Authors: Hiroyuki Kasai
Last page update: Seo. 11, 2017
Latest library version: 1.0.7 (see Release notes for more info)
This package provides various tools for classification, e.g., image classification, face recogntion, and related applicaitons.
- 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).
- PCA (Principal component analysis)
- 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.
- LRC (Linear regression classification)
- 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.
- NMF (Non-negative matrix factorization)
- 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.
- RCM+kNN (Region covariance matrix algorithm)
- 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.
- SRC (Sparse representation based classifcation)
- 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.
- K-SVD
- 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.
- R-SRC and R-DL-SC (Riemannian dictionary learning and sparse coding for positive definite matrices)
./ - 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.
Run run_me_first
for path configurations.
%% First run the setup script
run_me_first;
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.
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!
- 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 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.
If you have any problems or questions, please contact the author: Hiroyuki Kasai (email: kasai at is dot uec dot ac dot jp)
- 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.