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

1. Sparse Coding algorithms
   • Orthogonal Matching Pursuit (OMP)
   • Batch OMP [1]
   • Group OMP [2]
   • Non-Negative OMP [3]
   • Iterative Hard Thresholding
2. Dictionary Learning algorithms
   • K-SVD and its approximate variant [4]
   • Online Dictionary Learning [5]
   • Projected Gradient Descent
3. Feature Extraction
   • Spatial Pyramid Matching using Sparse Coding [6]
   • Convolutional Feature Encoders [8]
   • Dense SIFT extraction
4. Classification
   • Label Consistent K-SVD [9]
   • Sparse Representation based Classification [10]

This package has the following dependencies:

• numpy
• scipy
• scikit-learn
• matplotlib (optional)
• Pillow
• for LASSO problems, the Python version of SPAMS http://spams-devel.gforge.inria.fr/index.html must be installed in your system
• OpenBLAS (optional)

First edit config.yml to specify the

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

and then do:

pip setup.py install

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

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

[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).