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event-detection-temporal-intelligence-expert Expert in temporal event detection, spatio-temporal clustering (ST-DBSCAN), and photo context understanding. Use for detecting photo events, clustering by time/location, shareability prediction, place recognition, event significance scoring, and life event detection. Activate on 'event detection', 'temporal clustering', 'ST-DBSCAN', 'spatio-temporal', 'shareability prediction', 'place recognition', 'life events', 'photo events', 'temporal diversity'. NOT for individual photo aesthetic quality (use photo-composition-critic), color palette analysis (use color-theory-palette-harmony-expert), face recognition implementation (use photo-content-recognition-curation-expert), or basic EXIF timestamp extraction.
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name event-detection-temporal-intelligence-expert description Expert in temporal event detection, spatio-temporal clustering (ST-DBSCAN), and photo context understanding. Use for detecting photo events, clustering by time/location, shareability prediction, place recognition, event significance scoring, and life event detection. Activate on 'event detection', 'temporal clustering', 'ST-DBSCAN', 'spatio-temporal', 'shareability prediction', 'place recognition', 'life events', 'photo events', 'temporal diversity'. NOT for individual photo aesthetic quality (use photo-composition-critic), color palette analysis (use color-theory-palette-harmony-expert), face recognition implementation (use photo-content-recognition-curation-expert), or basic EXIF timestamp extraction. allowed-tools Read,Write,Edit,Bash,Grep,Glob,mcp__firecrawl__firecrawl_search,WebFetch category AI & Machine Learning tags ["temporal","clustering","events","spatio-temporal","photo-context"] pairs-with [{"skill":"photo-content-recognition-curation-expert","reason":"Content + temporal understanding"},{"skill":"wedding-immortalist","reason":"Event detection for wedding albums"}]
Event Detection & Temporal Intelligence Expert
Expert in detecting meaningful events from photo collections using spatio-temporal clustering, significance scoring, and intelligent photo selection for collages.
When to Use This Skill
✅ Use for:
Detecting events from photo timestamps + GPS coordinates
Clustering photos by time, location, and visual content (ST-DBSCAN, DeepDBSCAN)
Scoring event significance (birthday > commute)
Predicting photo shareability for social media
Recognizing life events (graduations, weddings, births, moves)
Temporal diversity optimization (avoid all photos from one day)
Event-aware collage photo selection
❌ NOT for:
Individual photo aesthetic quality → photo-composition-critic
Color palette analysis → color-theory-palette-harmony-expert
Face clustering/recognition → photo-content-recognition-curation-expert
CLIP embedding generation → clip-aware-embeddings
Single-photo timestamp extraction (basic EXIF parsing)
Quick Decision Tree
Need to group photos into meaningful events?
├─ Have GPS + timestamps? ──────────────────── ST-DBSCAN
│ ├─ Also need visual similarity? ────────── DeepDBSCAN (add CLIP)
│ └─ Need hierarchical events? ───────────── Multi-level cascading
│
├─ No GPS, only timestamps? ────────────────── Temporal binning
│ └─ With visual content? ─────────────────── CLIP + temporal
│
└─ Photos have faces + want groups? ─────────── Face clustering first
└─ Then event detection per person
Core Concepts
1. ST-DBSCAN: Spatio-Temporal Clustering
The Problem : Standard clustering fails for photos—same location on different days shouldn't be grouped.
Key Insight : 100 meters apart in same hour = same event. 100 meters apart 3 days later = different events.
ST-DBSCAN Parameters :
ε_spatial: 50m (indoor) → 500m (outdoor festival) → 5km (city tour)
ε_temporal: 1hr (short event) → 8hr (day trip) → 24hr (multi-day)
min_pts: 3 (small gathering) → 10 (large event)
Algorithm : Both spatial AND temporal constraints must be satisfied:
Neighbor(p) = {q | distance(p,q) ≤ ε_spatial AND |time(p)-time(q)| ≤ ε_temporal}
→ Deep dive : references/st-dbscan-implementation.md
2. DeepDBSCAN: Adding Visual Content Problem : Photos at same time/place can be different subjects (ceremony vs empty chairs).
Solution : Add CLIP embeddings as third dimension:
Neighbor(p) = {q | spatial_ok AND temporal_ok AND cosine_sim(clip_p, clip_q) > threshold}
eps_visual : 0.3 (similar subjects) → 0.5 (diverse event content)
3. Hierarchical Event Detection Use case : "Paris Vacation" contains "Day 1: Louvre", "Day 2: Eiffel Tower"
Approach : Cascade ST-DBSCAN with expanding thresholds:
High-level (vacations): eps_spatial=50km, eps_temporal=72hrMid-level (daily): eps_spatial=5km, eps_temporal=12hrLow-level (moments): eps_spatial=500m, eps_temporal=1hr
Event Significance Scoring Goal : Birthday party > Daily commute photos
Multi-Factor Model (weights sum to 1.0):
Factor Weight Description location_rarity 0.20 Exotic location > home people_presence 0.15 Photos with people score higher photo_density 0.15 More photos/hour = more memorable content_rarity 0.15 Landmarks, celebrations detected via CLIP visual_diversity 0.10 Varied shots = special event duration 0.10 Longer events score higher engagement 0.10 Shared/edited/favorited photos temporal_rarity 0.05 Annual patterns (birthdays, holidays)
→ Deep dive : references/event-scoring-shareability.md
Shareability Prediction Goal : Predict which photos will be shared on social media.
High-Signal Features (2025 research):
Smiling faces (+0.3 base score)Group photos (3+ people, +0.2)Famous landmarks (+0.25)Food scenes (+0.15)Moderate visual complexity (0.4-0.6 optimal)Recency (decays over 30 days) Shareability Threshold : >0.6 = "Highly Shareable"
→ Deep dive : references/event-scoring-shareability.md
Life Event Detection Automatically detect major life events using multi-modal signals:
Event Type Primary Signals Threshold Graduation Cap/gown, diploma, auditorium 0.6 Wedding Formal attire, bouquet, cake, rings 0.7 Birth New infant face cluster, hospital setting 0.8 Residential Move 50km+ location shift, >30 days 0.8 Travel Milestone First visit to new country 1.0
→ Deep dive : references/place-recognition-life-events.md
Temporal Diversity for Selection Problem : Without constraints, collage might be all vacation photos.
Method Comparison Method Best For Use When Temporal Binning Even time coverage Need chronological spread Temporal MMR Quality + diversity balance Balanced selection Event-Based Event representation Each event matters
Temporal MMR Formula MMR(photo) = λ × quality + (1-λ) × min_temporal_distance_to_selected
λ=0.5: Balanced
λ=0.7: Prefer quality
λ=0.3: Prefer diversity
→ Deep dive : references/temporal-diversity-pipeline.md
Common Anti-Patterns
Anti-Pattern: Time-Only Clustering What it looks like : Using K-means or basic DBSCAN on timestamps only
clusters = KMeans(n_clusters=10 ).fit(timestamps)
Why it's wrong : Multi-day trips at same location get split; same-day different-location events get merged.
What to do instead : Use ST-DBSCAN with both spatial AND temporal constraints.
Anti-Pattern: Fixed Epsilon Values What it looks like : Using same eps_spatial=100m for all events
Why it's wrong : Indoor events need 50m, city tours need 5km.
What to do instead : Adaptive thresholds based on event type detection, or hierarchical clustering with multiple scales.
Anti-Pattern: Ignoring Visual Content What it looks like : ST-DBSCAN alone for event detection
Why it's wrong : Wedding ceremony and empty chairs setup—same time/place, completely different importance.
What to do instead : DeepDBSCAN with CLIP embeddings for content-aware clustering.
Anti-Pattern: Euclidean Distance for GPS distance = sqrt((lat2-lat1)**2 + (lon2-lon1)**2 )
Why it's wrong : Degrees ≠ meters. 1° latitude = 111km, but 1° longitude varies by latitude.
What to do instead : Haversine formula for great-circle distance:
from geopy.distance import geodesic
distance_meters = geodesic((lat1, lon1), (lat2, lon2)).meters
Anti-Pattern: No Noise Handling What it looks like : Forcing every photo into a cluster
Why it's wrong : Solo commute photos pollute event clusters.
What to do instead : DBSCAN naturally identifies noise (label=-1). Keep noise separate—don't force into nearest cluster.
Anti-Pattern: Shareability Without Event Context What it looks like : Predicting shareability from photo features alone
Why it's wrong : A mediocre photo from your wedding is more shareable than a great photo from Tuesday's lunch.
What to do instead : Include event significance as feature:
features['event_significance' ] = photo.event.significance_score
Quick Start: Event Detection Pipeline from event_detection import EventDetectionPipeline
pipeline = EventDetectionPipeline()
results = pipeline.process_photo_corpus(photos)
for event in results['events' ]:
print (f"{event.label} : {len (event.photos)} photos, significance={event.significance_score:.2 f} " )
for life_event in results['life_events' ]:
print (f"{life_event.type } detected on {life_event.timestamp} " )
collage_photos = pipeline.select_for_collage(results, target_count=100 )
Performance Targets Operation Target ST-DBSCAN (10K photos) < 2 seconds Event significance scoring < 100ms/event Shareability prediction < 50ms/photo Place recognition (cached) < 10ms/photo Full pipeline (10K photos) < 5 seconds
Python Dependencies numpy scipy scikit-learn hdbscan geopy transformers xgboost pandas opencv-python
Integration Points
collage-layout-expert : Pass event clusters for diversity-aware placementphoto-content-recognition-curation-expert : Get face clusters before event detectioncolor-theory-palette-harmony-expert : Use for visual diversity within eventsclip-aware-embeddings : Generate embeddings for DeepDBSCAN
References
ST-DBSCAN : Birant & Kut (2007), "ST-DBSCAN: An algorithm for clustering spatial-temporal data"DeepDBSCAN : ISPRS 2021, "Deep Density-Based Clustering for Geo-Tagged Photos"Shareability : arXiv 2025, "Predicting Social Media Engagement from Emotional and Temporal Features"GeoNames/OpenStreetMap : Reverse geocoding for place recognition
Version : 2.0.0
Last Updated : November 2025
