Image Retrieval System

This project implements an image retrieval system using various techniques, from basic similarity measures to advanced deep learning approaches.

Table of Contents

• Introduction
• Features
• Project Structure
• Installation
• Usage
• Contributing

Introduction

This image retrieval system offers implementations ranging from traditional similarity measures to state-of-the-art deep learning techniques. It aims to efficiently retrieve relevant images from a dataset based on a query image.

Features

• Multiple similarity measures: L1, L2, Cosine Similarity, Correlation Coefficient
• Deep learning-based feature extraction using CLIP
• Vector database integration for efficient retrieval
• Jupyter notebooks for method comparisons
• Configurable data loading and result visualization

Project Structure

IMAGE-RETRIEVAL/
├── github/
│   └── workflows/
│       └── build.yml
├── assets/
├── data/
├── notebooks/
│   ├── embedding_method.ipynb
│   ├── traditional_method.ipynb
│   └── vector_database_method.ipynb
├── source/
│   ├── __pycache__/
│   ├── config.py
│   ├── data_loader.py
│   ├── plot_results.py
│   ├── retrieval.py
│   └── similarity.py
├── .gitignore
├── LICENSE
├── README.md
├── main.py
├── requirements.txt
└── sonar-project.properties

Installation

1. Clone the repository:

   git clone https://github.com/tiendat25052004/image-retrieval
   cd image-retrieval
   

2. Install necessary libraries:

   pip install -r requirements.txt
   

Usage

Run the main script:

python main.py

Examples:

• L1 Distance:
• L2 Distance:
• Cosine Similarity:
• Correlation Coefficient:

3. For advanced methods, refer to the Jupyter notebooks in the notebooks/ directory. Examples:

• Vector Database with Cosine Similarity:

Contributing

Contributions are welcome. Please fork the repository and submit a pull request with your changes.