文件列表:

algorithms.xlsx (23192, 2017-02-09)
algorithms (0, 2017-02-09)
algorithms\lrr (0, 2017-02-09)
algorithms\lrr\ADM (0, 2017-02-09)
algorithms\lrr\ADM\Atxz.m (526, 2017-02-09)
algorithms\lrr\ADM\Axz.m (353, 2017-02-09)
algorithms\lrr\ADM\adm_lrr.m (2522, 2017-02-09)
algorithms\lrr\ADM\lrr.m (2051, 2017-02-09)
algorithms\lrr\ADM\lrra.m (1934, 2017-02-09)
algorithms\lrr\ADM\lrraffine.m (2465, 2017-02-09)
algorithms\lrr\ADM\run_alg.m (273, 2017-02-09)
algorithms\lrr\ALM (0, 2017-02-09)
algorithms\lrr\ALM\demo.m (1223, 2017-02-09)
algorithms\lrr\ALM\exact_alm_lrr_l1v2.m (2977, 2017-02-09)
algorithms\lrr\ALM\exact_alm_lrr_l21v2.m (2876, 2017-02-09)
algorithms\lrr\ALM\inexact_alm_lrr_l1.m (1840, 2017-02-09)
algorithms\lrr\ALM\inexact_alm_lrr_l21.m (1813, 2017-02-09)
algorithms\lrr\ALM\solve_l1l2.m (273, 2017-02-09)
algorithms\lrr\ALM\solve_lrr.m (1263, 2017-02-09)
algorithms\lrr\EALM (0, 2017-02-09)
algorithms\lrr\EALM\run_alg.m (340, 2017-02-09)
algorithms\lrr\FastLADMAP (0, 2017-02-09)
algorithms\lrr\FastLADMAP\ladmp_lrr_fast.m (4554, 2017-02-09)
algorithms\lrr\FastLADMAP\run_alg.m (429, 2017-02-09)
algorithms\lrr\IALM (0, 2017-02-09)
algorithms\lrr\IALM\run_alg.m (342, 2017-02-09)
algorithms\lrr\LADMAP (0, 2017-02-09)
algorithms\lrr\LADMAP\ladmp_lrr.m (3315, 2017-02-09)
algorithms\lrr\LADMAP\run_alg.m (428, 2017-02-09)
algorithms\lrr\ROSL (0, 2017-02-09)
algorithms\lrr\ROSL\LowRankDictionaryShrinkage_m.m (2786, 2017-02-09)
algorithms\lrr\ROSL\LowRankDictionarySparsify_m.m (1320, 2017-02-09)
algorithms\lrr\ROSL\LowRankDictionaryUpdate_m.m (546, 2017-02-09)
algorithms\lrr\ROSL\inexact_alm_rlr.m (2038, 2017-02-09)
algorithms\lrr\ROSL\inexact_alm_rosl.m (3601, 2017-02-09)
algorithms\lrr\ROSL\inexact_alm_rosl_subsampling.m (1891, 2017-02-09)
... ...

Last Page Update: **07/02/2017** Latest Library Version: **1.0.9** (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 motion segmentation in videos, but it can be also used or adapted for other computer vision problems (for more information, please see this [page](http://perception.csl.illinois.edu/matrix-rank/applications.html)). Currently the LRSLibrary contains a total of **104** *matrix-based* and *tensor-based* algorithms. The LRSLibrary was tested successfully in MATLAB R2013, R2014, R2015, and R2016 both x86 and x*** versions.

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} } ``` 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!

(Click in the image to see the video)

Each algorithm is classified by its cpu time consumption with the following icons:

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 [(Hintermuller 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://arxiv.org/abs/1511.05261) * * NSA1: Non-Smooth Augmented Lagrangian v1 [(Aybat et al. 2011)](http://arxiv.org/abs/1105.2126) * * NSA2: Non-Smooth Augmented Lagrangian v2 [(Aybat et al. 2011)](http://arxiv.org/abs/1105.2126) * * PSPG: Partially Smooth Proximal Gradient [(Aybat et al. 2012)](http://arxiv.org/abs/1309.6976) * * flip-SPCP-sum-SPG: Flip-Flop version of Stable PCP-sum solved by Spectral Projected Gradient [(Aravkin et al. 2014)](https://github.com/stephenbeckr/fastRPCA) * * flip-SPCP-max-QN: Flip-Flop version of Stable PCP-max solved by Quasi-Newton [(Aravkin et al. 2014)](https://github.com/stephenbeckr/fastRPCA) * * Lag-SPCP-SPG: Lagrangian SPCP solved by Spectral Projected Gradient [(Aravkin et al. 2014)](https://github.com/stephenbeckr/fastRPCA) * * Lag-SPCP-QN: Lagrangian SPCP solved by Quasi-Newton [(Aravkin et al. 2014)](https://github.com/stephenbeckr/fastRPCA) * * FW-T: SPCP solved by Frank-Wolfe method [(Mu et al. 2014)](http://arxiv.org/abs/1403.7588) [website](https://sites.google.com/site/mucun1***8/publi) * * BRPCA-MD: Bayesian Robust PCA with Markov Dependency [(Ding et al. 2011)](http://people.ee.duke.edu/~lcarin/LRS_09.pdf) [website](http://people.ee.duke.edu/~lcarin/BCS.html) * * BRPCA-MD-NSS: BRPCA-MD with Non-Stationary Noise [(Ding et al. 2011)](http://people.ee.duke.edu/~lcarin/LRS_09.pdf) [website](http://people.ee.duke.edu/~lcarin/BCS.html) * * VBRPCA: Variational Bayesian RPCA [(Babacan et al. 2011)](http://arxiv.org/abs/1102.5288) * * PRMF: Probabilistic Robust Matrix Factorization [(Wang et al. 2012)](http://winsty.net/papers/prmf.pdf) [website](http://winsty.net/prmf.html) * * OPRMF: Online PRMF [(Wang et al. 2012)](http://winsty.net/papers/prmf.pdf) [website](http://winsty.net/prmf.html) * * MBRMF: Markov BRMF [(Wang and Yeung, 2013)](http://winsty.net/papers/brmf.pdf) [website](http://winsty.net/brmf.html) * * TFOCS-EC: TFOCS with equality constraints [(Becker et al. 2011)](https://github.com/cvxr/TFOCS/raw/gh-pages/TFOCS.pdf) [website](http://cvxr.com/tfocs/demos/rpca/) * * TFOCS-IC: TFOCS with inequality constraints [(Becker et al. 2011)](https://github.com/cvxr/TFOCS/raw/gh-pages/TFOCS.pdf) [website](http://cvxr.com/tfocs/demos/rpca/) * * GoDec: Go Decomposition [(Zhou and Tao, 2011)](http://www.icml-2011.org/papers/41_icmlpaper.pdf) [website](https://sites.google.com/site/godecomposition/home) * * SSGoDec: Semi-Soft GoDec [(Zhou and Tao, 2011)](http://www.icml-2011.org/papers/41_icmlpaper.pdf) [website](https://sites.google.com/site/godecomposition/home) * * GreGoDec: Greedy Semi-Soft GoDec Algotithm [(Zhou and Tao, 2013)](http://jmlr.org/proceedings/papers/v31/zhou13b.pdf) [website](https://sites.google.com/site/godecomposition/home) * ST: Subspace Tracking (3) * * GRASTA: Grassmannian Robust Adaptive Subspace Tracking Algorithm [(He et al. 2012)](http://www.citeulike.org/user/lambertch/article/125439***) [website](https://sites.google.com/site/hejunzz/grasta) * * GOSUS: Grassmannian Online Subspace Updates with Structured-sparsity [(Xu et al. 2013)](http://pages.cs.wisc.edu/~jiaxu/projects/gosus/gosus-iccv2013.pdf) [website](http://pages.cs.wisc.edu/~jiaxu/projects/gosus/) * * pROST: Robust PCA and subspace tracking from incomplete observations using L0-surrogates [(Hage and Kleinsteuber, 2013)](http://arxiv.org/abs/1210.0805) [website](http://www.gol.ei.tum.de/index.php?id=37&L=1) * MC: Matrix Completion (15) * * PG-RMC: Nearly Optimal Robust matrix Completion [(Cherapanamjeri et al. 2016)](https://arxiv.org/abs/1606.07315) * * FPC: Fixed point and Bregman iterative methods for matrix rank minimization [(Ma et al. 2008)](http://arxiv.org/pdf/0905.1***3.pdf) [website](http://www1.se.cuhk.edu.hk/~sqma/FPCA.html) * * GROUSE: Grassmannian Rank-One Update Subspace Estimation [(Balzano et al. 2010)](http://arxiv.org/pdf/100***046.pdf) [website](http://sunbeam.ece.wisc.edu/grouse/) * * IALM-MC: Inexact ALM for Matrix Completion [(Lin et al. 2009)](https://arxiv.org/abs/1009.5055) [website](http://perception.csl.illinois.edu/matrix-rank/sample_code.html) * * LMaFit: Low-Rank Matrix Fitting [(Wen et al. 2012)](http://link.springer.com/article/10.1007%2Fs12532-012-0044-1) [website](http://lmafit.blogs.rice.edu/) * * LRGeomCG: Low-rank matrix completion by Riemannian optimization [(Bart Vandereycken, 2013)](http://web.math.princeton.edu/~bartv/papers/84576.pdf) [website1](http://web.math.princeton.edu/~bartv/matrix_completion.html) [website2](http://www.manopt.org/reference/examples/low_rank_matrix_completion.html) * * MC_logdet: Top-N Recommender System via Matrix Completion [(Kang et al. 2016)](https://arxiv.org/abs/1601.04800) * * MC-NMF: Nonnegative Matrix Completion [(Xu et al. 2011)](https://arxiv.org/abs/1103.1168) * * OP-RPCA: Robust PCA via Outlier Pursuit [(Xu et al. 2012)](http://guppy.mpe.nus.edu.sg/~mpexuh/papers/OutlierPursuit-TIT.pdf) [website](http://guppy.mpe.nus.edu.sg/~mpexuh/publication.html) * * OptSpace: Matrix Completion from Noisy Entries [(Keshavan et al. 2009)](http://arxiv.org/pdf/0906.2027v1.pdf) [website](http://web.engr.illinois.edu/~swoh/software/optspace/code.html) * * OR1MP: Orthogonal rank-one matrix pursuit for low rank matrix completion [(Wang et al. 2015)](https://arxiv.org/abs/1404.1377) * * RPCA-GD: Robust PCA via Gradient Descent [(Yi et al. 2016)](https://arxiv.org/abs/1605.07784) * * ScGrassMC: Scaled Gradients on Grassmann Manifolds for Matrix Completion [(Ngo and Saad, 2012)](https://papers.nips.cc/paper/4713-scaled-gradients-on-grassmann-manifolds-for-matrix-completion.pdf) * * SVP: Guaranteed Rank Minimization via Singular Value Projection [(Meka et al. 2009)](https://arxiv.org/abs/0909.5457) * * SVT: A singular value thresholding algorithm for matrix completion [(Cai et al. 2008)](http://arxiv.org/pdf/0810.3286.pdf) [website](http://svt.stanford.edu/) * LRR: Low Rank Recovery (6) * * EALM: Exact ALM [(Lin et al. 2009)](http://arxiv.org/abs/1009.5055) * * IALM: Inexact ALM [(Lin et al. 2009)](http://arxiv.org/abs/1009.5055) * * ADM: Alternating Direction Method [(Lin et al. 2011)](http://arxiv.org/abs/1109.0367) * * LADMAP: Linearized ADM with Adaptive Penalty [(Lin et al. 2011)](http://arxiv.org/abs/1109.0367) * * FastLADMAP: Fast LADMAP [(Lin et al. 2011)](http://arxiv.org/abs/1109.0367) * * ROSL: Robust Orthonormal Subspace Learning [(Shu et al. 2014)](https://dl.dropboxusercontent.com/u/10893363/Homepage/CVPR2014_ROSL.pdf) [website](https://sites.google.com/site/xianbiaoshu/) * TTD: Three-Term Decomposition (4) * * 3WD: 3-Way-Decomposition [(Oreifej et al. 2012)](http://www.cs.ucf.edu/~oreifej/papers/3-Way-Decomposition.pdf) [website](http://vision.eecs.ucf.edu/projects/Turbulence/) * * MAMR: Motion-Assisted Matrix Restoration [(Ye et al. 2015)](http://projects.medialab-tju.org/bf_separation/download/2015_TCSVT.pdf) [website](http://projects.medialab-tju.org/bf_separation/) * * RMAMR: Robust Motion-Assisted Matrix Restoration [(Ye et al. 2015)](http://projects.medialab-tju.org/bf_separation/download/2015_TCSVT.pdf) [website](http://projects.medialab-tju.org/bf_separation/) * * ADMM: Alternating Direction Method of Multipliers [(Parikh and Boyd, 2014)](http://projects.medialab-tju.org/bf_separation/download/2015_TCSVT.pdf) [website1](http://stanford.edu/~boyd/admm.html) [website2](http://web.stanford.edu/~boyd/papers/prox_algs.html) * NMF: Non-Negative Matrix Factorization (14) * * NMF-MU: NMF solved by Multiplicative Updates * * NMF-PG: NMF solved by Projected Gradient * * NMF-ALS: NMF solved by Alternating Least Squares * * NMF-ALS-OBS: NMF solved by Alternating Least Squares with Optimal Brain Surgeon * * PNMF: Probabilistic Non-negative Matrix Factorization * * ManhNMF: Manhattan NMF [(Guan et al. 2013)](http://arxiv.org/abs/1207.3438) [website](https://sites.google.com/site/nmfsolvers/) * * NeNMF: NMF via Nesterovs Optimal Gradient Method [(Guan et al. 2012)](http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=6166359) [website](https://sites.google.com/site/nmfsolvers/) * * LNMF: Spatially Localized NMF [(Li et al. 2001)](http://dx.doi.org/10.1109/CVPR.2001.990477) * * ENMF: Exact NMF [(Gillis and Glineur, 2012)](http://arxiv.org/abs/1009.0880) [website](https://sites.google.com/site/nicolasgillis/code) * * nmfLS2: Non-negative Matrix Factorization with sparse matrix [(Ji and Eisenstein, 2013)](http://www.cc.gatech.edu/~jeisenst/papers/ji-emnlp-2013.pdf) [website](https://github.com/jiyfeng/tfkld) * * Semi-NMF: Semi Non-negative Matrix Factorization * * Deep-Semi-NMF: Deep Semi Non-negative Matrix Factorization [(Trigeorgis et al. 2014)](http://trigeorgis.com/uploads/downloads/file/1/cameraready.pdf) [website](http://trigeorgis.com/papers/deepseminmfmodel-2014) * * iNMF: Incremental Subspace Learning via NMF [(Bucak and Gunsel, 2009)](http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=42***684) [website](http://www.cse.msu.edu/~bucakser/inmf_genel.html) * * DRMF: Direct Robust Matrix Factorization [(Xiong et al. 2011)](http://ieeexplore.ieee.org/xpl/freeabs_all.jsp?arnumber=6137289) [website](http://www.autonlab.org/autonweb/20605.html) * NTF: Non-Negative Tensor Factorization (6) * * betaNTF: Simple beta-NTF implementation [(Antoine Liutkus, 2012)](http://www.mathworks.com/matlabcentral/fileexchange/38109-nonnegative-matrix-and-tensor-factorization--nmf--ntf--with-any-beta-divergence) * * bcuNTD: Non-negative Tucker Decomposition by block-coordinate update (Xu and Yin, 2012) [website](http://www.math.ucla.edu/~wotaoyin/papers/bcu/ntd/index.html) * * bcuNCP: Non-negative CP Decomposition by block-coordinate update (Xu and Yin, 2012) [website](http://www.math.ucla.edu/~wotaoyin/papers/bcu/ncp/index.html) * * NTD-MU: Non-negative Tucker Decomposition solved by Multiplicative Updates [(Zhou et al. 2012)](http://dx.doi.org/10.1109/TSP.2012.2190410) * * NTD-APG: Non-negative Tucker Decomposition solved by Accelerated Proximal Gradient [(Zhou et al. 2012)](http://dx.doi.org/10.1109/TSP.2012.2190410) * * NTD-HALS: Non-negative Tucker Decomposition solved by Hierarchical ALS [(Zhou et al. 2012)](http://dx.doi.org/10.1109/TSP.2012.2190410) * TD: Tensor Decomposition (12) * * HoSVD: Higher-order Singular Value Decomposition (Tucker Decomposition) * * HoRPCA-IALM: HoRPCA solved by IALM [(Goldfarb and Qin, 2013)](http://arxiv.org/abs/1311.6182) [website](https://sites.google.com/site/tonyqin/research) * * HoRPCA-S: HoRPCA with Singleton model solved by ADAL [(Goldfarb and Qin, 2013)](http://arxiv.org/abs/1311.6182) [website](https://sites.google.com/site/tonyqin/research) * * HoRPCA-S-NCX: HoRPCA with Singleton model solved by ADAL (non-convex) [(Goldfarb and Qin, 2013)](http://arxiv.org/abs/1311.6182) [website](https://sites.google.com/site/tonyqin/research) * * Tucker-ADAL: Tucker Decomposition solved by ADAL [(Goldfarb and Qin, 2013)](http://arxiv.org/abs/1311.6182) [website](https://sites.google.com/site/tonyqin/research) * * Tucker-ALS: Tucker Decomposition solved by ALS * * CP-ALS: PARAFAC/CP decomposition solved by ALS * * CP-APR: PARAFAC/CP decomposition solved by Alternating Poisson Regression [(Chi et al. 2011)](http://arxiv.org/abs/1112.2414) * * CP2: PARAFAC2 decomposition solved by ALS [(Bro et al. 1999)](http://www.mathworks.com/matlabcentral/fileexchange/1089-parafac2) * * RSTD: Rank Sparsity Tensor Decomposition [(Yin Li, 2010)](www.pami.sjtu.edu.cn/demo/RSTD.pdf) [website](http://yinli.cvpr.net/) * * t-SVD: Tensor SVD in Fourrier Domain [(Zhang et al. 2013)](http://arxiv.org/abs/1307.0805) * * OSTD: Online Stochastic Tensor Decomposition [(Sobral et al. 2015)](http://www.cv-foundation.org/openaccess/content_iccv_2015_workshops/w24/papers/Sobral_Online_Stochastic_Tensor_ICCV_2015_paper.pdf) [website](https://github.com/andrewssobral/ostd) * **Some remarks**: * * The FW-T algorithm of Mu et al. (2014) works only with [CVX library](http://cvxr.com/cvx/). Download and install it in: *lrslibrary/libs/cvx/*. * * The DECOLOR algorithm of Zhou et al. (2011) don't works in MATLAB R2014a(x***), but works successfully in MATLAB R2013b(x***) and both R2014a(x86) and R2013b(x86). Usage example ---------------------------- For complete details and examples, please see the **demo.m** file. ```Matlab %% First run the setup script lrs_setup; % or run('C:/lrslibrary/lrs_setup') %% Load configuration lrs_load_conf; %% Load video input_avi = fullfile(lrs_conf.lrs_dir,'dataset','demo.avi'); output_avi = fullfile(lrs_conf.lrs_dir,'output','output.avi'); %% Processing videos % % Robust PCA process_video('RPCA', 'FPCP', input_avi, output_avi); % Subspace Tracking proce ... ...

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