Discriminative Paired Dictionary Learning for Visual Recognition

A Paired Discriminative K-SVD (PD-KSVD) dictionary learning method is presented in this paper for visual recognition. To achieve high discrimination and low reconstruction errors simultaneously for sparse coding, we propose to learn class-specific sub-dictionaries from pairs of positive and negative classes to jointly reduce the reconstruction errors of positive classes while keeping the reconstruction errors of negative classes high. Then, multiple sub-dictionaries are concatenated with respect to the same negative class so that the non-zero sparse coefficients can be discriminatively distributed to improve classification accuracy. Compared to the current dictionary learning methods, the proposed PD-KSVD method achieves very competitive performance in a variety of visual recognition tasks on several publicly available datasets.

[1]  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.

[2]  J. C. BurgesChristopher.  A Tutorial on Support Vector Machines for Pattern Recognition , 1998 .

[3]  Kjersti Engan,et al.  Frame based signal compression using method of optimal directions (MOD) , 1999, ISCAS'99. Proceedings of the 1999 IEEE International Symposium on Circuits and Systems VLSI (Cat. No.99CH36349).

[4]  Pietro Perona,et al.  Learning Generative Visual Models from Few Training Examples: An Incremental Bayesian Approach Tested on 101 Object Categories , 2004, 2004 Conference on Computer Vision and Pattern Recognition Workshop.

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

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

[7]  G. Griffin,et al.  Caltech-256 Object Category Dataset , 2007 .

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

[9]  Antonio Torralba,et al.  Recognizing indoor scenes , 2009, 2009 IEEE Conference on Computer Vision and Pattern Recognition.

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

[11]  Yihong Gong,et al.  Locality-constrained Linear Coding for image classification , 2010, 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

[12]  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.

[13]  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.

[14]  Chih-Jen Lin,et al.  LIBSVM: A library for support vector machines , 2011, TIST.

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

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

[17]  V. Kuznetsov,et al.  A robust tool for discriminative analysis and feature selection in paired samples impacts the identification of the genes essential for reprogramming lung tissue to adenocarcinoma , 2011, BMC Genomics.

[18]  Guillermo Sapiro,et al.  Sparse Modeling of Human Actions from Motion Imagery , 2012, International Journal of Computer Vision.

[19]  Michael Elad,et al.  Analysis K-SVD: A Dictionary-Learning Algorithm for the Analysis Sparse Model , 2013, IEEE Transactions on Signal Processing.

[20]  Jian-Feng Cai,et al.  Fast Sparsity-Based Orthogonal Dictionary Learning for Image Restoration , 2013, 2013 IEEE International Conference on Computer Vision.

[21]  Bhabesh Deka,et al.  Single image super-resolution using compressive sensing with learned overcomplete dictionary , 2013, 2013 Fourth National Conference on Computer Vision, Pattern Recognition, Image Processing and Graphics (NCVPRIPG).

[22]  Qingming Huang,et al.  Multi-level Discriminative Dictionary Learning towards Hierarchical Visual Categorization , 2013, 2013 IEEE Conference on Computer Vision and Pattern Recognition.

[23]  Yan Liu,et al.  Joint discriminative dimensionality reduction and dictionary learning for face recognition , 2013, Pattern Recognit..

[24]  Haiping Lu,et al.  Learning Canonical Correlations of Paired Tensor Sets Via Tensor-to-Vector Projection , 2013, IJCAI.

[25]  Qingshan Liu,et al.  Learning Discriminative Dictionary for Group Sparse Representation , 2014, IEEE Transactions on Image Processing.

[26]  刘青山,et al.  Learning Discriminative Dictionary for Group Sparse Representation , 2014 .

[27]  Lei Zhang,et al.  Support Vector Guided Dictionary Learning , 2014, ECCV.

[28]  Lei Zhang,et al.  Projective dictionary pair learning for pattern classification , 2014, NIPS.

[29]  Luc Van Gool,et al.  Latent Dictionary Learning for Sparse Representation Based Classification , 2014, 2014 IEEE Conference on Computer Vision and Pattern Recognition.