Agent skill · research science · erichowens
photo-content-recognition-curation-expert
Expert in photo content recognition, intelligent curation, and quality filtering. Specializes in face/animal/place recognition, perceptual hashing for de-duplication, screenshot/meme detection,
Why this skill is useful
Provides domain-specific knowledge on perceptual hashing and intelligent curation techniques that the AI wouldn't reliably generate on its own.
What it needs
About 5k tokens when loaded. Last updated 2026-07-14. 180 stars on the source repository.
What this skill does
Photo Content Recognition & Curation Expert Expert in photo content analysis and intelligent curation. Combines classical computer vision with modern deep learning for comprehensive photo analysis. When to Use This Skill ✅ Use for: Face recognition and clustering (identifying important people) Animal/pet detection and clustering Near-duplicate detection using perceptual hashing (DINOHash, pHash, dHash) Burst photo selection (finding best frame from 10-50 shots) Screenshot vs photo classification Meme/download filtering NSFW content detection Quick indexing for large photo libraries (10K+) Aesthetic quality scoring (NIMA) ❌ NOT for: GPS-based location clustering → event-detection-temporal-intelligence-expert Color palette extraction → color-theory-palette-harmony-expert Semantic image-text matching → clip-aware-embeddings Video analysis or frame extraction Quick Decision Tree --- Core Concepts 1. Perceptual Hashing for Near-Duplicate Detection Problem: Camera bursts, re-saved images, and minor edits create near-duplicates. Solution: Perceptual hashes generate similar values for visually similar images. Method Comparison: Method Speed Robustness Best For -------- ------- ------------ ---------- dHash Fastest Low Exact duplicates pHash Fast Medium Brightness/contrast changes DINOHash Slower High Heavy crops, compression Hybrid Medium Very High Production systems Hybrid Pipeline (2025 Best Practice): 1. Stage 1: Fast pHash filtering (eliminates obvious non-duplicates) 2. Stage 2: DINOHash refinement (accurate detection) 3. Stage 3: Optional Siamese ViT verification Hamming Distance Thresholds: Conservative: ≤5 bits different = duplicates Aggressive: ≤10 bits different = duplicates → Deep dive: references/perceptual-hashing.md --- 2. Face Recognition & Clustering Goal: Group photos by person without user labeling. Apple Photos Strategy (2021-2025): 1. Extract face + upper body embeddings (FaceNet, 512-dim) 2. Two-pass agglomerative clustering 3. …
How to use it
Reference it in AdaL, Claude Code, Cursor or any coding agent — nothing to install:
@skills erichowens/photo-content-recognition-curation-expert