Marouan19/multimedia_mining_labs

Series of practical works and implementations on multimedia mining, carried out as part of the Multimedia Mining course at FST Tangier. # 🖼️ Multimedia Mining Labs Series of practical works and implementations on multimedia mining, carried out as part of the Multimedia Mining course at FST Tangier. ## 📚 Table of Contents 1. Description 2. Repository Structure 3. Technologies Used 4. Installation 5. Labs 6. Contributions 7. Author ## 📖 Description This repository contains a collection of practical laboratories exploring different aspects of multimedia mining technology, including: - Image processing and analysis - Feature extraction techniques - Pattern recognition in multimedia - Performance analysis and visualization - Multimedia data mining algorithms ## 📂 Repository Structure ``` multimedia_mining_labs/ ├── Lab1/ │ └── [Basic image processing implementations] ├── Seance2/ │ └── [Feature extraction techniques] ├── Seance3/ │ └── [Pattern recognition exercises] ├── Seance4/ │ └── [Advanced multimedia analysis] ├── Devoir_individuel/ │ └── [Individual assignments] └── README.md ``` ## 💻 Technologies Used - **Python 3.8+** - **Main libraries**: - `OpenCV` - For image processing and computer vision - `NumPy` - For numerical computations - `scikit-image` - For image processing algorithms - `matplotlib` - For data visualization - `scikit-learn` - For machine learning algorithms - `PIL` - For image manipulation ## 🔧 Installation 1. Clone the repository: ```bash git clone github.com ``` 2. Create and activate a virtual environment: ```bash cd multimedia_mining_labs python -m venv .venv source .venv/bin/activate # On Unix/macOS # or .\.venv\Scripts\activate # On Windows ``` 3. Install dependencies: ```bash pip install -r requirements.txt ``` ## 📘 Labs ### 🔍 Lab 1: Introduction to Image Processing - Basic image manipulation - Color space transformations - Image filtering techniques ### 🎨 Session 2: Feature Extraction - Color features extraction - Texture analysis - Shape descriptors ### 🔎 Session 3: Pattern Recognition - Implementation of patt …