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Source of "Artificial image generation and latent feature extraction applied to image classification" master's thesis

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Description

  1. Image feature extraction, using quantization techniques and image descriptors.

Quantization Techniques:

Gleam
Intensity
Luminance
MSB

Descriptors:

BIC
GCH
CCV
Haralick
ACC
  1. Dimensionality reduction of feature vectors.

Dimensionality Reduction Techniques:

PCA
Entropia
  1. Image classification.

Classifier:

Naive Bayes
  1. SMOTE: Synthetic Minority Over-sampling Technique.

An implementation based on "SMOTE: Synthetic Minority Over-sampling Technique" paper's pseudo-code

  1. Artificial Generation of images to rebalance classes.

Generate artificial images using preprocessing techniques to solve the balancing problem

Use

Before running the code, create a symbolic link to the images directory:

ln -s <IMAGES DIRECTORY> BaseImagens

Makefile will compile the code for you:

make

To run all descriptors and generate the feature vectors:

./runAllDescriptors.sh

After the previous command, to reduce the vectors dimension and apply the classification run:

./dimensionReduction <VECTORS DIRECTORY> <TECHNIQUE> <PARAMETERS LIST>

Options for techniques and parameters:

[0] None:
    Just the classifier is going to be used, with the extracted vectors without dimensionality reduction.
[1] PCA:
    - <nAttributes>: number of attributes to keep on PCA
[2] Entropy:
    - <tWindow>: window size
[3] All:
    - <nAttributes>: number of attributes to keep on PCA
    - <tWindow>: window size

The classification analysis (using Naive Bayes classifier and Repeated subsampling as a cross validation method) is going to be printed on the terminal. So, to write on a file the results of the analysis:

./dimensionReduction <VECTORS DIRECTORY> <TECHNIQUE> <PARAMETERS LIST>  >  analysis/<TECHNIQUE>_<PARAMETERS>.txt

Examples:

./dimensionReduction caracteristicas_corel/256/ 1 35 > analysis/Corel/PCA_50.txt
./dimensionReduction caracteristicas_corel/256/ 2 4 > analysis/Corel/ENTROPY_4.txt

Compare SMOTE and Artificial Generation of Images performance:

./rebalanceTest Desbalanced/original/ features/
python plot.py # Check this file before running, it may modify a previous image

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Source of "Artificial image generation and latent feature extraction applied to image classification" master's thesis

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