热门
Learning Coupled Convolutional Networks Fusion for Video Saliency Prediction
文献类型：期刊论文
| 作者 | Wu, Zhe1,2; Su, Li1,2; Huang, Qingming1,2,3 |
| 刊名 | IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY
 |
| 出版日期 | 2019-10-01 |
| 卷号 | 29期号:10页码:2960-2971 |
| ISSN号 | 1051-8215 |
| 关键词 | Video saliency feature integration fully convolutional network |
| DOI | 10.1109/TCSVT.2018.2870954 |
| 英文摘要 | Visual saliency provides important information for understanding scenes in many computer vision tasks. The existing video saliency algorithms mainly focus on predicting spatial and temporal saliency maps. However, these maps are simply fused without considering the complex dynamic scenes in videos. To overcome this drawback, we propose a deep convolutional fusion framework for video saliency prediction. The proposed model, which is based on coupled fully convolutional networks (FCNs), effectively encodes the spatiotemporal information by integrating spatial and temporal features. We demonstrate that this information is helpful for accurately fusing the spatial and temporal saliency maps according to changes in video scenes. In particular, we gradually design three different deep fusion architectures to investigate how to better utilize the spatiotemporal information. Moreover, we propose a reasonable sampling strategy for selecting suitable training sets for the coupled FCNs. Through extensive experiments, we demonstrate that our model outperforms the state-of-the-art algorithms on four public video saliency data sets. |
| 资助项目 | National Natural Sciences Foundation of China[61472389] ; National Natural Sciences Foundation of China[61650202] ; National Natural Sciences Foundation of China[61332016] ; National Natural Sciences Foundation of China[61620106009] ; National Natural Sciences Foundation of China[U1636214] ; Key Research Program of Frontier Sciences[CAS: QYZDJ-SSW-SYS013] |
| WOS研究方向 | Engineering |
| 语种 | 英语 |
| 出版者 | IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC |
| WOS记录号 | WOS:000489749900008 |
| 源URL | [http://119.78.100.204/handle/2XEOYT63/4596]  |
| 专题 | 中国科学院计算技术研究所期刊论文_英文 |
| 通讯作者 | Su, Li |
| 作者单位 | 1.UCAS, Key Lab Big Data Min & Knowledge Management, Beijing 101408, Peoples R China 2.UCAS, Sch Comp & Control Engn, Beijing 101408, Peoples R China 3.Chinese Acad Sci, Key Lab Intelligent Informat Proc, Inst Comp Technol, Beijing 100190, Peoples R China |
| 推荐引用方式 GB/T 7714 | Wu, Zhe,Su, Li,Huang, Qingming. Learning Coupled Convolutional Networks Fusion for Video Saliency Prediction[J]. IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY,2019,29(10):2960-2971. |
| APA | Wu, Zhe,Su, Li,&Huang, Qingming.(2019).Learning Coupled Convolutional Networks Fusion for Video Saliency Prediction.IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY,29(10),2960-2971. |
| MLA | Wu, Zhe,et al."Learning Coupled Convolutional Networks Fusion for Video Saliency Prediction".IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY 29.10(2019):2960-2971. |
入库方式： OAI收割
来源：计算技术研究所
浏览0
下载0
收藏0
其他版本
除非特别说明，本系统中所有内容都受版权保护，并保留所有权利。