Project Description:

The AI Photo Tagging Assistant is a smart web-based application that automatically scans uploaded photos and applies relevant tags based on the objects, people, scenes, and emotions detected in the image using machine learning models. The main goal is to help users easily organize, search, and manage their photo collections without manual tagging.

This system mimics the functionality used in Google Photos or Facebook for facial recognition and scene detection.

Key Features:

Automatic Tagging Using AI/ML:

• When a photo is uploaded, the AI model analyzes it to:

  • Detect objects (e.g., car, tree, food, pet).

  • Identify people (if enabled).

  • Recognize settings (e.g., beach, party, city, indoor).

  • Analyze emotions (smiling, sad, etc.).

• Generates and attaches descriptive tags to each photo automatically.

Search by Tags:

• Users can search for photos by typing keywords (e.g., "beach", "birthday", "dog").

• Filter photos by people, date, location, emotion, or category.

User Account System:

• Secure registration and login system.

• Users can maintain private and shared albums.

• Cloud-based storage of photo collections.

Album & Category Organization:

• Photos can be grouped automatically by tags or date.

• Users can manually create albums or rely on smart auto-generated categories.

AI/ML Technology Stack:

• Image Recognition APIs/Models:

  • Google Vision API / Microsoft Azure Vision

  • Or open-source models like:

    • YOLO (You Only Look Once)

    • OpenCV + TensorFlow/Keras (for custom classification)

    • FaceNet for facial recognition (optional)

• Pre-trained ML Models:

  • Can be trained using datasets like ImageNet, COCO, or Flickr image sets.

Web Application Technology:

Frontend:

• HTML, CSS, Bootstrap for UI

• JavaScript for dynamic preview and interaction

• AJAX for async uploads and tagging feedback

Backend (choose one):

• PHP / Node.js / Java

• Handles file uploads, image analysis requests, and user management

• Stores data in MySQL / MongoDB

Database Structure:

• Users Table (user_id, name, email, password)

• Photos Table (photo_id, user_id, filepath, upload_time)

• Tags Table (tag_id, tag_name)

• Photo_Tag_Mapping (photo_id, tag_id)

Security Features:

• Encrypted user authentication

• Private photo storage

• Optional watermark or image protection