this item
Bookmark
Usage statistics
Export to Endnote
Google Scholar
Similar articles in Google Scholar
[Gao, Shenghua]'s Articles
[Zeng, Zinan]'s Articles
[Jia, Kui]'s Articles
Baidu academic
Similar articles in Baidu academic
[Gao, Shenghua]'s Articles
[Zeng, Zinan]'s Articles
[Jia, Kui]'s Articles
Bing Scholar
Similar articles in Bing Scholar
[Gao, Shenghua]'s Articles
[Zeng, Zinan]'s Articles
[Jia, Kui]'s Articles
Terms of Use
No data!
Social Bookmark/Share
All comments (0)
No comment.
 

Items in the repository are protected by copyright, with all rights reserved, unless otherwise indicated.