Performing image segmentation using a gaussian mixture model.
University: Athens University of Economics and Business
Department: Informatics
Subject: Machine Learning
Writer: Andreas Gouletas (@BrainBroader)
It is an implementation of a gaussian mixture model that performs image segmentation in 3 dimensional input data. The model uses the Expectation Maximization algorithm for training.
Any image(RGB).
The technologies used that are worth mentioning, are:
- Python
- Numpy
- Matplotlib
Before you execute the given program, you need to:
1.get an image of your choice.
2.check if you have installed the libraries mention in Section "Technologies".
If you haven't previously installed the libraries mentioned above, you can use the provided requirements.txt file, by running the following command:
cd path-to-project pip install -r requirements.txt
To execute the program the following command is used:
python main.py arg1 arg2
where
- arg1 is the path to the image.
- arg2 is the number of image segments you want (It must be an integer).
Running example,
python main.py img.jpg 2