| Flowing on Riemannian Manifold: Domain Adaptation by Shifting Covariance | |
Cui, Zhen1,2; Li, Wen3; Xu, Dong3; Shan, Shiguang2; Chen, Xilin2; Li, Xuelong4
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| 作者部门 | 光学影像学习与分析中心 |
| 2014-12-01 | |
| 发表期刊 | IEEE TRANSACTIONS ON CYBERNETICS
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| ISSN | 2168-2267 |
| 卷号 | 44期号:12页码:2264-2273 |
| 产权排序 | 4 |
| 摘要 | Domain adaptation has shown promising results in computer vision applications. In this paper, we propose a new unsupervised domain adaptation method called domain adaptation by shifting covariance (DASC) for object recognition without requiring any labeled samples from the target domain. By characterizing samples from each domain as one covariance matrix, the source and target domain are represented into two distinct points residing on a Riemannian manifold. Along the geodesic constructed from the two points, we then interpolate some intermediate points (i.e., covariance matrices), which are used to bridge the two domains. By utilizing the principal components of each covariance matrix, samples from each domain are further projected into intermediate feature spaces, which finally leads to domain-invariant features after the concatenation of these features from intermediate points. In the multiple source domain adaptation task, we also need to effectively integrate different types of features between each pair of source and target domains. We additionally propose an SVM based method to simultaneously learn the optimal target classifier as well as the optimal weights for different source domains. Extensive experiments demonstrate the effectiveness of our method for both single source and multiple source domain adaptation tasks. |
| 文章类型 | Article |
| 关键词 | Domain Adaptation Riemannian Manifold Support Vector Machine |
| WOS标题词 | Science & Technology ; Technology |
| DOI | 10.1109/TCYB.2014.2305701 |
| 收录类别 | SCI ; EI |
| 关键词[WOS] | EVENT RECOGNITION ; VIDEOS |
| 语种 | 英语 |
| WOS研究方向 | Computer Science |
| WOS类目 | Computer Science, Artificial Intelligence ; Computer Science, Cybernetics |
| WOS记录号 | WOS:000345629000003 |
| 引用统计 | |
| 文献类型 | 期刊论文 |
| 条目标识符 | http://ir.opt.ac.cn/handle/181661/22414 |
| 专题 | 光谱成像技术研究室 |
| 作者单位 | 1.Huaqiao Univ, Coll Comp Sci & Technol, Xiamen 361021, Peoples R China 2.Chinese Acad Sci, Inst Comp Technol, Key Lab Intelligent Informat Proc, Beijing 100190, Peoples R China 3.Nanyang Technol Univ, Sch Comp Engn, Singapore 639798, Singapore 4.Chinese Acad Sci, Xian Inst Opt & Precis Mech, State Key Lab Transient Opt & Photon, Ctr Opt Imagery Anal & Learning, Xian 710119, Peoples R China |
| 推荐引用方式 GB/T 7714 | Cui, Zhen,Li, Wen,Xu, Dong,et al. Flowing on Riemannian Manifold: Domain Adaptation by Shifting Covariance[J]. IEEE TRANSACTIONS ON CYBERNETICS,2014,44(12):2264-2273. |
| APA | Cui, Zhen,Li, Wen,Xu, Dong,Shan, Shiguang,Chen, Xilin,&Li, Xuelong.(2014).Flowing on Riemannian Manifold: Domain Adaptation by Shifting Covariance.IEEE TRANSACTIONS ON CYBERNETICS,44(12),2264-2273. |
| MLA | Cui, Zhen,et al."Flowing on Riemannian Manifold: Domain Adaptation by Shifting Covariance".IEEE TRANSACTIONS ON CYBERNETICS 44.12(2014):2264-2273. |
| 条目包含的文件 | ||||||
| 文件名称/大小 | 文献类型 | 版本类型 | 开放类型 | 使用许可 | ||
| Flowing on Riemannia（1515KB） | 期刊论文 | 出版稿 | 限制开放 | CC BY | 请求全文 | |
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