Sparse Representation-based Object Recognition

In this chapter, we show how the sparse representation framework can be used to develop robust algorithms for object classification [156], [112], [106]. In particular, we will outline the Sparse Representation-based Classification (SRC) algorithm [156] and present its applications in robust biometrics recognition [156], [112], [111].

[1]  Terence Tao,et al.  The Dantzig selector: Statistical estimation when P is much larger than n , 2005, math/0506081.

[2]  Richard G. Baraniuk,et al.  An Architecture for Compressive Imaging , 2006, 2006 International Conference on Image Processing.

[3]  J. Goodman Introduction to Fourier optics , 1969 .

[4]  Rama Chellappa,et al.  Dictionary-Based Face Recognition from Video , 2012, ECCV.

[5]  Rama Chellappa,et al.  Joint Sparsity-Based Robust Multimodal Biometrics Recognition , 2012, ECCV Workshops.

[6]  Hao Ling,et al.  Time-Frequency Transforms for Radar Imaging and Signal Analysis , 2002 .

[7]  R. Tibshirani Regression Shrinkage and Selection via the Lasso , 1996 .

[8]  Larry S. Davis,et al.  Learning a discriminative dictionary for sparse coding via label consistent K-SVD , 2011, CVPR 2011.

[9]  Rama Chellappa,et al.  Object Detection, Tracking and Recognition for Multiple Smart Cameras , 2008, Proceedings of the IEEE.

[10]  Namrata Vaswani,et al.  Kalman filtered Compressed Sensing , 2008, 2008 15th IEEE International Conference on Image Processing.

[11]  Yaakov Tsaig,et al.  Breakdown of equivalence between the minimal l1-norm solution and the sparsest solution , 2006, Signal Process..

[12]  Rama Chellappa,et al.  Design of Non-Linear Discriminative Dictionaries for Image Classification , 2012, ACCV.

[13]  E R Dowski,et al.  Realizations of focus invariance in optical-digital systems with wave-front coding. , 1997, Applied optics.

[14]  Nuno Vasconcelos,et al.  Probabilistic kernels for the classification of auto-regressive visual processes , 2005, 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05).

[15]  Fatih Murat Porikli,et al.  Region Covariance: A Fast Descriptor for Detection and Classification , 2006, ECCV.

[16]  Guillermo Sapiro,et al.  Sparse Representation for Computer Vision and Pattern Recognition , 2010, Proceedings of the IEEE.

[17]  Rama Chellappa,et al.  Kernel dictionary learning , 2012, 2012 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).

[18]  Rémi Gribonval,et al.  Sparse representations in unions of bases , 2003, IEEE Trans. Inf. Theory.

[19]  J.N. Mait,et al.  94-GHz Imager With Extended Depth of Field , 2009, IEEE Transactions on Antennas and Propagation.

[20]  L. Carin,et al.  Compressive particle filtering for target tracking , 2009, 2009 IEEE/SP 15th Workshop on Statistical Signal Processing.

[21]  Rama Chellappa,et al.  Secure and Robust Iris Recognition Using Random Projections and Sparse Representations , 2011, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[22]  W. Clem Karl,et al.  Feature-enhanced synthetic aperture radar image formation based on nonquadratic regularization , 2001, IEEE Trans. Image Process..

[23]  Alexander J. Smola,et al.  Learning with Kernels: support vector machines, regularization, optimization, and beyond , 2001, Adaptive computation and machine learning series.

[24]  Wai Lam Chan,et al.  A single-pixel terahertz imaging system based on compressed sensing , 2008 .

[25]  Justin Romberg,et al.  Practical Signal Recovery from Random Projections , 2005 .

[26]  Jared Tanner,et al.  Phase Transitions for Greedy Sparse Approximation Algorithms , 2010, ArXiv.

[27]  R. Chellappa,et al.  Optimal Multi-View Fusion of Object Locations , 2008, 2008 IEEE Workshop on Motion and video Computing.

[28]  Mehrdad Soumekh,et al.  Synthetic Aperture Radar Signal Processing with MATLAB Algorithms , 1999 .

[29]  Michael A. Saunders,et al.  Atomic Decomposition by Basis Pursuit , 1998, SIAM J. Sci. Comput..

[30]  Emmanuel J. Candès,et al.  Signal recovery from random projections , 2005, IS&T/SPIE Electronic Imaging.

[31]  Rama Chellappa,et al.  Enforcing integrability by error correction using l1-minimization , 2009, CVPR.

[32]  Stefano Tubaro,et al.  Joint Compressive Video Coding and Analysis , 2010, IEEE Transactions on Multimedia.

[33]  Sharath Pankanti,et al.  Filterbank-based fingerprint matching , 2000, IEEE Trans. Image Process..

[34]  Rama Chellappa,et al.  Separability-based multiscale basis selection and feature extraction for signal and image classification , 1998, IEEE Trans. Image Process..

[35]  Jean Ponce,et al.  Task-Driven Dictionary Learning , 2010, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[36]  Joseph N. Mait,et al.  Sparse Fourier Sampling in Millimeter-Wave Compressive Holography , 2010 .

[37]  Rama Chellappa,et al.  Compressed sensing for Synthetic Aperture Radar imaging , 2009, 2009 16th IEEE International Conference on Image Processing (ICIP).

[38]  Cordelia Schmid,et al.  Beyond Bags of Features: Spatial Pyramid Matching for Recognizing Natural Scene Categories , 2006, 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'06).

[39]  Michael Elad,et al.  Dictionaries for Sparse Representation Modeling , 2010, Proceedings of the IEEE.

[40]  Liang-Tien Chia,et al.  Kernel Sparse Representation for Image Classification and Face Recognition , 2010, ECCV.

[41]  Ting Wang,et al.  Kernel Sparse Representation-Based Classifier , 2012, IEEE Transactions on Signal Processing.

[42]  Rama Chellappa,et al.  Adaptive rate compressive sensing for background subtraction , 2012, 2012 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).

[43]  Vishal M. Patel,et al.  Compressive passive millimeter wave imaging with extended depth of field , 2012 .

[44]  David J. Kriegman,et al.  From Few to Many: Illumination Cone Models for Face Recognition under Variable Lighting and Pose , 2001, IEEE Trans. Pattern Anal. Mach. Intell..

[45]  Guillermo Sapiro,et al.  Sparse representations for image classification: learning discriminative and reconstructive non-parametric dictionaries , 2008 .

[46]  Junfeng Yang,et al.  A Fast Alternating Direction Method for TVL1-L2 Signal Reconstruction From Partial Fourier Data , 2010, IEEE Journal of Selected Topics in Signal Processing.

[47]  Rama Chellappa,et al.  Sparsity-motivated automatic target recognition. , 2011, Applied optics.

[48]  Edward R. Dowski,et al.  A New Paradigm for Imaging Systems , 2002, PICS.

[49]  M. Lustig,et al.  Compressed Sensing MRI , 2008, IEEE Signal Processing Magazine.

[50]  Emmanuel J. Candès,et al.  Near-Optimal Signal Recovery From Random Projections: Universal Encoding Strategies? , 2004, IEEE Transactions on Information Theory.

[51]  Orges Furxhi,et al.  Compressive sensing for a sub-millimeter-wave single pixel imager , 2011, Defense + Commercial Sensing.

[52]  Libor Masek,et al.  MATLAB Source Code for a Biometric Identification System Based on Iris Patterns , 2003 .

[53]  Rama Chellappa,et al.  Sparse Representations, Compressive Sensing and dictionaries for pattern recognition , 2011, The First Asian Conference on Pattern Recognition.

[54]  Junfeng Yang,et al.  A New Alternating Minimization Algorithm for Total Variation Image Reconstruction , 2008, SIAM J. Imaging Sci..

[55]  S. Mallat VI – Wavelet zoom , 1999 .

[56]  Bhaskar D. Rao,et al.  Sparse signal reconstruction from limited data using FOCUSS: a re-weighted minimum norm algorithm , 1997, IEEE Trans. Signal Process..

[57]  Rama Chellappa,et al.  Gradient-Based Image Recovery Methods From Incomplete Fourier Measurements , 2012, IEEE Transactions on Image Processing.

[58]  Stefano Soatto,et al.  Dynamic Textures , 2003, International Journal of Computer Vision.

[59]  Damiana Lazzaro,et al.  Nonlinear Filtering for Sparse Signal Recovery From Incomplete Measurements , 2009, IEEE Transactions on Signal Processing.

[60]  Donald Goldfarb,et al.  Second-order cone programming , 2003, Math. Program..

[61]  Rama Chellappa,et al.  Compressive Acquisition of Dynamic Scenes , 2010, ECCV.

[62]  Aggelos K. Katsaggelos,et al.  Compressive passive millimeter-wave imaging , 2011, 2011 18th IEEE International Conference on Image Processing.

[63]  Victor Vianu,et al.  Invited articles section foreword , 2010, JACM.

[64]  M. Yuan,et al.  Model selection and estimation in regression with grouped variables , 2006 .

[65]  Allen Y. Yang,et al.  Robust Face Recognition via Sparse Representation , 2009, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[66]  David L Donoho,et al.  Compressed sensing , 2006, IEEE Transactions on Information Theory.

[67]  J. Tropp,et al.  CoSaMP: Iterative signal recovery from incomplete and inaccurate samples , 2008, Commun. ACM.

[68]  Mike E. Davies,et al.  Gradient Pursuits , 2008, IEEE Transactions on Signal Processing.

[69]  Wotao Yin,et al.  EdgeCS: edge guided compressive sensing reconstruction , 2010, Visual Communications and Image Processing.

[70]  Trac D. Tran,et al.  Robust multi-sensor classification via joint sparse representation , 2011, 14th International Conference on Information Fusion.

[71]  Stephen P. Boyd,et al.  Convex Optimization , 2004, Algorithms and Theory of Computation Handbook.

[72]  Rama Chellappa,et al.  Design of Non-Linear Kernel Dictionaries for Object Recognition , 2013, IEEE Transactions on Image Processing.

[73]  Stephen P. Boyd,et al.  Enhancing Sparsity by Reweighted ℓ1 Minimization , 2007, 0711.1612.

[74]  Ting Sun,et al.  Single-pixel imaging via compressive sampling , 2008, IEEE Signal Process. Mag..

[75]  Rama Chellappa,et al.  Unsupervised view and rate invariant clustering of video sequences q , 2009 .

[76]  Yonina C. Eldar,et al.  Structured Compressed Sensing: From Theory to Applications , 2011, IEEE Transactions on Signal Processing.

[77]  Guillermo Sapiro,et al.  Supervised Dictionary Learning , 2008, NIPS.

[78]  David A. Forsyth,et al.  Generalizing motion edits with Gaussian processes , 2009, ACM Trans. Graph..

[79]  Christopher J. C. Burges,et al.  A Tutorial on Support Vector Machines for Pattern Recognition , 1998, Data Mining and Knowledge Discovery.

[80]  Kjersti Engan,et al.  Method of optimal directions for frame design , 1999, 1999 IEEE International Conference on Acoustics, Speech, and Signal Processing. Proceedings. ICASSP99 (Cat. No.99CH36258).

[81]  Emmanuel J. Candès,et al.  Quantitative Robust Uncertainty Principles and Optimally Sparse Decompositions , 2004, Found. Comput. Math..

[82]  Vishal M. Patel,et al.  Passive millimeter-wave imaging with extended depth of field and sparse data , 2012, 2012 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).

[83]  Michael Elad,et al.  From Sparse Solutions of Systems of Equations to Sparse Modeling of Signals and Images , 2009, SIAM Rev..

[84]  David L. Donoho,et al.  High-Dimensional Centrally Symmetric Polytopes with Neighborliness Proportional to Dimension , 2006, Discret. Comput. Geom..

[85]  Rama Chellappa,et al.  Ieee Transactions on Pattern Analysis and Machine Intelligence 1 Matching Shape Sequences in Video with Applications in Human Movement Analysis. Ieee Transactions on Pattern Analysis and Machine Intelligence 2 , 2022 .

[86]  Mike E. Davies,et al.  Iterative Hard Thresholding for Compressed Sensing , 2008, ArXiv.

[87]  David L. Donoho,et al.  Sparse Solution Of Underdetermined Linear Equations By Stagewise Orthogonal Matching Pursuit , 2006 .

[88]  Peter Kovesi,et al.  Shapelets correlated with surface normals produce surfaces , 2005, Tenth IEEE International Conference on Computer Vision (ICCV'05) Volume 1.

[89]  Michael P. Friedlander,et al.  Probing the Pareto Frontier for Basis Pursuit Solutions , 2008, SIAM J. Sci. Comput..

[90]  Aggelos K. Katsaggelos,et al.  Compressive sampling in passive millimeter-wave imaging , 2011, Defense + Commercial Sensing.

[91]  Rama Chellappa,et al.  Sparse dictionary-based representation and recognition of action attributes , 2011, 2011 International Conference on Computer Vision.

[92]  Pieter Peers,et al.  Compressive light transport sensing , 2009, ACM Trans. Graph..

[93]  Wotao Yin,et al.  Bregman Iterative Algorithms for (cid:2) 1 -Minimization with Applications to Compressed Sensing ∗ , 2008 .

[94]  Ramesh Raskar,et al.  Coded Strobing Photography: Compressive Sensing of High Speed Periodic Videos , 2011, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[95]  N. Hamano,et al.  Digital processing of synthetic aperture radar data , 1984 .

[96]  Rama Chellappa,et al.  Information-Theoretic Dictionary Learning for Image Classification , 2012, IEEE Transactions on Pattern Analysis and Machine Intelligence.

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

[98]  E. Candès,et al.  Stable signal recovery from incomplete and inaccurate measurements , 2005, math/0503066.

[99]  Volkan Cevher,et al.  Compressed sensing for multi-view tracking and 3-D voxel reconstruction , 2008, 2008 15th IEEE International Conference on Image Processing.

[100]  T. Blumensath,et al.  Iterative Thresholding for Sparse Approximations , 2008 .

[101]  Stephen J. Wright,et al.  Sparse Reconstruction by Separable Approximation , 2008, IEEE Transactions on Signal Processing.

[102]  David J. Field,et al.  Emergence of simple-cell receptive field properties by learning a sparse code for natural images , 1996, Nature.

[103]  Rama Chellappa,et al.  Direct Analytical Methods for Solving Poisson Equations in Computer Vision Problems , 1990, IEEE Trans. Pattern Anal. Mach. Intell..

[104]  P. Bühlmann,et al.  The group lasso for logistic regression , 2008 .

[105]  Rama Chellappa,et al.  P2C2: Programmable pixel compressive camera for high speed imaging , 2011, CVPR 2011.

[106]  Y. C. Pati,et al.  Orthogonal matching pursuit: recursive function approximation with applications to wavelet decomposition , 1993, Proceedings of 27th Asilomar Conference on Signals, Systems and Computers.

[107]  Volkan Cevher,et al.  Compressive Sensing for Background Subtraction , 2008, ECCV.

[108]  Guillermo Sapiro,et al.  Discriminative learned dictionaries for local image analysis , 2008, 2008 IEEE Conference on Computer Vision and Pattern Recognition.

[109]  Stéphane Mallat,et al.  Matching pursuits with time-frequency dictionaries , 1993, IEEE Trans. Signal Process..

[110]  Mário A. T. Figueiredo,et al.  Gradient Projection for Sparse Reconstruction: Application to Compressed Sensing and Other Inverse Problems , 2007, IEEE Journal of Selected Topics in Signal Processing.

[111]  Armando Manduca,et al.  Highly Undersampled Magnetic Resonance Image Reconstruction via Homotopic $\ell_{0}$ -Minimization , 2009, IEEE Transactions on Medical Imaging.

[112]  Richard G Baraniuk,et al.  More Is Less: Signal Processing and the Data Deluge , 2011, Science.

[113]  R. M. Willett,et al.  Compressed sensing for practical optical imaging systems: A tutorial , 2011, IEEE Photonics Conference 2012.

[114]  Deanna Needell,et al.  Signal Recovery From Incomplete and Inaccurate Measurements Via Regularized Orthogonal Matching Pursuit , 2007, IEEE Journal of Selected Topics in Signal Processing.

[115]  Rama Chellappa,et al.  Sparsity inspired selection and recognition of iris images , 2009, 2009 IEEE 3rd International Conference on Biometrics: Theory, Applications, and Systems.

[116]  José M. Bioucas-Dias,et al.  A New TwIST: Two-Step Iterative Shrinkage/Thresholding Algorithms for Image Restoration , 2007, IEEE Transactions on Image Processing.

[117]  P. Tseng Convergence of a Block Coordinate Descent Method for Nondifferentiable Minimization , 2001 .

[118]  Baoxin Li,et al.  Discriminative K-SVD for dictionary learning in face recognition , 2010, 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

[119]  Lawrence Carin,et al.  Sparse multinomial logistic regression: fast algorithms and generalization bounds , 2005, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[120]  Rama Chellappa,et al.  Learning discriminative dictionaries with partially labeled data , 2012, 2012 19th IEEE International Conference on Image Processing.

[121]  Pascal Frossard,et al.  Semantic Coding by Supervised Dimensionality Reduction , 2008, IEEE Transactions on Multimedia.

[122]  D. Munson,et al.  A tomographic formulation of spotlight-mode synthetic aperture radar , 1983, Proceedings of the IEEE.

[123]  Arian Maleki,et al.  Optimally Tuned Iterative Reconstruction Algorithms for Compressed Sensing , 2009, IEEE Journal of Selected Topics in Signal Processing.

[124]  R. N. Anderton,et al.  Millimeter-Wave and Submillimeter-Wave Imaging for Security and Surveillance , 2007, Proceedings of the IEEE.

[125]  Emmanuel J. Candès,et al.  Robust uncertainty principles: exact signal reconstruction from highly incomplete frequency information , 2004, IEEE Transactions on Information Theory.

[126]  Michael Elad,et al.  Sparse and Redundant Representations - From Theory to Applications in Signal and Image Processing , 2010 .

[127]  Rama Chellappa,et al.  Synthesis-based recognition of low resolution faces , 2011, 2011 International Joint Conference on Biometrics (IJCB).

[128]  David R. Bull,et al.  Projective image restoration using sparsity regularization , 2013, 2013 IEEE International Conference on Image Processing.

[129]  W. Carrara,et al.  Spotlight synthetic aperture radar : signal processing algorithms , 1995 .

[130]  Arun Ross,et al.  Handbook of Multibiometrics , 2006, The Kluwer international series on biometrics.

[131]  M. Elad,et al.  $rm K$-SVD: An Algorithm for Designing Overcomplete Dictionaries for Sparse Representation , 2006, IEEE Transactions on Signal Processing.

[132]  L. Yujiri,et al.  Passive Millimeter Wave Imaging , 2003, 2006 IEEE MTT-S International Microwave Symposium Digest.

[133]  Stephen P. Boyd,et al.  An Interior-Point Method for Large-Scale $\ell_1$-Regularized Least Squares , 2007, IEEE Journal of Selected Topics in Signal Processing.

[134]  Ke Huang,et al.  Sparse Representation for Signal Classification , 2006, NIPS.

[135]  D. Donoho,et al.  Sparse MRI: The application of compressed sensing for rapid MR imaging , 2007, Magnetic resonance in medicine.

[136]  Andy M. Yip,et al.  Recent Developments in Total Variation Image Restoration , 2004 .

[137]  Rama Chellappa,et al.  A Method for Enforcing Integrability in Shape from Shading Algorithms , 1988, IEEE Trans. Pattern Anal. Mach. Intell..

[138]  M. Rudelson,et al.  On sparse reconstruction from Fourier and Gaussian measurements , 2008 .

[139]  Michael Elad,et al.  Optimally sparse representation in general (nonorthogonal) dictionaries via ℓ1 minimization , 2003, Proceedings of the National Academy of Sciences of the United States of America.

[140]  Yaakov Tsaig,et al.  Extensions of compressed sensing , 2006, Signal Process..

[141]  Joseph N Mait,et al.  Millimeter-wave compressive holography. , 2010, Applied optics.

[142]  David L. Donoho,et al.  Precise Undersampling Theorems , 2010, Proceedings of the IEEE.

[143]  Rick Chartrand,et al.  Exact Reconstruction of Sparse Signals via Nonconvex Minimization , 2007, IEEE Signal Processing Letters.

[144]  Joel A. Tropp,et al.  Signal Recovery From Random Measurements Via Orthogonal Matching Pursuit , 2007, IEEE Transactions on Information Theory.

[145]  Rama Chellappa,et al.  Automatic target recognition based on simultaneous sparse representation , 2010, 2010 IEEE International Conference on Image Processing.

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

[147]  J. Tropp Algorithms for simultaneous sparse approximation. Part II: Convex relaxation , 2006, Signal Process..

[148]  Damon L. Woodard,et al.  Non-ideal iris segmentation using graph cuts , 2008, 2008 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops.

[149]  Rama Chellappa,et al.  Compressed Synthetic Aperture Radar , 2010, IEEE Journal of Selected Topics in Signal Processing.

[150]  Pawan Sinha,et al.  Face Recognition by Humans: Nineteen Results All Computer Vision Researchers Should Know About , 2006, Proceedings of the IEEE.

[151]  I. Daubechies,et al.  An iterative thresholding algorithm for linear inverse problems with a sparsity constraint , 2003, math/0307152.

[152]  Joel A. Tropp,et al.  Algorithms for simultaneous sparse approximation. Part I: Greedy pursuit , 2006, Signal Process..

[153]  Yaakov Tsaig,et al.  Recent advances in sparsity-driven signal recovery , 2005, Proceedings. (ICASSP '05). IEEE International Conference on Acoustics, Speech, and Signal Processing, 2005..

[154]  John Daugman How iris recognition works , 2004 .

[155]  Trac D. Tran,et al.  Hyperspectral Image Classification via Kernel Sparse Representation , 2011, IEEE Transactions on Geoscience and Remote Sensing.

[156]  Rama Chellappa,et al.  Dictionary-Based Face Recognition Under Variable Lighting and Pose , 2012, IEEE Transactions on Information Forensics and Security.

[157]  Shuicheng Yan,et al.  Visual classification with multi-task joint sparse representation , 2010, 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

[158]  Aswin C. Sankaranarayanan,et al.  Compressive Sensing , 2008, Computer Vision, A Reference Guide.

[159]  R.G. Baraniuk,et al.  Compressive Sensing [Lecture Notes] , 2007, IEEE Signal Processing Magazine.

[160]  W. Cathey,et al.  Extended depth of field through wave-front coding. , 1995, Applied optics.

[161]  David Zhang,et al.  Fisher Discrimination Dictionary Learning for sparse representation , 2011, 2011 International Conference on Computer Vision.