Practical ReProCS for separating sparse and low-dimensional signal sequences from their sum — Part 1

This paper designs and evaluates a practical algorithm, called Prac-ReProCS, for recovering a time sequence of sparse vectors St and a time sequence of dense vectors Lt from their sum, Mt := St + Lt, when any subsequence of the Lt's lies in a slowly changing low-dimensional subspace. A key application where this problem occurs is in video layering where the goal is to separate a video sequence into a slowly changing background sequence and a sparse foreground sequence that consists of one or more moving regions/objects. Prac-ReProCS is the practical analog of its theoretical counterpart that was studied in our recent work.

[1]  Joel A. Tropp,et al.  Sharp recovery bounds for convex deconvolution, with applications , 2012, ArXiv.

[2]  Jason Morphett,et al.  An integrated algorithm of incremental and robust PCA , 2003, Proceedings 2003 International Conference on Image Processing (Cat. No.03CH37429).

[3]  Matthew Brand,et al.  Incremental Singular Value Decomposition of Uncertain Data with Missing Values , 2002, ECCV.

[4]  Morteza Mardani,et al.  Recovery of Low-Rank Plus Compressed Sparse Matrices With Application to Unveiling Traffic Anomalies , 2012, IEEE Transactions on Information Theory.

[5]  Shie Mannor,et al.  Online PCA for Contaminated Data , 2013, NIPS.

[6]  Hassan Mansour,et al.  Recovering Compressively Sampled Signals Using Partial Support Information , 2010, IEEE Transactions on Information Theory.

[7]  Namrata Vaswani,et al.  Recursive sparse recovery in large but correlated noise , 2011, 2011 49th Annual Allerton Conference on Communication, Control, and Computing (Allerton).

[8]  Aswin C. Sankaranarayanan,et al.  SpaRCS: Recovering low-rank and sparse matrices from compressive measurements , 2011, NIPS.

[9]  Xiaoming Yuan,et al.  Recovering Low-Rank and Sparse Components of Matrices from Incomplete and Noisy Observations , 2011, SIAM J. Optim..

[10]  Gonzalo Mateos,et al.  Robust PCA as Bilinear Decomposition With Outlier-Sparsity Regularization , 2011, IEEE Transactions on Signal Processing.

[11]  Michael J. Black,et al.  A Framework for Robust Subspace Learning , 2003, International Journal of Computer Vision.

[12]  Danijel Skocaj,et al.  Weighted and robust incremental method for subspace learning , 2003, Proceedings Ninth IEEE International Conference on Computer Vision.

[13]  E. Candès The restricted isometry property and its implications for compressed sensing , 2008 .

[14]  Namrata Vaswani,et al.  Modified-CS: Modifying compressive sensing for problems with partially known support , 2009, ISIT.

[15]  Emmanuel J. Candès,et al.  Decoding by linear programming , 2005, IEEE Transactions on Information Theory.

[16]  Yi Ma,et al.  Robust principal component analysis? , 2009, JACM.

[17]  Namrata Vaswani,et al.  Recursive robust PCA or recursive sparse recovery in large but structured noise , 2013, ICASSP.

[18]  Weiyu Xu,et al.  Weighted ℓ1 minimization for sparse recovery with prior information , 2009, 2009 IEEE International Symposium on Information Theory.

[19]  John Wright,et al.  Principal Component Pursuit with reduced linear measurements , 2012, 2012 IEEE International Symposium on Information Theory Proceedings.

[20]  Namrata Vaswani,et al.  Stability (over time) of modified-CS for recursive causal sparse reconstruction , 2010, 2010 48th Annual Allerton Conference on Communication, Control, and Computing (Allerton).

[21]  Namrata Vaswani,et al.  Recursive sparse recovery in large but structured noise — Part 2 , 2013, 2013 IEEE International Symposium on Information Theory.

[22]  Sham M. Kakade,et al.  Robust Matrix Decomposition With Sparse Corruptions , 2011, IEEE Transactions on Information Theory.

[23]  Constantine Caramanis,et al.  Robust PCA via Outlier Pursuit , 2010, IEEE Transactions on Information Theory.

[24]  Emmanuel J. Cand The Restricted Isometry Property and Its Implications for Compressed Sensing , 2008 .

[25]  J. Tropp,et al.  Two proposals for robust PCA using semidefinite programming , 2010, 1012.1086.

[26]  Namrata Vaswani,et al.  An Online Algorithm for Separating Sparse and Low-Dimensional Signal Sequences From Their Sum , 2013, IEEE Transactions on Signal Processing.

[27]  Namrata Vaswani,et al.  Real-time Robust Principal Components' Pursuit , 2010, 2010 48th Annual Allerton Conference on Communication, Control, and Computing (Allerton).

[28]  Gilad Lerman,et al.  A novel M-estimator for robust PCA , 2011, J. Mach. Learn. Res..

[29]  Pablo A. Parrilo,et al.  The Convex Geometry of Linear Inverse Problems , 2010, Foundations of Computational Mathematics.

[30]  강승택 2006 IEEE International Symposium on EMC를 다녀와서 , 2006 .

[31]  Shuicheng Yan,et al.  Online Robust PCA via Stochastic Optimization , 2013, NIPS.

[32]  Laura Balzano,et al.  Incremental gradient on the Grassmannian for online foreground and background separation in subsampled video , 2012, 2012 IEEE Conference on Computer Vision and Pattern Recognition.

[33]  Pablo A. Parrilo,et al.  Rank-Sparsity Incoherence for Matrix Decomposition , 2009, SIAM J. Optim..

[34]  Namrata Vaswani,et al.  Practical ReProCS for separating sparse and low-dimensional signal sequences from their sum — Part 2 , 2014, 2014 IEEE Global Conference on Signal and Information Processing (GlobalSIP).

[35]  Yi Ma,et al.  Compressive principal component pursuit , 2012, 2012 IEEE International Symposium on Information Theory Proceedings.