Unsupervised and Semi-Supervised Image Classification With Weak Semantic Consistency
文献类型：期刊论文
| 作者 | Zhang, Chunjie1 ; Cheng, Jian2,3; Tian, Qi4
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| 刊名 | IEEE TRANSACTIONS ON MULTIMEDIA
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| 出版日期 | 2019-10-01 |
| 卷号 | 21期号:10页码:2482-2491 |
| 关键词 | Semi-supervised classification unsupervised classification weak semantic representation semantic consistency |
| ISSN号 | 1520-9210 |
| DOI | 10.1109/TMM.2019.2903628 |
| 通讯作者 | Zhang, Chunjie(chunjie.zhang@ia.ac.cn) |
| 英文摘要 | Supervised methods have been widely used for image classifications. Although great progress has been made, existing supervised methods rely on well-labeled samples for classification. However, we often have large quantities of images with few or no labels. To cope with this problem, in this paper, we propose a novel weak semantic consistency constrained image classification method. We start from an extreme circumstance by viewing each image as one class. We train exemplar classifiers to separate each image from other images. For each image, we use the learned exemplar classifiers to predict the weak semantic correlations with the exemplar classifiers. When no labeled information is available, we cluster images using the weak semantic correlations and assign images within one cluster to the same mid-level class. When partially labeled images are available, we can use them to constrain the clustering process by assigning images of varied semantics to different mid-level classes. We use the newly assigned images for classifier training and new image representations, which can then be used for similar image assignments. The classifier training, image representation, and assignment processes are repeated until convergence. We conduct both unsupervised and semi-supervised image classification experiments on several datasets. The experimental results show the effectiveness of the proposed unsupervised and semi-supervised weak semantic consistency image classification method. |
| WOS关键词 | LABEL PROPAGATION ; LOW-RANK ; REPRESENTATION |
| 资助项目 | National Science Foundation of China[61872362] ; National Science Foundation of China[61876135] |
| WOS研究方向 | Computer Science ; Telecommunications |
| 语种 | 英语 |
| WOS记录号 | WOS:000489728400005 |
| 出版者 | IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC |
| 资助机构 | National Science Foundation of China |
| 源URL | [http://ir.ia.ac.cn/handle/173211/26666]  |
| 专题 | 类脑芯片与系统研究 |
| 通讯作者 | Zhang, Chunjie |
| 作者单位 | 1.Chinese Acad Sci, Inst Automat, Res Ctr Brain Inspired Intelligence, Beijing 100190, Peoples R China 2.Chinese Acad Sci, Inst Automat, Natl Lab Pattern Recognit, Beijing 100190, Peoples R China 3.Chinese Acad Sci, Ctr Excellence Brain Sci & Intelligence Technol, Beijing 100190, Peoples R China 4.Univ Texas San Antonio, Dept Comp Sci, San Antonio, TX 78249 USA |
| 推荐引用方式 GB/T 7714 | Zhang, Chunjie,Cheng, Jian,Tian, Qi. Unsupervised and Semi-Supervised Image Classification With Weak Semantic Consistency[J]. IEEE TRANSACTIONS ON MULTIMEDIA,2019,21(10):2482-2491. |
| APA | Zhang, Chunjie,Cheng, Jian,&Tian, Qi.(2019).Unsupervised and Semi-Supervised Image Classification With Weak Semantic Consistency.IEEE TRANSACTIONS ON MULTIMEDIA,21(10),2482-2491. |
| MLA | Zhang, Chunjie,et al."Unsupervised and Semi-Supervised Image Classification With Weak Semantic Consistency".IEEE TRANSACTIONS ON MULTIMEDIA 21.10(2019):2482-2491. |
入库方式： OAI收割
来源：自动化研究所
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