README.md

ClassifierToolbox : A Matlab toolbox for classifier.

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Authors: Hiroyuki Kasai

Last page update: Seo. 11, 2017

Latest library version: 1.0.7 (see Release notes for more info)

Introduction

This package provides various tools for classification, e.g., image classification, face recogntion, and related applicaitons.

List of algorithms

• Basis
  • PCA (Principal component analysis)
    • M. Turk and A. Pentland, "Eigenfaces for recognition," J. Cognitive Neurosci," vol.3, no.1, pp.71-86, 1991.
    • See also wikipedia.
  • ICA (Independent component analysis)
    • See wikipedia.
  • LDA (Linear discriminant analysis)
    • P. N. Belhumeur, J. P. Hespanha, and D. I. Kriegman, "Eigenfaces vs. Fisherfaces: recognition using class specific linear projection," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.19, no.7, pp.711-720, 1997.
    • See also wikipedia.
  • SVM (Support vector machine)
    • See wikipedia
    • Use Matlab built-in library (svmfitcsvm and predict).
• LRC variant
  • LRC (Linear regression classification)
    • I. Nassem, M. Bennamoun, "Linear regression for face recognition," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.32, no.11, 2010.
  • LDRC (Linear discriminant regression classificatoin)
    • S.-M. Huang and J.-F. Yang, "Linear discriminant regression classification for face recognition," IEEE Signal Processing Letters, vol.20, no.1, pp.91-94, 2013.
  • LCDRC (Linear collaborative discriminant regression classificatoin)
    • X. Qu, S. Kim, R. Cui and H. J. Kim, "Linear collaborative discriminant regression classification for face recognition," J. Visual Communication Image Represetation, vol.31, pp. 312-319, 2015.
• CRC (Collaborative representation based classification)
  • L. Zhanga, M. Yanga, and X. Feng, "Sparse representation or collaborative representation: which helps face recognition?," Proceedings of the 2011 International Conference on Computer Vision (ICCV'11), pp. 471-478, 2011.
• LSR variant
  • LSR (Least squares regression)
  • DERLR (Discriminative elastic-net regularized linear regression)
    • Z. Zhang, Z. Lai, Y. Xu, L. Shao and G. S. Xie, "Discriminative elastic-net regularized linear regression," IEEE Transactions on Image Processing, vol.26, no.3, pp.1466-1481, 2017.
• Low-rank matrix factorization based
  • NMF (Non-negative matrix factorization)
    • Please refer NMFLibrary.
  • Robust PCA classifier
    • E. Candes, X. Li, Y. Ma, and J. Wright, "Robust Principal Component Analysis?," Journal of the ACM, vol.58, no.3, 2011.
    • Classifier uses SRC.
    • Use SparseGDLibrary.
• RCM based
  • RCM+kNN (Region covariance matrix algorithm)
    • O. Tuzel, F. Porikli, and P. Meer "Region covariance: a fast descriptor for detection and classification," European Conference on Computer Vision (ECCV2006), pp.589-600, 2006.
  • GRCM+kNN (Gabor-wavelet-based region covariance matrix algorithm)
    • Y. Pang, Y. Yuan, and X. Li, "Gabor-based Region covariance matrices for face recognition," IEEE Transactions on Circuits and Systems for Video Technology vol.18, no.7, 2008.
• SRC variant
  • SRC (Sparse representation based classifcation)
    • J. Wright, A. Yang, A. Ganesh, S. Sastry, and Y. Ma, "Robust face recognition via sparse representation," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.31, no.2, pp.210-227, 2009.
  • ESRC (Extended sparse representation based classifcation)
    • W. Deng, J. Hu, and J. Guo, "Extended SRC: Undersampled face recognition via intraclass variant dictionary," IEEE Transation on Pattern Analysis Machine Intelligence, vol.34, no.9, pp.1864-1870, 2012.
  • SSRC (Superposed sparse representation based classifcation)
    • W. Deng, J. Hu, and J. Guo, "In defense of sparsity based face recognition," IEEE Conference on Computer Vision and Pattern Recognition (CVPR2013), 2013.
  • SRC-RLS
    • M. Iliadis, L. Spinoulas, A. S. Berahas, H. Wang, and A. K. Katsaggelos, "Sparse representation and least squares-based classification in face recognition," Proceedings of the 22nd European Signal Processing Conference (EUSIPCO), 2014.
  • SDR-SLR (Sparse- and dense-hybrid representation and supervised low-rank)
    • X. Jiang, and J. Lai, "Sparse and dense hybrid representation via dictionary decomposition for face recognition," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.37, no.5, pp.1067-1079, 2015.
• Dictionary learning based
  • K-SVD
    • M. Aharon, M. Elad, and A.M. Bruckstein, "The K-SVD: An algorithm for designing of overcomplete dictionaries for sparse representation", IEEE Transactions On Signal Processing, vol.54, no.11, pp.4311-4322, November 2006.
  • LC-KSVD (Label Consistent K-SVD)
    • Z. Jiang, Z. Lin, L. S. Davis, "Learning a discriminative dictionary for sparse coding via label consistent K-SVD," IEEE Conference on Computer Vision and Pattern Recognition (CVPR2011), 2011.
    • Z. Jiang, Z. Lin, L. S. Davis, "Label consistent K-SVD: learning A discriminative dictionary for recognition," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.35, no.11, pp.2651-2664, 2013.
  • FDDL (Fisher Discriminative Dictionary Learning)
    • M. Yang, L. Zhang, X. Feng, and D. Zhang, "Fisher discrimination dictionary learning for sparse representation," IEEE International Conference on Computer Vision (ICCV), 2011.
  • JDDRDL
    • Z. Feng, M. Yang, L. Zhang, Y. Liu, and D. Zhang, "Joint discriminative dimensionality reduction and dictionary learning for face recognition," Pattern Recognition, vol.46, pp.2134-2143, 2013.
• Geometry-aware
  • R-SRC and R-DL-SC (Riemannian dictionary learning and sparse coding for positive definite matrices)
    • A. Cherian and S. Sra, "Riemannian dictionary learning and sparse coding for positive definite matrices," IEEE Transactions on Neural Networks and Learning Systems (TNNLS), 2016.
  • R-KSRC (Stein kernel) (a.k.a. RSR) (Riemannian kernelized sparse representation classification)
    • M. Harandi, R. Hartley, B. Lovell and C. Sanderson, "Sparse coding on symmetric positive definite manifolds using bregman divergences," IEEE Transactions on Neural Networks and Learning Systems (TNNLS), 2016.
    • M. Harandi, C. Sanderson, R. Hartley and B. Lovell, "Sparse coding and dictionary learning for symmetric positive definite matrices: a kernel approach," European Conference on Computer Vision (ECCV), 2012.
  • R-KSRC (Log-Euclidean kernel) (Riemannian kernelized sparse representation classification)
    • P. Li, Q. Wang, W. Zuo, and L. Zhang, "Log-Euclidean kernels for sparse representation and dictionary learning," IEEE International Conference on Computer Vision (ICCV), 2013.
    • S. Jayasumana, R. Hartley, M. Salzmann, H. Li, and M. Harandi, "Kernel methods on the Riemannian manifold of symmetric positive definite matrices," IEEE Conference on Computer Vision and Pattern Recognition (CVPR2013), 2013.
    • S. Jayasumana, R. Hartley, M. Salzmann, H. Li, and M. Harandi, "Kernel methods on the Riemannian manifold with Gaussian RBF Kernels," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.37, no.12, 2015.
  • [Reference] R-KSRC (Deta-dependent kernel) [Not included in this package]
    • Y. Wu, Y. Jia, P. Li, J. Zhang, and J. Yuan, "Manifold kernel sparse representation of symmetric positive definite matrices and its applications," IEEE Transactions on Image Processing, vol.24, no.11, 2015.
  • R-DR (Riemannian dimensinality reduction)
    • M. Harandi, M. Salzmann and R. Hartley, "From manifold to manifold: geometry-aware dimensionality reduction for SPD matrices," European Conference on Computer Vision (ECCV), 2014.

Folders and files

./                              - Top directory.
./README.md                     - This readme file.
./run_me_first.m                - The scipt that you need to run first.
./demo.m                        - Demonstration script to check and understand this package easily. 
|algorithm/                     - Algorithms for classifcations.
|auxiliary/                     - Some auxiliary tools for this project.
|demo_examples/                 - Some demonstration files.
|lib/                           - 3rd party tools.
|dataset/                       - Folder where datasets are stored.

First to do: configure path

Run run_me_first for path configurations.

%% First run the setup script
run_me_first; 

Second to do: download datasets and external libraries

Run download for downloading datasets and external libraries.

%% Run the downloading script
download; 

• If your computer is behind a proxy server, please configure your Matlab setting. See this.

Usage example: ORL face dateset demo: 3 steps!

Now, just execute demo for demonstration of this package.

%% Execute the demonstration script
demo; 

The "demo.m" file contains below.

%% load data
load('./dataset/AR_Face_img_60x43.mat');

%% set option
options.verbose = true;

%% LSR
[accuracy_lsr, ~, ~] = lsr(TrainSet, TestSet, train_num, test_num, class_num, 0.001, options);

%% LRC
accuracy_lrc = lrc(TrainSet, TestSet, test_num, class_num, options);

%% show recognition accuracy
fprintf('# LSR: Accuracy = %5.5f\n', accuracy_lsr);
fprintf('# LRC: Accuracy = %5.5f\n', accuracy_lrc);

Let take a closer look at the code above bit by bit. The procedure has only 3 steps!

Step 1: Load data

First, we load datasets including train set and test set.

load('./dataset/AR_Face_img_60x43.mat');

Step 2: Perform solver

Now, you can perform optimization solvers, i.e., LSR and LRC with appropriate paramters.

%% LSR
[accuracy_lsr, ~, ~] = lsr(TrainSet, TestSet, train_num, test_num, class_num, 0.001, options);

%% LRC
accuracy_lrc = lrc(TrainSet, TestSet, test_num, class_num, options);

Step 3: Show recognition accuracy

Finally, the final recognition accuracis are shown.

fprintf('# LSR: Accuracy = %5.5f\n', accuracy_lsr);
fprintf('# LRC: Accuracy = %5.5f\n', accuracy_lrc);

That's it!

License

• This toobox is free, non-commercial and open source.
• The code provided in this toobox should only be used for academic/research purposes.

Third party tools, libraries, and packages.

• Third party files are included.
  • OMPBox is used for OMP (orthogonal matching pursuit) algorithm.
  • KSVDBox is used for K-SVD algorithm.
  • SPAMS is used for various lasso problems.
  • LC-KSVD.
  • FDDL.
  • RSR.
  • R-DR.
  • R-SRC and R-DL-SC.
  • Learning Discriminative Stein Kernel for SPD Matrices and Its Applications.
  • SDR-SLR.
  • R-KSRC (Log-Euclidean kernel).
  • DERLR.
  • JDDRDL;
  • JACOBI_EIGENVALUE is a MATLAB library which computes the eigenvalues and eigenvectors of a real symmetric matrix.
  • NMFLibrary is for NMF.
  • SparseGDLibrary is for Robust PCA classifier.
• Note that please see the corresponding license for each.

Problems or questions

If you have any problems or questions, please contact the author: Hiroyuki Kasai (email: hiroyuki dot kasai at waseda dot jp)

Release Notes

• Version 1.0.7 (Sep. 11, 2017)
  • Fix bugs.
• Version 1.0.6 (Aug. 03, 2017)
  • Add R-DR, R-SRC, others.
• Version 1.0.5 (July 27, 2017)
  • Add JDDRDL and others.
• Version 1.0.4 (July 11, 2017)
  • Add and modify SSRC etc.
• Version 1.0.3 (July 10, 2017)
  • Add and modify SDR-SLR etc.
• Version 1.0.2 (July 07, 2017)
  • Add and modify RSR, SVM etc.
• Version 1.0.1 (July 06, 2017)
  • Add and modify many items.
• Version 1.0.0 (July 01, 2017)
  • Initial version.