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Machine Learning - Image Segmentation

Per pixel image segmentation using machine learning algorithms. Programmed using the following libraries: Scikit-Learn, Scikit-Image OpenCV, and Mahotas and ProgressBar. Compatible with Python 2.7+ and 3.X.

Feature vector

Spectral:

  • Red
  • Green
  • Blue

Texture:

  • Local binary pattern

Haralick (Co-occurance matrix) features (Also texture):

  • Angular second moment
  • Contrast
  • Correlation
  • Sum of Square: variance
  • Inverse difference moment
  • Sum average
  • Sum variance
  • Sum entropy
  • Entropy

Supported Learners

  • Support Vector Machine
  • Random Forest
  • Gradient Boosting Classifier

Example Usage

python train.py -i <path_to_image_folder> -l <path/to/label/folder> -c <SVM, RF, GBC> -o <path/to/model.p>

python inference.py -i <path_to_image_folder> -m <path/to/model.p> -o <path/to/output/folder>

python evaluation.py -i <path/to/test/images> -g <path/to/ground/truth/images> [-m]

Example Output

Example Output