Deep video code for efficient face video retrieval
文献类型：期刊论文
| 作者 | Qiao, Shishi1,2; Wang, Ruiping1,2; Shan, Shiguang1,2; Chen, Xilin1,2 |
| 刊名 | PATTERN RECOGNITION
 |
| 出版日期 | 2021-05-01 |
| 卷号 | 113页码:11 |
| ISSN号 | 0031-3203 |
| 关键词 | Face video retrieval Temporal feature pooling Bounded triplet loss Deep video code Hash learning |
| DOI | 10.1016/j.patcog.2020.107754 |
| 英文摘要 | In this paper, we address one specific video retrieval problem in terms of human face. Given one query in forms of either a frame or a sequence from a person, we search the database and return the most relevant face videos, i.e., ones have the same class label with the query. Such problem is very challenging due to the large intra-class variations and the high request on the efficiency of video representations in terms of both time and space. To handle such challenges, this paper proposes a novel Deep Video Code (DVC) method which encodes video faces into compact binary codes. Specifically, we devise an end-to end convolutional neural network (CNN) framework that takes face videos as training inputs, models each of them as a unified representation by temporal feature pooling operation, and finally projects the high dimensional representations of both frames and videos into Hamming space to generate binary codes. In such Hamming space, distance of dissimilar pairs is larger than that of similar pairs by a margin. To this end, a novel bounded triplet hashing loss is elaborately designed, which takes all dissimilar pairs into consideration for each anchor point in a mini-batch, and the optimization of the loss function is smoother and more stable. Extensive experiments on challenging video face databases and general image/video datasets with comparison to the state-of-the-arts verify the effectiveness of our method in different kinds of retrieval scenarios. (c) 2020 Elsevier Ltd. All rights reserved. |
| 资助项目 | Natural Science Foundation of China[61922080] ; Natural Science Foundation of China[U19B2036] ; Natural Science Foundation of China[61772500] ; CAS Frontier Science Key Research Project[QYZDJ-SSWJSC009] ; Beijing Academy of Artificial Intelligence[BAAI2020ZJ0201] |
| WOS研究方向 | Computer Science ; Engineering |
| 语种 | 英语 |
| 出版者 | ELSEVIER SCI LTD |
| WOS记录号 | WOS:000626268400007 |
| 源URL | [http://119.78.100.204/handle/2XEOYT63/16738]  |
| 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 video code for efficient face video retrieval[J]. PATTERN RECOGNITION,2021,113:11. |
| APA | Qiao, Shishi,Wang, Ruiping,Shan, Shiguang,&Chen, Xilin.(2021).Deep video code for efficient face video retrieval.PATTERN RECOGNITION,113,11. |
| MLA | Qiao, Shishi,et al."Deep video code for efficient face video retrieval".PATTERN RECOGNITION 113(2021):11. |
入库方式： OAI收割
来源：计算技术研究所
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