Channelwise-Saab-Transform

Feature extraction (Module 1) packages for PixelHop/PixelHop++.

Introduction

This is an implementation by Yijing Yang for the feature extraction part in the paper by Chen et.al. PixelHop++: A Small Successive-Subspace-Learning-Based (SSL-based) Model for Image Classification.

It is modified based on Chengyao Wang's implementation (ObjectOriented / Numpy version), with lower memory cost.

Note that this is not the official implementation.

Installation

This code has been tested with Python 3.7 and Python 3.8. Other dependent packages include: numpy, scikit-image, numba and scikit-learn.

Contents

• saab.py: Saab transform.

• cwSaab.py: Channel-wise Saab transform. Use energy threshold TH1 and TH2 to choose intermediate nodes and leaf nodes, respectively. Set 'cw' to 'False' in order to turn off the channel-wise structure.

• pixelhop.py: Built upon cwSaab.py with additional functions of saving models, loading models, and concatenation operation across Hops.

• Example of usage can be found at the bottom of each file.

  Note: All the images or data that are fed into these functions should be in the channel last format.