A collection of Python tools for feature extraction and image classification with 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:
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
Learning the dictionary from the data involves solving the following objective
A dictionary learned from image patched of natural images looks like
Supported solvers:
- K-SVD and its approximate variant [4]
- Online Dictionary Learning [5]
- Projected Gradient Descent
- Spatial Pyramid Matching using Sparse Coding [6]
- Convolutional Feature Encoders [8]
- Dense SIFT extraction
- Label Consistent K-SVD [9]
- Sparse Representation based Classification [10]
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).
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).