Institutional Repository of Chinese Acad Sci, Inst Automat, Res Ctr Precis Sensing & Control, Beijing 100190, Peoples R China
| Class-Oriented Self-Learning Graph Embedding for Image Compact Representation | |
Hu, Liangchen1; Dai, Zhenlei2; Tian, Lei3,4 ; Zhang, Wensheng3,4
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| Source Publication | IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY
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| ISSN | 1051-8215 |
| 2023 | |
| Volume | 33Issue:1Pages:74-87 |
| Corresponding Author | Zhang, Wensheng(zhangwenshengia@hotmail.com) |
| Abstract | As 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. |
| Keyword | Sparse 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 |
| DOI | 10.1109/TCSVT.2022.3197746 |
| WOS Keyword | DIMENSIONALITY REDUCTION ; PRESERVING PROJECTIONS ; FEATURE-SELECTION ; FACE RECOGNITION ; MANIFOLD ; ILLUMINATION ; MODELS |
| Indexed By | SCI |
| Language | 英语 |
| Funding Project | National Key Research and Development Program of China[2020AAA0109600] ; National Natural Science Foundation of China[62173328] ; National Natural Science Foundation of China[62106266] |
| Funding Organization | National Key Research and Development Program of China ; National Natural Science Foundation of China |
| WOS Research Area | Engineering |
| WOS Subject | Engineering, Electrical & Electronic |
| WOS ID | WOS:000911746000006 |
| Publisher | IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC |
| Citation statistics | |
| Document Type | 期刊论文 |
| Identifier | http://ir.ia.ac.cn/handle/173211/51336 |
| Collection | 精密感知与控制研究中心_人工智能与机器学习 |
| Corresponding Author | Zhang, Wensheng |
| Affiliation | 1.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 Affilication | Chinese 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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