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