Intelligent Duplicate Detection
Build smart algorithms to find similar and duplicate photos in large collections
Module Overview
Discover how to implement sophisticated duplicate detection algorithms with Claude Code's help. You'll explore perceptual hashing, similarity scoring, and performance optimization techniques to efficiently identify duplicate and near-duplicate images in photo collections of any size.
Perceptual Hashing
Generate fingerprints for visual similarity
Smart Matching
Find exact and near-duplicate images
AI Enhancement
Use AI to improve detection accuracy
What You'll Learn
Implement perceptual hashing algorithms (pHash, dHash, aHash)
Build efficient similarity comparison systems
Create UI for reviewing and managing duplicates
Optimize performance for large photo collections
Handle edge cases like crops, rotations, and filters
Integrate AI-powered similarity detection
Topics Covered
Hashing Algorithms
• Average Hash (aHash)
• Difference Hash (dHash)
• Perceptual Hash (pHash)
• Wavelet Hash implementation
Similarity Detection
• Hamming distance calculation
• Threshold tuning strategies
• Multi-algorithm fusion
• False positive handling
Performance & Scale
• Parallel processing with workers
• Caching strategies
• Database indexing
• Progressive scanning
User Experience
• Duplicate groups visualization
• Side-by-side comparison
• Bulk action handling
• Smart deletion suggestions
Technical Implementation
Key algorithms and techniques you'll implement:
• Generate 64-bit perceptual hashes for images
• Build a similarity matrix using Hamming distance
• Implement clustering for duplicate groups
• Create worker threads for parallel processing
• Design efficient data structures for comparison
• Add machine learning for improved accuracy