Dictionaries for image-based recognition

In recent years, Sparse Representation (SR) and Dictionary Learning (DL) have emerged as powerful tools for efficiently processing of image and video data in non-traditional ways. An area of promise for these theories is object recognition. In this paper, we present an overview of SR and DR and examine several interesting object recognition approaches using these theories. We will also explore the use of non-linear kernel SR as well as DL methods in many computer vision problems including object recognition, multimodal biometrics recognition, and domain adaptation.

[1]  Ehsan Elhamifar,et al.  Sparse subspace clustering , 2009, 2009 IEEE Conference on Computer Vision and Pattern Recognition.

[2]  Emmanuel J. Candès,et al.  A Geometric Analysis of Subspace Clustering with Outliers , 2011, ArXiv.

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

[4]  Avrim Blum,et al.  The Bottleneck , 2021, Monopsony Capitalism.

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

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

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

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

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

[10]  Alexander Zien,et al.  Semi-Supervised Learning , 2006 .

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

[12]  S. Sathiya Keerthi,et al.  Large scale semi-supervised linear SVMs , 2006, SIGIR.

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

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

[15]  Rama Chellappa,et al.  Rotation invariant simultaneous clustering and dictionary learning , 2012, 2012 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).

[16]  Rama Chellappa,et al.  Sectored Random Projections for Cancelable Iris Biometrics , 2010, 2010 IEEE International Conference on Acoustics, Speech and Signal Processing.

[17]  Rama Chellappa,et al.  Sparse Embedding: A Framework for Sparsity Promoting Dimensionality Reduction , 2012, ECCV.

[18]  Rama Chellappa,et al.  Domain Adaptive Dictionary Learning , 2012, ECCV.

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

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

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

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

[23]  Guillermo Sapiro,et al.  Classification and clustering via dictionary learning with structured incoherence and shared features , 2010, 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

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

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

[26]  A. Bruckstein,et al.  K-SVD : An Algorithm for Designing of Overcomplete Dictionaries for Sparse Representation , 2005 .

[27]  René Vidal,et al.  Motion segmentation via robust subspace separation in the presence of outlying, incomplete, or corrupted trajectories , 2008, 2008 IEEE Conference on Computer Vision and Pattern Recognition.

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

[29]  Guillermo Sapiro,et al.  Dictionary learning and sparse coding for unsupervised clustering , 2010, 2010 IEEE International Conference on Acoustics, Speech and Signal Processing.

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

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

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

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

[34]  Michael Elad,et al.  Sparse and Redundant Representation Modeling—What Next? , 2012, IEEE Signal Processing Letters.

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

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

[37]  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.

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

[39]  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.

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

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

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

[43]  Joel A. Tropp,et al.  Greed is good: algorithmic results for sparse approximation , 2004, IEEE Transactions on Information Theory.

[44]  Michael Elad,et al.  On the Role of Sparse and Redundant Representations in Image Processing , 2010, Proceedings of the IEEE.

[45]  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).

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