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Semi-Supervised Tensor-Based Graph Embedding Learning and Its Application to Visual Discriminant Tracking
Hu, Weiming1; Gao, Jin1; Xing, Junliang1; Zhang, Chao1; Maybank, Stephen2; Weiming Hu
Source PublicationIEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE
2017
Volume39Issue:1Pages:172-188
SubtypeArticle
AbstractAn appearance model adaptable to changes in object appearance is critical in visual object tracking. In this paper, we treat an image patch as a two-order tensor which preserves the original image structure. We design two graphs for characterizing the intrinsic local geometrical structure of the tensor samples of the object and the background. Graph embedding is used to reduce the dimensions of the tensors while preserving the structure of the graphs. Then, a discriminant embedding space is constructed. We prove two propositions for finding the transformation matrices which are used to map the original tensor samples to the tensor-based graph embedding space. In order to encode more discriminant information in the embedding space, we propose a transfer-learning-based semi-supervised strategy to iteratively adjust the embedding space into which discriminative information obtained from earlier times is transferred. We apply the proposed semi-supervised tensor-based graph embedding learning algorithm to visual tracking. The new tracking algorithm captures an object's appearance characteristics during tracking and uses a particle filter to estimate the optimal object state. Experimental results on the CVPR 2013 benchmark dataset demonstrate the effectiveness of the proposed tracking algorithm.
KeywordDiscriminant Tracking Tensor Samples Semi-supervised Learning Graph Embedding Space
WOS HeadingsScience & Technology ; Technology
DOI10.1109/TPAMI.2016.2539944
WOS KeywordOBJECT TRACKING ; DIMENSIONALITY REDUCTION ; REPRESENTATION ; FRAMEWORK
Indexed BySCI
Language英语
Funding Organization973 basic research program of China(2014CB349303) ; Natural Science Foundation of China(61472421 ; Strategic Priority Research Program of the CAS(XDB02070003) ; 61370185)
WOS Research AreaComputer Science ; Engineering
WOS SubjectComputer Science, Artificial Intelligence ; Engineering, Electrical & Electronic
WOS IDWOS:000390421300015
Citation statistics
Cited Times:9[WOS]   [WOS Record]     [Related Records in WOS]
Document Type期刊论文
Identifierhttp://ir.ia.ac.cn/handle/173211/11102
Collection模式识别国家重点实验室_视频内容安全
Corresponding AuthorWeiming Hu
Affiliation1.Chinese Acad Sci, CAS Ctr Excellence Brain Sci & Intelligence Techn, Natl Lab Pattern Recognit, Inst Automat, Beijing 100190, Peoples R China
2.Birkbeck Coll, Dept Comp Sci & Informat Syst, Malet St, London WC1E 7HX, England
First Author AffilicationChinese Acad Sci, Inst Automat, Natl Lab Pattern Recognit, Beijing 100190, Peoples R China
Recommended Citation
GB/T 7714
Hu, Weiming,Gao, Jin,Xing, Junliang,et al. Semi-Supervised Tensor-Based Graph Embedding Learning and Its Application to Visual Discriminant Tracking[J]. IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE,2017,39(1):172-188.
APA Hu, Weiming,Gao, Jin,Xing, Junliang,Zhang, Chao,Maybank, Stephen,&Weiming Hu.(2017).Semi-Supervised Tensor-Based Graph Embedding Learning and Its Application to Visual Discriminant Tracking.IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE,39(1),172-188.
MLA Hu, Weiming,et al."Semi-Supervised Tensor-Based Graph Embedding Learning and Its Application to Visual Discriminant Tracking".IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE 39.1(2017):172-188.
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