The Twist Tensor Nuclear Norm for Video Completion
文献类型：期刊论文
| 作者 | Hu, Wenrui1 ; Tao, Dacheng2; Zhang, Wensheng1; Xie, Yuan1; Yang, Yehui1; Wensheng Zhang
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| 刊名 | IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
 |
| 出版日期 | 2017-12-01 |
| 卷号 | 28期号:12页码:2961-2973 |
| 关键词 | Low-rank Tensor Estimation (Lrte) Tensor Multirank Tensor Nuclear Norm (Tnn) Twist Tensor Video Completion |
| DOI | 10.1109/TNNLS.2016.2611525 |
| 文献子类 | Article |
| 英文摘要 | In this paper, we propose a new low-rank tensor model based on the circulant algebra, namely, twist tensor nuclear norm (t-TNN). The twist tensor denotes a three-way tensor representation to laterally store 2-D data slices in order. On one hand, t-TNN convexly relaxes the tensor multirank of the twist tensor in the Fourier domain, which allows an efficient computation using fast Fourier transform. On the other, t-TNN is equal to the nuclear norm of block circulant matricization of the twist tensor in the original domain, which extends the traditional matrix nuclear norm in a block circulant way. We test the t-TNN model on a video completion application that aims to fill missing values and the experiment results validate its effectiveness, especially when dealing with video recorded by a nonstationary panning camera. The block circulant matricization of the twist tensor can be transformed into a circulant block representation with nuclear norm invariance. This representation, after transformation, exploits the horizontal translation relationship between the frames in a video, and endows the t-TNN model with a more powerful ability to reconstruct panning videos than the existing state-of-the-art low-rank models. |
| WOS关键词 | RANK ; IMAGE ; DECOMPOSITION ; REGULARIZATION ; APPROXIMATION ; FACTORIZATION ; FRAMEWORK |
| WOS研究方向 | Computer Science ; Engineering |
| 语种 | 英语 |
| WOS记录号 | WOS:000416261400010 |
| 资助机构 | National Natural Science Foundation of China(61402480 ; Australian Research Council(DP-140102164 ; 61432008 ; FT-130101457 ; 61472423 ; LE-140100061) ; 61502495 ; 61532006) |
| 源URL | [http://ir.ia.ac.cn/handle/173211/12255]  |
| 专题 | 精密感知与控制研究中心_人工智能与机器学习 |
| 通讯作者 | Wensheng Zhang |
| 作者单位 | 1.Chinese Acad Sci, Inst Automat, Beijing 100190, Peoples R China 2.Univ Technol Sydney, Fac Engn & Informat Technol, Ctr Quantum Computat & Intelligent Syst, Ultimo, NSW 2007, Australia |
| 推荐引用方式 GB/T 7714 | Hu, Wenrui,Tao, Dacheng,Zhang, Wensheng,et al. The Twist Tensor Nuclear Norm for Video Completion[J]. IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS,2017,28(12):2961-2973. |
| APA | Hu, Wenrui,Tao, Dacheng,Zhang, Wensheng,Xie, Yuan,Yang, Yehui,&Wensheng Zhang.(2017).The Twist Tensor Nuclear Norm for Video Completion.IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS,28(12),2961-2973. |
| MLA | Hu, Wenrui,et al."The Twist Tensor Nuclear Norm for Video Completion".IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS 28.12(2017):2961-2973. |
入库方式： OAI收割
来源：自动化研究所
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