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Ivan Damnjanovic, 2011-03-31 03:10 PM
FAQ¶
Q1: What is SMALLbox?¶
SMALLbox is an evaluation framework for processing signals using adaptive sparse structured representations. SMALLbox is built within FP7 EU FET project called "SMALL" that is exploring new provably good methods to obtain inherently data-driven sparse models, which are able to cope with large-scale and complicated data. The main focus of research in the area of sparse representations is in developing reliable algorithms with provable performance and bounded complexity. There exist many applications for which it was proven beneficial to sparsely represent the data in some transform domain (i.e. "dictionary"). Moreover, the success of sparse models heavily depends on the choice of a “dictionary” to reflect the natural structures of a class of data. Dictionary learning for sparse representation deals with inferring such a dictionary from training data and is a key to the extension of sparse models for new exotic types of data.
SMALLbox provides an easy way to evaluate these methods against state-of-the art alternatives in a variety of standard signal processing problems. This is achieved trough a unifying interface that enables a seamless connection between the three types of modules: problems, dictionary learning algorithms and sparse solvers. In addition, it provides interoperability between existing state-of-the-art toolboxes.
As an open source MATLAB toolbox, the SMALLbox can be seen as not only as a evaluation and educational tool, but as a tool for reproducible research in the sparse representations research community.
Q2: How to obtain SMALLbox?¶
The SMALLbox project is maintained by people at the Centre for Digital Music at SEECS, Queen Mary University of London. To access the SMALLbox project page follow the link bellow:
https://code.soundsoftware.ac.uk/projects/smallbox/
If you want to try the latest stable public release please go to Downloads section. If you want to check the latest development and to contribute to the project then please first register to soundsoftware.ac.uk following the link in the upper right corner of the page.
The code repository hosted at soundsoftware.ac.uk is using Mercurial distributed version control, so you will need mercurial installed on your system. If you are new to mercurial the easiest way to start is to install EasyMercurial, which you can find at https://code.soundsoftware.ac.uk/projects/easyhg.
To check out SMALLbox repository please hg clone the following URL, or provide this URL to your preferred Mercurial client (e.g. EasyMercurial):
https://code.soundsoftware.ac.uk/hg/smallbox
Q3: How to install SMALLbox?¶
To install the toolbox run the script SMALLboxSetup.m from the MATLAB command prompt and follow the instructions. SmallboxSetup.m is in the root SMALLbox directory. The SMALLbox installation involves the automatic download of several existing toolboxes. These are described in Q5. Due to the automatic download of toolboxes you must have an active internet connection.
Please note that within the toolboxes are several MEX components that must be compiled. If you do not already have MEX setup, run "mex -setup" or type "help mex" in the MATLAB command prompt.
Once installed, there are two optional demo functions that can be run. Further information can be found in the README.txt in the main SMALLbox directory.
Q4: What are the Problem, solver, and DL structures in SMALLbox?¶
There are three main structures in SMALLbox that describe common parts of problem solving using sparse representation and dictionary learning - Problem, DL and solver structures.
The Problem structure defines all necessary aspects of a problem to be solved. To be compatible with the SPARCO, it needs to have five fields defined prior to any sparse representation of the data: A – a matrix or operator representing dictionary in which signal is sparse, b – a vector or matrix representing signal or signals to be represented, reconstruct – a function handle to reconstruct the signal from coefficients, signalSize – the dimension of the signal, sizeA – if matrix A is given as an operator the size of the dictionary needs to be defined in advance. Other fields that further describe the problem, which are useful for either reconstruction of the signal or representation of the results, might be generated by the SPARCO generateProblem function or the SMALLbox problem functions. The new problems implemented in the SMALLbox version 1.0 are: Image De-noising, Automatic Music Transcription and Image Representation using another image as a dictionary. In the case of a dictionary learning problem, fields A and reconstruct are not defined while generating the problem, but after the dictionary is learned and prior to the sparse representation. In this case, field b needs to be given in matrix form to represent the training data and another field p defining the number of dictionary elements to be learned needs to be specified.
The structure for dictionary learning - DL is a structure that defines dictionary learning algorithm to be used. It is initialised with a utility function SMALL_init_DL, which will define five mandatory fields: toolbox - a field used to discriminate the API, name - the name of dictionary learning function from the particular toolbox, param - a field containing parameters for the particular DL technique and in the form given by the toolbox API, D - a field where the learned dictionary will be stored, time - a field to store learning time. After toolbox, name and param fields are set, the function SMALL_learn is called with Problem and DL structures as inputs. According to the DL.toolbox field, the function calls the DL.name algorithm with its API and outputs learned dictionary D and time spent. The DL.param field contains parameters such as dictionary size, the number of iterations, the error goal or similar depending on the particular algorithm used.
Similar to dictionary learning every instance of the sparse representation needs to be initialised with the SMALL_init_solver function. It will define mandatory fields of the solver structure: toolbox - a field with toolbox name (e.g. sparselab), name - the name of solver from the particular toolbox (e.g. SolveOMP), param - the parameters in the form given by the toolbox API, solution - the output representation, reconstructed - the signal reconstructed from solution, time - the time spent for sparse representation. With the input parameters of the solver structure set, the SMALL_solve function is called with Problem and solver structure as inputs. The function calls solver.name algorithm with API specified by solver.toolbox and outputs solution, reconstructed and time fields.
Q5: What is included in SMALLbox?¶
To enable easy comparison with the existing state-of-the-art algorithms, during the installation procedure SMALLbox checks the Matlab path for existence of the following freely available toolboxes and will automatically download and install them, as required:
- SPARCO (v.1.2) - set of sparse representation problems
- SparseLab (v.2.1) - set of sparse solvers
- Sparsify (v.0.4) - set of greedy and hard thresholding algorithms
- SPGL1 (v.1.7) - large-scale sparse reconstruction solver
- GPSR (v.6.0) - Gradient projection for sparse reconstruction
- KSVD-box (v.13) and OMP-box (v.10) - dictionary learning
- KSVDS-box (v.11) and OMPS-box (v.1) - sparse dictionary learning
In addition there are also implementations of three solvers in the solver directory (MP, OMP and PCGP) and our implementation of recursive least square dictionary learning algorithm (RLS-DLA) in DL directory.
Q6: How do I contribute?¶
If you want to contribute to the project then please first register to soundsoftware.ac.uk following the link in the upper right corner of the page.
The code repository hosted at soundsoftware.ac.uk is using Mercurial distributed version control, so you will need mercurial installed on your system. If you are new to mercurial the easiest way to start is to install EasyMercurial, which you can find at https://code.soundsoftware.ac.uk/projects/easyhg.
To check out SMALLbox repository please hg clone the following URL, or provide this URL to your preferred Mercurial client (e.g. EasyMercurial):
https://code.soundsoftware.ac.uk/hg/smallbox
There are three ways how you can contribute your code to SMALLbox:
1. I have a toolbox that I maintain myself and it is available at my repository, but I want SMALLbox users to be able to use it within SMALLbox.
For example you have toolbox called my_dummy_toolbox and it is available at my_dummy_url. You should add the following lines to SMALLboxSetup.m script and of course change the parts in bold letters:
% check if toolbox is already installed (assuming that your toolbox have setup file, but any other script that is unique to your toolbox will do.
if ~exist(' my_dummy_toolbox_setup.m','file')
fprintf('\n **********************************************************');
fprintf('\n\n Initialising My_dummy_toolbox Setup');
try
% setting up the path where toolbox will be installed
my_dummy_toolbox_path =[SMALL_path,FS,'toolboxes',FS,' my_dummy_toolbox '];
%setting up the url of the file to be downloaded
my_dummy_toolbox_zip*='http:// *my_dummy_url/ my_dummy_toolbox.zip';
fprintf('\n\n Downloading toolbox, please be patient\n\n');
unzip( my_dummy_toolbox_zip, my_dummy_toolbox_path);
% generate the path for the toolbox and add it to the MATLAB search path
my_dummy_toolbox_p*=genpath( *my_dummy_toolbox_path);
addpath( my_dummy_toolbox_p);
% go to the installation directory and run the setup script if needed (e.g. there are mex files that needs to be compiled)
cd([ my_dummy_toolbox_path]);
try
my_dummy_toolbox_setup.m;
fprintf('\n My_dummy_toolbox Installation Successful!\n');
catch
warning('*My_dummy_toolbox* setup failed');
end
catch
fprintf('\n My_dummy_toolbox Installation Failed\n');
end
% return to the SMALL root directory
cd(SMALL_path);
else
fprintf('\n **********************************************************');
fprintf('\n\n My_dummy_toolbox is already installed');
end
Once you made this changes, you should also follow the steps in Q7, Q8 and Q9 to integrate your API with SMALLbox API.
2. I have a toolbox that I would like to incorporate into SMALLbox and to make it maintained and developed through the SMALLbox project.
Make a folder in toolboxes directory, add your files and commit to the repository. If your toolbox needs setup (e.g. mex files need to be compiled), add the lines in the SMALLboxSetup.m script:
my_dummy_toolbox_path =[SMALL_path,FS,'toolboxes',FS,' my_dummy_toolbox '];
cd([*my_dummy_toolbox_path*]);
try
my_dummy_toolbox_setup.m;
fprintf('\n My_dummy_toolbox Installation Successful!\n');
catch
warning('*My_dummy_toolbox* setup failed');
end
cd(SMALL_path);
If setup is not needed, no changes are required and the SMALLbox setup script will automatically generate the path for your toolbox.
Once you made this changes, you should also follow the steps in Q7, Q8 and Q9 to integrate your API with SMALLbox API.