Building a Content-Based Image Retrieval (CBIR) System

Introduction

Content-based image retrieval (CBIR) focuses on finding similar images by analyzing their visual content instead of relying only on text annotations. In this project, I built a full-stack CBIR system to enable efficient and interactive image search at scale.

Project Highlights

1. Hybrid Feature Extraction: Combined classic CV algorithms (Color Histograms, HOG) with deep learning models (VGG19, ResNet152) to capture both low-level and semantic image features.
2. Efficient Retrieval Engine: Leveraged FAISS for large-scale vector indexing, achieving millisecond-level similarity search.
3. Dynamic Updates: Designed an incremental indexing mechanism for efficient database updates without full re-indexing.
4. Interactive Interface: Deployed a FastAPI-powered backend and a real-time web interface for seamless user interaction.

Impact

This project demonstrates the potential of combining traditional computer vision with modern deep learning techniques, offering a scalable and user-friendly image retrieval system.