这是带边信息的在线鲁棒张量补全的代码.zip

[](https://www.mathworks.com/matlabcentral/fileexchange/48404-lrslibrary) Last Page Update: **29/07/2022**, Previous Page Update: **07/03/2020** Latest Library Version: **1.0.11** (see Release Notes for more info) LRSLibrary ---------- *Low-Rank and Sparse* tools for Background Modeling and Subtraction in Videos. The *LRSLibrary* provides a collection of **low-rank and sparse decomposition** algorithms in MATLAB. The library was designed for moving object detection in videos, but it can be also used for other computer vision and machine learning problems (for more information, please see [here](https://en.wikipedia.org/wiki/Robust_principal_component_analysis#Applications) and [here](https://en.wikipedia.org/wiki/Low-rank_approximation#Applications)). Currently the LRSLibrary offers more than **100** algorithms based on *matrix* and *tensor* methods. The LRSLibrary was tested successfully in several MATLAB versions (e.g. R2014, R2015, R2016, R2017, on both x86 and x64 versions). It requires minimum **R2014b**. <p align="center"><img src="https://raw.githubusercontent.com/andrewssobral/lrslibrary/master/figs/lrs_results2.png" /></p> <p align="center"><img src="https://raw.githubusercontent.com/andrewssobral/lrslibrary/master/figs/lrs-opt.gif" /></p> See also: ``` Presentation about Matrix and Tensor Tools for Computer Vision http://www.slideshare.net/andrewssobral/matrix-and-tensor-tools-for-computer-vision MTT: Matlab Tensor Tools for Computer Vision https://github.com/andrewssobral/mtt IMTSL: Incremental and Multi-feature Tensor Subspace Learning https://github.com/andrewssobral/imtsl ``` Citation --------- If you use this library for your publications, please cite it as: ``` @incollection{lrslibrary2015, author = {Sobral, Andrews and Bouwmans, Thierry and Zahzah, El-hadi}, title = {LRSLibrary: Low-Rank and Sparse tools for Background Modeling and Subtraction in Videos}, booktitle = {Robust Low-Rank and Sparse Matrix Decomposition: Applications in Image and Video Processing}, publisher = {CRC Press, Taylor and Francis Group.} year = {2015} } ``` Additional reference: ``` @article{bouwmans2015, author = {Bouwmans, Thierry and Sobral, Andrews and Javed, Sajid and Jung, Soon Ki and Zahzah, El-hadi}, title = {Decomposition into Low-rank plus Additive Matrices for Background/Foreground Separation: {A} Review for a Comparative Evaluation with a Large-Scale Dataset}, journal = {CoRR}, volume = {abs/1511.01245} year = {2015}, url = {http://arxiv.org/abs/1511.01245} } ``` ## Stargazers over time [](https://starchart.cc/andrewssobral/lrslibrary) Install --- Just do the following steps: * First, clone the repository: ```$ git clone --recursive https://github.com/andrewssobral/lrslibrary.git``` * Then, open your MATLAB and run the following setup script: ```>> lrs_setup``` That's all! GUI --- The *LRSLibrary* provides an easy-to-use graphical user interface (GUI) for background modeling and subtraction in videos. First, run the setup script **lrs_setup** (or **run('C:/lrslibrary/lrs_setup')**), then run **lrs_gui**, and enjoy it! <p align="center">(Click in the image to see the video)</p> <p align="center"> <a href="https://www.youtube.com/watch?v=zziJ7-WnvV8" target="_blank"> <img src="https://raw.githubusercontent.com/andrewssobral/lrslibrary/master/figs/lrslibrary_gui2.png" width="500" border="0" /> </a> </p> Each algorithm is classified by its cpu time consumption with the following icons: <p align="center"><img src="https://raw.githubusercontent.com/andrewssobral/lrslibrary/master/figs/time_legend.png" width="300" /></p> The algorithms were grouped in eight categories: **RPCA** for Robust PCA, **ST** for Subspace Tracking, **MC** for Matrix Completion, **TTD** for Three-Term Decomposition, **LRR** for Low-Rank Representation, **NMF** for Non-negative Matrix Factorization, **NTF** for Non-negative Tensor Factorization, or **TD** for standard Tensor Decomposition. List of the algorithms available in LRSLibrary ---------------------------------------------- * RPCA: Robust PCA (44) * * RPCA: Robust Principal Component Analysis [(De la Torre and Black, 2001)](http://users.salleurl.edu/~ftorre/papers/rpca/rpca.pdf) [website](http://users.salleurl.edu/~ftorre/papers/rpca2.html) * * PCP: Principal Component Pursuit [(Candes et al. 2009)](http://arxiv.org/abs/0912.3599) * * FPCP: Fast PCP [(Rodriguez and Wohlberg, 2013)](http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=6738015) * * R2PCP: Riemannian Robust Principal Component Pursuit [(Hintermüller and Wu, 2014)](http://link.springer.com/article/10.1007/s10851-014-0527-y) * * AS-RPCA: Active Subspace: Towards Scalable Low-Rank Learning [(Liu and Yan, 2012)](http://dl.acm.org/citation.cfm?id=2421487) * * ALM: Augmented Lagrange Multiplier [(Tang and Nehorai 2011)](http://dx.doi.org/10.1109/CISS.2011.5766144) * * EALM: Exact ALM [(Lin et al. 2009)](http://arxiv.org/abs/1009.5055) [website](http://perception.csl.illinois.edu/matrix-rank/sample_code.html) * * IALM: Inexact ALM [(Lin et al. 2009)](http://arxiv.org/abs/1009.5055) [website](http://perception.csl.illinois.edu/matrix-rank/sample_code.html) * * IALM_LMSVDS: IALM with LMSVDS [(Liu et al. 2012)](http://epubs.siam.org/doi/abs/10.1137/120871328) * * IALM_BLWS: IALM with BLWS [(Lin and Wei, 2010)](http://arxiv.org/abs/1012.0365) * * APG_PARTIAL: Partial Accelerated Proximal Gradient [(Lin et al. 2009)](http://arxiv.org/abs/1009.5055) [website](http://perception.csl.illinois.edu/matrix-rank/sample_code.html) * * APG: Accelerated Proximal Gradient [(Lin et al. 2009)](http://arxiv.org/abs/1009.5055) [website](http://perception.csl.illinois.edu/matrix-rank/sample_code.html) * * DUAL: Dual RPCA [(Lin et al. 2009)](http://arxiv.org/abs/1009.5055) [website](http://perception.csl.illinois.edu/matrix-rank/sample_code.html) * * SVT: Singular Value Thresholding [(Cai et al. 2008)](http://arxiv.org/abs/0810.3286) [website](http://perception.csl.illinois.edu/matrix-rank/sample_code.html) * * ADM: Alternating Direction Method [(Yuan and Yang, 2009)](http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.400.8797) * * LSADM: LSADM [(Goldfarb et al. 2010)](http://arxiv.org/abs/0912.4571) * * L1F: L1 Filtering [(Liu et al. 2011)](http://arxiv.org/abs/1108.5359) * * DECOLOR: Contiguous Outliers in the Low-Rank Representation [(Zhou et al. 2011)](http://arxiv.org/abs/1109.0882) [website1](https://sites.google.com/site/eeyangc/software/decolor) [website2](https://fling.seas.upenn.edu/~xiaowz/dynamic/wordpress/?p=144) * * RegL1-ALM: Low-Rank Matrix Approximation under Robust L1-Norm [(Zheng et al. 2012)](https://sites.google.com/site/yinqiangzheng/home/zheng_CVPR12_robust%20L1-norm%20low-rank%20matrix%20factorization.pdf) [website](https://sites.google.com/site/yinqiangzheng/) * * GA: Grassmann Average [(Hauberg et al. 2014)](http://files.is.tue.mpg.de/black/papers/RGA2014.pdf) [website](http://ps.is.tuebingen.mpg.de/project/Robust_PCA) * * GM: Grassmann Median [(Hauberg et al. 2014)](http://files.is.tue.mpg.de/black/papers/RGA2014.pdf) [website](http://ps.is.tuebingen.mpg.de/project/Robust_PCA) * * TGA: Trimmed Grassmann Average [(Hauberg et al. 2014)](http://files.is.tue.mpg.de/black/papers/RGA2014.pdf) [website](http://ps.is.tuebingen.mpg.de/project/Robust_PCA) * * STOC-RPCA: Online Robust PCA via Stochastic Optimization [(Feng et al. 2013)](http://guppy.mpe.nus.edu.sg/~mpexuh/papers/Stochastic_online_pca.pdf) [website](https://sites.google.com/site/jshfeng/) * * MoG-RPCA: Mixture of Gaussians RPCA [(Zhao et al. 2014)](http://jmlr.org/proceedings/papers/v32/zhao14.pdf) [website](http://www.cs.cmu.edu/~deyum/index.htm) * * noncvxRPCA: Robust PCA via Nonconvex Rank Approximation [(Kang et al. 2015)](http