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=72hr

• Mid-level (daily): eps_spatial=5km, eps_temporal=12hr

• Low-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) # WRONG

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

What it looks like:

distance = sqrt((lat2-lat1)**2 + (lon2-lon1)**2) # WRONG

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()

Process photo corpus

results = pipeline.process_photo_corpus(photos)

Access events

for event in results['events']: print(f"{event.label}: {len(event.photos)} photos, significance={event.significance_score:.2f}")

Access life events

for life_event in results['life_events']: print(f"{life_event.type} detected on {life_event.timestamp}")

Select for collage with diversity

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 placement

• photo-content-recognition-curation-expert: Get face clusters before event detection

• color-theory-palette-harmony-expert: Use for visual diversity within events

• clip-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