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Transfer Learning for Visual Categorization: A Survey
文献类型：期刊论文
| 作者 | Shao, Ling1,2; Zhu, Fan2; Li, Xuelong3
|
| 刊名 | ieee transactions on neural networks and learning systems
 |
| 出版日期 | 2015-05-01 |
| 卷号 | 26期号:5页码:1019-1034 |
| 关键词 | Action recognition image classification machine learning object recognition survey transfer learning visual categorization |
| 英文摘要 | regular machine learning and data mining techniques study the training data for future inferences under a major assumption that the future data are within the same feature space or have the same distribution as the training data. however, due to the limited availability of human labeled training data, training data that stay in the same feature space or have the same distribution as the future data cannot be guaranteed to be sufficient enough to avoid the over-fitting problem. in real-world applications, apart from data in the target domain, related data in a different domain can also be included to expand the availability of our prior knowledge about the target future data. transfer learning addresses such cross-domain learning problems by extracting useful information from data in a related domain and transferring them for being used in target tasks. in recent years, with transfer learning being applied to visual categorization, some typical problems, e.g., view divergence in action recognition tasks and concept drifting in image classification tasks, can be efficiently solved. in this paper, we survey state-of-the-art transfer learning algorithms in visual categorization applications, such as object recognition, image classification, and human action recognition. |
| WOS标题词 | science & technology ; technology |
| 类目[WOS] | computer science, artificial intelligence ; computer science, hardware & architecture ; computer science, theory & methods ; engineering, electrical & electronic |
| 研究领域[WOS] | computer science ; engineering |
| 关键词[WOS] | human action recognition ; view action recognition ; domain adaptation ; image classification ; invariant analysis ; fuzzy system ; kernel ; representation ; motion ; histograms |
| 收录类别 | SCI ; EI |
| 语种 | 英语 |
| WOS记录号 | WOS:000353122400010 |
| 公开日期 | 2015-07-28 |
| 源URL | [http://ir.opt.ac.cn/handle/181661/25044]  |
| 专题 | 西安光学精密机械研究所_光学影像学习与分析中心 |
| 作者单位 | 1.Nanjing Univ Informat Sci & Technol, Coll Elect & Informat Engn, Nanjing 210044, Jiangsu, Peoples R China 2.Univ Sheffield, Dept Elect & Elect Engn, Sheffield S1 3JD, S Yorkshire, England 3.Chinese Acad Sci, Ctr OPT IMagery Anal & Learning, State Key Lab Transient Opt & Photon, Xian Inst Opt & Precis Mech, Xian 710119, Peoples R China |
| 推荐引用方式 GB/T 7714 | Shao, Ling,Zhu, Fan,Li, Xuelong. Transfer Learning for Visual Categorization: A Survey[J]. ieee transactions on neural networks and learning systems,2015,26(5):1019-1034. |
| APA | Shao, Ling,Zhu, Fan,&Li, Xuelong.(2015).Transfer Learning for Visual Categorization: A Survey.ieee transactions on neural networks and learning systems,26(5),1019-1034. |
| MLA | Shao, Ling,et al."Transfer Learning for Visual Categorization: A Survey".ieee transactions on neural networks and learning systems 26.5(2015):1019-1034. |
入库方式： OAI收割
来源：西安光学精密机械研究所
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