Image Segmentation using Gaussian Mixture Model and Expectation Maximization

Project Overview

This project demonstrates the segmentation of grayscale and color images using a Gaussian Mixture Model (GMM) and the Expectation-Maximization (EM) algorithm. The project applies these advanced machine learning techniques to segment images effectively, providing a comprehensive approach to image segmentation.

Key Features

• Grayscale and Color Image Segmentation: Uses GMM and EM algorithms to segment grayscale and color images.
• Machine Learning Application: Demonstrates the application of machine learning techniques in image processing.
• Log-Likelihood Plotting: Visualizes the log-likelihood and negative log-likelihood curves to show the convergence of the EM algorithm.

Technical Description

This section provides a detailed explanation of the core functions and algorithms used in the project.

Getting Started

To get started with the Image Segmentation project, follow these steps to clone the repository and install the required dependencies.

Prerequisites

Ensure you have Python installed on your machine. You can download it from python.org.

Installation

1. Clone the Repository

   git clone https://github.com/JayKareliya-code/GMM-EM-model.git

2. Create a Virtual Environment

   It's a good practice to use a virtual environment to manage dependencies. You can create a virtual environment using the following command:

   python -m venv venv

3. Activate the Virtual Environment

   • On Windows:

     venv\Scripts\activate

   • On macOS/Linux:

     source venv/bin/activate

4. Install Dependencies

   Install the required dependencies using the requirements.txt file:

   pip install -r requirements.txt

5. Run the Script

   Once the dependencies are installed, you can run the script to perform image segmentation:

   python ImageSegmentation.py

Requirements

Ensure the following libraries are included in your requirements.txt file: