FotoFind: An Advanced Image Retrieval System

Table of Contents

• Introduction
• Features
• System Architecture
• Models and Methodology
• Implementation Details
• Results
• Future Work
• References

Introduction

FotoFind is a robust and feature-rich image retrieval system designed to efficiently store, manage, and search large-scale image collections. By integrating object detection, image captioning, and Optical Character Recognition (OCR), FotoFind enhances metadata extraction and enables efficient text-based image search.

Features

• Automated Feature Extraction: Uses advanced deep learning models to detect objects, generate captions, and extract text from images.
• Searchable Image Metadata: Stores object labels, captions, and OCR text in a structured MySQL database.
• User-Centric Interface: Flask-based web application for uploading, browsing, and searching images.
• Efficient Search Mechanism: Uses TF-IDF vectorization and cosine similarity to rank search results based on relevance.

System Architecture

FotoFind follows a modular, scalable architecture comprising:

1. Flask Web Server: Manages user interactions and file uploads.
2. Feature Extraction Pipeline: Implements YOLO/Faster-RCNN (object detection), BLIP/ViT-GPT2 (captioning), and EasyOCR (text extraction).
3. Metadata Database (MySQL): Stores metadata for efficient retrieval.
4. Local Storage: Stores uploaded images.
5. Search Mechanism: Computes TF-IDF vectors and ranks results using cosine similarity.

Models and Methodology

Object Detection

• Faster R-CNN: Two-stage object detection framework for high accuracy.
• YOLO: Single-shot detector for real-time object recognition.

Image Captioning

• ViT-GPT2: Vision Transformer with GPT-2 for coherent captions.
• BLIP: Bootstrapped Language-Image Pre-training for enhanced captioning accuracy.

Optical Character Recognition (OCR)

• EasyOCR: Deep learning-based OCR for multi-language text recognition.

Search Algorithm

• TF-IDF (Term Frequency-Inverse Document Frequency): Converts text metadata into vectorized form.
• Cosine Similarity: Measures query relevance and ranks image search results.

Implementation Details

Development Environment and Project info

• Backend: Python (Flask, PyTorch, Transformers, Scikit-learn, MySQL Connector)
• Frontend: HTML, CSS (Bootstrap)
• Database: MySQL

Project Structure

FotoFind/
├── app.py                # Flask application
├── getfeatures.py        # Feature extraction pipeline
├── templates/
│   ├── gallery.html      # Web UI for browsing images
├── static/
│   ├── css/              # Styling files
├── images/               # Directory for uploaded images
├── requirements.txt      # Dependencies

Usage

Just simply clone the repo and run the 'app.py'
Now you can access the application in your favourite browser. -> localhost:5000

!! Ensure that you have installed all required packages. see: requirements.txt (NA yet) 

Workflow

1. Upload: Users upload images via the web interface.
2. Feature Extraction: Object detection, captioning, and OCR processing.
3. Metadata Storage: Extracted data is stored in MySQL.
4. Search Query: User searches are matched with stored metadata.
5. Image Retrieval: Results are ranked based on relevance and displayed.

Results

FotoFind successfully retrieves images based on:

• Object detection (e.g., "stop sign" retrieves relevant traffic images).
• Captioning (e.g., "bus to Navy Pier" finds transportation-related images).
• OCR (e.g., "train to Nagoya" matches embedded text in images).

Future Work

• Advanced Indexing: Integrating FAISS/Elasticsearch for scalable search.
• Domain Adaptation: Fine-tuning models for specific industries (e.g., healthcare, retail).
• Cloud Deployment: Using Docker/Kubernetes for cloud scalability.
• User Feedback Mechanism: Allowing manual corrections to improve model performance.

Usage References

• Object Detection: Faster R-CNN (Torchvision), YOLO (Ultralytics)
• Image Captioning: ViT-GPT2, BLIP (Hugging Face Transformers)
• OCR: EasyOCR (Deep Learning OCR framework)
• Search Algorithm: Scikit-learn (TF-IDF, Cosine Similarity)

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FotoFind provides a powerful and scalable solution for intelligent image retrieval, leveraging cutting-edge computer vision techniques to enhance searchability and metadata enrichment.

-JD (RulerOfEternalNight)