Building a Content-Based Image Retrieval (CBIR) System
A full-stack system enabling efficient and interactive image search at scale using hybrid feature extraction and FAISS.
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
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.
Efficient Retrieval Engine
Leveraged FAISS for large-scale vector indexing, achieving millisecond-level similarity search across the entire image database.
Dynamic Updates
Designed an incremental indexing mechanism for efficient database updates without full re-indexing.
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.