Lyssandra

A collection of Python tools for feature extraction and image classification with Sparse Coding algorithms.

features

Sparse Coding algorithms

Sparse coding is a class of unsupervised methods for learning sets of over-complete dictionaries to represent data efficiently. Each signal can be expressed as a sparse linear combination of the atoms in the dictionary:

alt text

To encode a set of datapoints X over a dictionary D:

from lyssa.sparse_coding import sparse_encoder

# ...
se = sparse_encoder(algorithm='bomp', params={'n_nonzero_coefs': 5}, n_jobs=8)
Z = se.encode(X, D)

Some of the supported solvers include:

Orthogonal Matching Pursuit (OMP)
Batch OMP [1]
Group OMP [2]
Non-Negative OMP [3]
Iterative Hard Thresholding

Dictionary Learning algorithms

Learning the dictionary from the data involves solving the following objective

alt text

A dictionary learned from image patched of natural images looks like

alt text

Supported solvers:

K-SVD and its approximate variant [4]
Online Dictionary Learning [5]
Projected Gradient Descent

Feature Extraction

Spatial Pyramid Matching using Sparse Coding [6]
Convolutional Feature Encoders [8]
Dense SIFT extraction

Classification

Label Consistent K-SVD [9]
Sparse Representation based Classification [10]

Installation

Install the dependencies using:

pip install -r requirements.txt

For LASSO problems, the Python version of SPAMS http://spams-devel.gforge.inria.fr/index.html must be installed in your system.

First edit config.yml to specify the

workspace path, the location of the directory in which outputs of feature extraction tasks will be saved
path to OpenBLAS in your system (optional)

and then do:

pip install .

For best performance, configuring numpy with OpenBLAS is recommended (see Dockerfile).

Usage

Have a look at the lyssa/examples folder for some usage examples, and typical workflows.

References

[1] R. Rubinstein, M. Zibulevsky and M. Elad: Efficient Implementation of the K-SVD Algorithm and the Batch-OMP Method.

[2] A. Lozano, G. Swirszcz, N. Abe: Group Orthogonal Matching Pursuit for Variable Selection and Prediction.

[3] A. Bruckstein, M. Elad and, M. Zibulevsky: On the uniqueness of nonnegative sparse solutions to underdetermined systems of equations. IEEE Trans. Inform. Theory, 54(11):4813–4820, 2008.

[4] M. Aharon, M. Elad, and A. Bruckstein: K-SVD: An Algorithm for Designing Overcomplete Dictionaries for Sparse Representation.

[5] J. Mairal, F. Bach, J. Ponce, and G. Sapiro: Online Dictionary Learning for Sparse Coding.

[6] J. Yang, K. Yu, Y. Gong, and T. Huang: Linear spatial pyramid matching using sparse coding for image classification, CVPR (2009).

[7] L. Bo, X. Ren, and D. Fox: Hierarchical Matching Pursuit for Image Classification: Architecture and Fast Algorithms. In NIPS, 2011.

[8] A. Coates and, A. Y. Ng: The Importance of Encoding Versus Training with Sparse Coding and Vector Quantization.

[9] Z. Jiang, Z. Lin, and L. S. Davis: Learning a discriminative dictionary for sparse coding via label consistent k-svd. CVPR, 2011.

[10] J. Wright, A. Yang, A. Ganesh, S. Sastry, and Y. Ma: Robust face recognition via sparse representation, PAMI (2009).