Class-Oriented Self-Learning Graph Embedding for Image Compact Representation
Hu, Liangchen1; Dai, Zhenlei2; Tian, Lei3,4; Zhang, Wensheng3,4
Source PublicationIEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY
ISSN1051-8215
2023
Volume33Issue:1Pages:74-87
Corresponding AuthorZhang, Wensheng(zhangwenshengia@hotmail.com)
AbstractAs one of the learning ways for inducing efficient image compact representation, graph embedding (GE) based manifold learning has been widely developed over the last two decades. Good graph embedding depends on the construction of graphs concerning intra-class compactness and inter-class separability, which are crucial indicators of the effectiveness of a model in generating discriminative features. Unsupervised approaches are designed to reveal the data structure information from a local or global perspective, but the resulting compact representation often has poorly inter-class margins due to the lack of label information. Moreover, supervised techniques only consider enhancing the adjacency affinity within classes, but exclude the affinity of different classes, resulting in inadequate capture of marginal structures between different class distributions. To overcome these issues, we propose a learning framework that implements Class-Oriented Self-Learning Graph Embedding (COSLGE), in which we achieve a flexible low-dimensional compact representation by imposing an adaptive graph learning process across the entire data while examining the inter-class separability of low-dimensional embedding by jointly learning a linear classifier. Besides, our framework can be easily extended to semi-supervised scenarios. Extensive experiments on several widely-used benchmark databases demonstrate the effectiveness of the proposed method in comparison to some state-of-the-art approaches.
KeywordSparse matrices Manifolds Machine learning algorithms Laplace equations Heuristic algorithms Data models Data mining Adaptive graph learning separability examination marginal information preserving L-2,L-p-norm sparsity compact representation
DOI10.1109/TCSVT.2022.3197746
WOS KeywordDIMENSIONALITY REDUCTION ; PRESERVING PROJECTIONS ; FEATURE-SELECTION ; FACE RECOGNITION ; MANIFOLD ; ILLUMINATION ; MODELS
Indexed BySCI
Language英语
Funding ProjectNational Key Research and Development Program of China[2020AAA0109600] ; National Natural Science Foundation of China[62173328] ; National Natural Science Foundation of China[62106266]
Funding OrganizationNational Key Research and Development Program of China ; National Natural Science Foundation of China
WOS Research AreaEngineering
WOS SubjectEngineering, Electrical & Electronic
WOS IDWOS:000911746000006
PublisherIEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
Citation statistics
Cited Times:2[WOS]   [WOS Record]     [Related Records in WOS]
Document Type期刊论文
Identifierhttp://ir.ia.ac.cn/handle/173211/51336
Collection多模态人工智能系统全国重点实验室_人工智能与机器学习(杨雪冰)-技术团队
Corresponding AuthorZhang, Wensheng
Affiliation1.Anhui Normal Univ, Sch Comp & Informat, Wuhu 241002, Peoples R China
2.Nanjing Univ Sci & Technol, Sch Comp Sci & Engn, Nanjing 210094, Peoples R China
3.Chinese Acad Sci, Inst Automat, Res Ctr Precis Sensing & Control, Beijing 100190, Peoples R China
4.Nanjing Univ Sci & Technol, Sch Comp Sci & Engn, Nanjing 210094, Peoples R China
Corresponding Author AffilicationChinese Acad Sci, Inst Automat, Res Ctr Precis Sensing & Control, Beijing 100190, Peoples R China
Recommended Citation
GB/T 7714
Hu, Liangchen,Dai, Zhenlei,Tian, Lei,et al. Class-Oriented Self-Learning Graph Embedding for Image Compact Representation[J]. IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY,2023,33(1):74-87.
APA Hu, Liangchen,Dai, Zhenlei,Tian, Lei,&Zhang, Wensheng.(2023).Class-Oriented Self-Learning Graph Embedding for Image Compact Representation.IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY,33(1),74-87.
MLA Hu, Liangchen,et al."Class-Oriented Self-Learning Graph Embedding for Image Compact Representation".IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY 33.1(2023):74-87.
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