Deep Heterogeneous Hashing for Face Video Retrieval
文献类型：期刊论文
| 作者 | Qiao, Shishi2; Wang, Ruiping1,2; Shan, Shiguang2; Chen, Xilin2 |
| 刊名 | IEEE TRANSACTIONS ON IMAGE PROCESSING
 |
| 出版日期 | 2020 |
| 卷号 | 29页码:1299-1312 |
| 关键词 | Face Covariance matrices Task analysis Binary codes Kernel Manifolds Feature extraction Face video retrieval deep heterogeneous hashing Riemannian kernel mapping structured matrix backpropagation |
| ISSN号 | 1057-7149 |
| DOI | 10.1109/TIP.2019.2940683 |
| 英文摘要 | Retrieving videos of a particular person with face image as query via hashing technique has many important applications. While face images are typically represented as vectors in Euclidean space, characterizing face videos with some robust set modeling techniques (e.g. covariance matrices as exploited in this study, which reside on Riemannian manifold), has recently shown appealing advantages. This hence results in a thorny heterogeneous spaces matching problem. Moreover, hashing with handcrafted features as done in many existing works is clearly inadequate to achieve desirable performance for this task. To address such problems, we present an end-to-end Deep Heterogeneous Hashing (DHH) method that integrates three stages including image feature learning, video modeling, and heterogeneous hashing in a single framework, to learn unified binary codes for both face images and videos. To tackle the key challenge of hashing on manifold, a well-studied Riemannian kernel mapping is employed to project data (i.e. covariance matrices) into Euclidean space and thus enables to embed the two heterogeneous representations into a common Hamming space, where both intra-space discriminability and inter-space compatibility are considered. To perform network optimization, the gradient of the kernel mapping is innovatively derived via structured matrix backpropagation in a theoretically principled way. Experiments on three challenging datasets show that our method achieves quite competitive performance compared with existing hashing methods. |
| 资助项目 | 973 Program[2015CB351802] ; Natural Science Foundation of China[61390511] ; Natural Science Foundation of China[61772500] ; Frontier Science Key Research Project CAS[QYZDJSSW-JSC009] ; Youth Innovation Promotion Association CAS[2015085] |
| WOS研究方向 | Computer Science ; Engineering |
| 语种 | 英语 |
| WOS记录号 | WOS:000498872600024 |
| 出版者 | IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC |
| 源URL | [http://119.78.100.204/handle/2XEOYT63/14924]  |
| 专题 | 中国科学院计算技术研究所期刊论文_英文 |
| 通讯作者 | Wang, Ruiping |
| 作者单位 | 1.Chinese Acad Sci, Inst Comp Technol, Key Lab Intelligent Informat Proc, Beijing 100190, Peoples R China 2.Univ Chinese Acad Sci, Beijing 100049, Peoples R China |
| 推荐引用方式 GB/T 7714 | Qiao, Shishi,Wang, Ruiping,Shan, Shiguang,et al. Deep Heterogeneous Hashing for Face Video Retrieval[J]. IEEE TRANSACTIONS ON IMAGE PROCESSING,2020,29:1299-1312. |
| APA | Qiao, Shishi,Wang, Ruiping,Shan, Shiguang,&Chen, Xilin.(2020).Deep Heterogeneous Hashing for Face Video Retrieval.IEEE TRANSACTIONS ON IMAGE PROCESSING,29,1299-1312. |
| MLA | Qiao, Shishi,et al."Deep Heterogeneous Hashing for Face Video Retrieval".IEEE TRANSACTIONS ON IMAGE PROCESSING 29(2020):1299-1312. |
入库方式： OAI收割
来源：计算技术研究所
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