Framework for Spectral Clustering on the Sparse Coefficients of Learned Dictionaries

Related tags

Deep Learning python dictionary-learning spectral-clustering hyperspectral-images
Overview

Dictionary Learning for Clustering on Hyperspectral Images

Overview

Framework for Spectral Clustering on the Sparse Coefficients of Learned Dictionaries. This framework was created as a part of the project I presented for completion of my Computer Science Honours' degree at the University of the Witwatersrand.
A paper was produced for this research, it was published by Springer's Journal of Signal, Image and Video Processing. The paper can be read for free here: https://rdcu.be/b5Vsq. Please look below for citation details.

Authored by: Joshua Bruton
Supervised by: Dr. Hairong Wang

Contents

This repository contains implementations or usage of the following techniques:

  1. Online Dictionary Learning
  2. Orthogonal Matching Pursuit with dynamic stopping criteria
  3. Spectral Clustering (sk-learn)

The repository also contains the SalinasA hyperspectral image. This and other hyperspectral data sets are available on the Grupo de Inteligencia Computacional website here.

Usage

I have created a requirements file. I recommend using pipenv with Python 3.6 to open a shell and then using

pipenv install -r requirements.txt

and requirements should be met. Then just run:

python demonstration.py

and the demonstration should run. It will train a dictionary and then use it for spectral clustering as discussed in the paper.

Previous work

One working discriminative dictionary has been provided in the repository, all of the others are available as assets on Comet.ml. They were all trained using the implementation of ODL provided in this repository. Bare in mind that dictionary learning is extremely sensitive to the initialisation of the dictionary; results for different dictionaries will vary drastically.

scikit-learn was used extensively throughout this project for more stable implementations. Thanks also go to Dave Biagioni, mitscha, and the authors of this article.

Future work

This repository is licensed under the GNU General Public License and therefore is completely free to use for any project you see fit. If you do use or learn from our work, we would appreciate if you cited the following details:

@article{10.1007/s11760-020-01750-z, 
  author = {Bruton, Joshua and Wang, Hairong}, 
  title = {{Dictionary learning for clustering on hyperspectral images}}, 
  issn = {1863-1703}, 
  doi = {10.1007/s11760-020-01750-z},
  pages = {1--7}, 
  journal = {Signal, Image and Video Processing}, 
  year = {2020}
}

Or:
Bruton, J., Wang, H. Dictionary learning for clustering on hyperspectral images. SIViP (2020). https://doi.org/10.1007/s11760-020-01750-z

The paper can be read for free.

Suggestions

If there are any pressing problems with the code please open an issue and I will attend to it as timeously as is possible.

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Comments
  • Bump joblib from 0.15.1 to 1.2.0

    Bump joblib from 0.15.1 to 1.2.0

    Bumps joblib from 0.15.1 to 1.2.0.

    Changelog

    Sourced from joblib's changelog.

    Release 1.2.0

    • Fix a security issue where eval(pre_dispatch) could potentially run arbitrary code. Now only basic numerics are supported. joblib/joblib#1327

    • Make sure that joblib works even when multiprocessing is not available, for instance with Pyodide joblib/joblib#1256

    • Avoid unnecessary warnings when workers and main process delete the temporary memmap folder contents concurrently. joblib/joblib#1263

    • Fix memory alignment bug for pickles containing numpy arrays. This is especially important when loading the pickle with mmap_mode != None as the resulting numpy.memmap object would not be able to correct the misalignment without performing a memory copy. This bug would cause invalid computation and segmentation faults with native code that would directly access the underlying data buffer of a numpy array, for instance C/C++/Cython code compiled with older GCC versions or some old OpenBLAS written in platform specific assembly. joblib/joblib#1254

    • Vendor cloudpickle 2.2.0 which adds support for PyPy 3.8+.

    • Vendor loky 3.3.0 which fixes several bugs including:

      • robustly forcibly terminating worker processes in case of a crash (joblib/joblib#1269);

      • avoiding leaking worker processes in case of nested loky parallel calls;

      • reliability spawn the correct number of reusable workers.

    Release 1.1.0

    • Fix byte order inconsistency issue during deserialization using joblib.load in cross-endian environment: the numpy arrays are now always loaded to use the system byte order, independently of the byte order of the system that serialized the pickle. joblib/joblib#1181

    • Fix joblib.Memory bug with the ignore parameter when the cached function is a decorated function.

    ... (truncated)

    Commits
    • 5991350 Release 1.2.0
    • 3fa2188 MAINT cleanup numpy warnings related to np.matrix in tests (#1340)
    • cea26ff CI test the future loky-3.3.0 branch (#1338)
    • 8aca6f4 MAINT: remove pytest.warns(None) warnings in pytest 7 (#1264)
    • 067ed4f XFAIL test_child_raises_parent_exits_cleanly with multiprocessing (#1339)
    • ac4ebd5 MAINT add back pytest warnings plugin (#1337)
    • a23427d Test child raises parent exits cleanly more reliable on macos (#1335)
    • ac09691 [MAINT] various test updates (#1334)
    • 4a314b1 Vendor loky 3.2.0 (#1333)
    • bdf47e9 Make test_parallel_with_interactively_defined_functions_default_backend timeo...
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  • Bump numpy from 1.18.4 to 1.22.0

    Bump numpy from 1.18.4 to 1.22.0

    Bumps numpy from 1.18.4 to 1.22.0.

    Release notes

    Sourced from numpy's releases.

    v1.22.0

    NumPy 1.22.0 Release Notes

    NumPy 1.22.0 is a big release featuring the work of 153 contributors spread over 609 pull requests. There have been many improvements, highlights are:

    • Annotations of the main namespace are essentially complete. Upstream is a moving target, so there will likely be further improvements, but the major work is done. This is probably the most user visible enhancement in this release.
    • A preliminary version of the proposed Array-API is provided. This is a step in creating a standard collection of functions that can be used across application such as CuPy and JAX.
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    Expired deprecations

    Deprecated numeric style dtype strings have been removed

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    (gh-19539)

    Expired deprecations for loads, ndfromtxt, and mafromtxt in npyio

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    (gh-19615)

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Owner
Joshua Bruton
MSc. Computer Science student at the University of the Witwatersrand. Co-founder of Educess.
Joshua Bruton
GitHub https://rdcu.be/b5Vsq
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