# ClassifierToolbox **Repository Path**: creater/ClassifierToolbox ## Basic Information - **Project Name**: ClassifierToolbox - **Description**: A MATLAB toolbox for face classifier 1.0.7 - **Primary Language**: Matlab - **License**: MIT - **Default Branch**: master - **Homepage**: None - **GVP Project**: No ## Statistics - **Stars**: 0 - **Forks**: 0 - **Created**: 2018-09-14 - **Last Updated**: 2020-12-19 ## Categories & Tags **Categories**: Uncategorized **Tags**: None ## README # ClassifierToolbox : A Matlab toolbox for classifier. ---------- Authors: [Hiroyuki Kasai](http://kasai.kasailab.com/) 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](https://www.cs.ucsb.edu/~mturk/Papers/jcn.pdf)," J. Cognitive Neurosci," vol.3, no.1, pp.71-86, 1991. - See also [wikipedia](https://en.wikipedia.org/wiki/Principal_component_analysis). - **ICA** (Independent component analysis) - See [wikipedia](https://en.wikipedia.org/wiki/Independent_component_analysis). - **LDA** (Linear discriminant analysis) - P. N. Belhumeur, J. P. Hespanha, and D. I. Kriegman, "[Eigenfaces vs. Fisherfaces: recognition using class specific linear projection](http://ieeexplore.ieee.org/document/598228/)," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.19, no.7, pp.711-720, 1997. - See also [wikipedia](https://en.wikipedia.org/wiki/Linear_discriminant_analysis). - **SVM** (Support vector machine) - See [wikipedia](https://en.wikipedia.org/wiki/Support_vector_machine) - Use Matlab built-in library (svmfitcsvm and predict). - **LRC** variant - **LRC** (Linear regression classification) - I. Nassem, M. Bennamoun, "[Linear regression for face recognition](http://ieeexplore.ieee.org/document/5506092/)," 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](http://ieeexplore.ieee.org/document/6373697/)," 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](http://www.sciencedirect.com/science/article/pii/S1047320315001297)," 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?](http://dl.acm.org/citation.cfm?id=2356341)," 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](http://ieeexplore.ieee.org/document/7814255/)," 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](https://github.com/hiroyuki-kasai/NMFLibrary). - **[Robust PCA](https://en.wikipedia.org/wiki/Robust_principal_component_analysis) classifier** - E. Candes, X. Li, Y. Ma, and J. Wright, "[Robust Principal Component Analysis?](http://perception.csl.illinois.edu/matrix-rank/Files/RPCA_JACM.pdf)," Journal of the ACM, vol.58, no.3, 2011. - Classifier uses SRC. - Use [SparseGDLibrary](https://github.com/hiroyuki-kasai/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](https://link.springer.com/chapter/10.1007/11744047_45)," 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](http://ieeexplore.ieee.org/document/4498432/)," 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](http://ieeexplore.ieee.org/document/4483511/)," 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](http://ieeexplore.ieee.org/document/6133293/)," 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](http://ieeexplore.ieee.org/document/6618902/)," 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](http://ieeexplore.ieee.org/document/6952144/)," 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](http://ieeexplore.ieee.org/document/6905839/)," 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](http://ieeexplore.ieee.org/document/1710377/)", 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](http://ieeexplore.ieee.org/abstract/document/5995354/)," 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](http://ieeexplore.ieee.org/document/6516503/)," 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](http://ieeexplore.ieee.org/document/6126286/)," 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](http://www.sciencedirect.com/science/article/pii/S0031320313000538)," 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](http://ieeexplore.ieee.org/document/7565529/)," 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](http://ieeexplore.ieee.org/document/7024121/)," 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](https://drive.google.com/uc?export=download&id=0B9_PW9TCpxT0eW00U1FVd0xaSmM)," 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](http://ieeexplore.ieee.org/document/6751309/)," 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](http://ieeexplore.ieee.org/document/6618861/)," 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](http://ieeexplore.ieee.org/document/7063231/)," 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](http://ieeexplore.ieee.org/document/7145428/)," 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](https://link.springer.com/chapter/10.1007/978-3-319-10605-2_2)," 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. ```Matlab %% First run the setup script run_me_first; ```
Second to do: download datasets and external libraries ---------------------------- Run `download` for downloading datasets and external libraries. ```Matlab %% Run the downloading script download; ``` - If your computer is behind a proxy server, please configure your Matlab setting. See [this](http://jp.mathworks.com/help/matlab/import_export/proxy.html?lang=en).
Usage example: ORL face dateset demo: 3 steps! ---------------------------- Now, just execute `demo` for demonstration of this package. ```Matlab %% Execute the demonstration script demo; ```
The "**demo.m**" file contains below. ```Matlab %% 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. ```Matlab 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. ```Matlab %% 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. ```Matlab 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](http://www.cs.technion.ac.il/~ronrubin/Software/ompbox10.zip) is used for [OMP](https://en.wikipedia.org/wiki/Matching_pursuit) (orthogonal matching pursuit) algorithm. - [KSVDBox](http://www.cs.technion.ac.il/~ronrubin/Software/ksvdbox13.zip) is used for K-SVD algorithm. - [SPAMS](http://spams-devel.gforge.inria.fr/downloads.html) is used for various lasso problems. - [LC-KSVD](https://www.umiacs.umd.edu/~zhuolin/projectlcksvd.html). - [FDDL](http://www4.comp.polyu.edu.hk/~cslzhang/code/FDDL.zip). - [RSR](https://drive.google.com/uc?export=download&id=0B9_PW9TCpxT0ZVpGRDNLX3NCbXc). - [R-DR](https://drive.google.com/uc?export=download&id=0B9_PW9TCpxT0RkY2NXdURnhYa00). - [R-SRC and R-DL-SC](http://users.cecs.anu.edu.au/~cherian/code/rspdl.tar.gz). - [Learning Discriminative Stein Kernel for SPD Matrices and Its Applications](https://github.com/seuzjj/DSK/archive/master.zip). - [SDR-SLR](http://www3.ntu.edu.sg/home/EXDJiang/CodesPAMI2015.zip). - [R-KSRC (Log-Euclidean kernel)](http://www4.comp.polyu.edu.hk/~cslzhang/LogEKernel_Project/ICCV_LogEKernel_Code.zip). - DERLR. - [JDDRDL](); - [JACOBI_EIGENVALUE](https://people.sc.fsu.edu/~jburkardt/m_src/jacobi_eigenvalue/jacobi_eigenvalue.html) is a MATLAB library which computes the eigenvalues and eigenvectors of a real symmetric matrix. - [NMFLibrary](https://github.com/hiroyuki-kasai/NMFLibrary) is for [NMF](https://en.wikipedia.org/wiki/Non-negative_matrix_factorization). - [SparseGDLibrary](https://github.com/hiroyuki-kasai/SparseGDLibrary) is for [Robust PCA](https://en.wikipedia.org/wiki/Robust_principal_component_analysis) 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](http://kasai.kasailab.com/) (email: kasai **at** is **dot** uec **dot** ac **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.