Event Detection & Temporal Intelligence Expert
Expert in detecting meaningful events from photo collections using spatio-temporal clustering, significance scoring, and intelligent photo selection.
DECISION POINTS
ST-DBSCAN vs DeepDBSCAN Algorithm Selection
Photo corpus analysis needed?
├─ Have GPS + timestamps?
│ ├─ Same location, different activities detected? ──── DeepDBSCAN
│ │ └─ Cost tolerance: High accuracy > speed ─────── Add CLIP embeddings
│ └─ Simple time/location grouping sufficient? ────── ST-DBSCAN
│
├─ Timestamps only (no GPS)?
│ ├─ Visual similarity important? ───────────────── Temporal + CLIP clustering
│ └─ Pure time-based events? ────────────────────── Temporal binning
│
└─ Need hierarchical events (vacation > daily > moments)?
└─ Multi-level ST-DBSCAN cascade ─────────────────── Expanding ε thresholds
Parameter Selection Matrix
| Event Type | ε_spatial | ε_temporal | min_pts | Use Case |
|---|
| Indoor party | 50m | 4hr | 5 | Home gatherings |
| Wedding | 200m | 8hr | 8 | Venue + reception |
| City tour | 5km | 12hr | 3 | Tourism, exploration |
| Multi-day trip | 50km | 72hr | 10 | Vacation clustering |
| Conference | 1km | 24hr | 6 | Business events |
Event Significance Threshold Decision
Computed significance score?
├─ Score ≥ 0.8? ──────── Life event candidate (birth, wedding, graduation)
├─ Score ≥ 0.6? ──────── Major memorable event
├─ Score ≥ 0.4? ──────── Significant social gathering
├─ Score ≥ 0.2? ──────── Minor event worth keeping
└─ Score < 0.2? ──────── Daily routine, consider filtering
FAILURE MODES
Over-Clustering Syndrome
Symptoms: Every few photos become separate "events"; 50+ micro-events from one vacation
Detection Rule: If >30% of events contain <5 photos AND duration <2 hours
Diagnosis: ε parameters too restrictive, treating natural breaks as separate events
Fix: Increase ε_temporal (2hr → 6hr) or use hierarchical clustering with larger top-level ε
Under-Clustering Collapse
Symptoms: Wedding ceremony + reception next day grouped as single event
Detection Rule: If single event spans >24hr AND contains >200 photos AND location changes >5km
Diagnosis: ε parameters too permissive, merging distinct occasions
Fix: Reduce ε_temporal (12hr → 6hr) OR add location change detection as break condition
GPS Noise Contamination
Symptoms: Indoor event scattered across 10km radius; bathroom photos 500m from venue
Detection Rule: If event location std_dev >2x expected venue size AND contains <20% outdoor photos
Diagnosis: GPS drift/reflection causing false spatial spread
Fix: Apply GPS smoothing filter OR increase min_pts to require more spatial consensus
Content-Blind Grouping
Symptoms: Empty venue setup photos grouped with ceremony; parking lot + wedding altar same event
Detection Rule: If visual diversity within event >0.8 cosine distance AND high location precision
Diagnosis: ST-DBSCAN without visual validation grouping unrelated content
Fix: Switch to DeepDBSCAN with ε_visual=0.4 OR post-filter by CLIP similarity
Temporal Boundary Bleeding
Symptoms: Friday work photos grouped with Saturday family party; overnight events split at midnight
Detection Rule: If event crosses date boundary AND activity types differ >0.6 semantic distance
Diagnosis: Fixed temporal windows ignoring natural event boundaries
Fix: Use adaptive temporal windows OR detect activity changes as natural breaks
WORKED EXAMPLES
Example 1: Wedding Event Detection
Input: 847 photos from weekend wedding, GPS enabled
photos = load_wedding_corpus("sarah_tom_wedding/")
# GPS range: Venue (40.7589, -73.9851) to Hotel (40.7505, -73.9934)
# Time range: Fri 2pm - Sun 11am (45 hours)
Decision Process:
- Algorithm Choice: Multiple venues + high importance → DeepDBSCAN
- Parameter Selection: Wedding type → ε_spatial=200m, ε_temporal=8hr, min_pts=8
- Visual Threshold: Diverse wedding activities → ε_visual=0.5
Expert vs Novice Decisions:
- Novice miss: Would use single ε_temporal=24hr, grouping rehearsal dinner with ceremony
- Expert catch: Detects natural breaks (rehearsal→ceremony→reception) using CLIP similarity drops
Results:
Event 1: Rehearsal Dinner (Fri 6pm-10pm, 34 photos)
Event 2: Getting Ready (Sat 10am-2pm, 89 photos)
Event 3: Ceremony (Sat 2pm-4pm, 156 photos)
Event 4: Reception (Sat 5pm-11pm, 203 photos)
Noise: Travel/hotel photos (47 photos)
Example 2: Parameter Trade-off Analysis
Scenario: 10,000 photo family corpus, computational budget constraints
Trade-off Decision:
- ST-DBSCAN: 2.1sec processing, 85% event accuracy, 12 false merges
- DeepDBSCAN: 47sec processing, 94% event accuracy, 3 false merges
Decision Factors:
- Real-time requirement? → ST-DBSCAN
- Batch processing + accuracy critical? → DeepDBSCAN
- Hybrid: ST-DBSCAN with DeepDBSCAN refinement on high-significance events
Expert Insight: Cost/accuracy inflection point at ~5,000 photos where CLIP embedding overhead becomes worthwhile.
QUALITY GATES
Event detection task complete when ALL conditions met:
NOT-FOR BOUNDARIES
Do NOT use this skill for:
- Individual photo quality assessment → Use
photo-composition-critic instead
- Color scheme analysis for events → Use
color-theory-palette-harmony-expert instead
- Face recognition/clustering → Use
photo-content-recognition-curation-expert first, then apply event detection
- EXIF timestamp extraction → Basic file parsing, not event intelligence
- Single photo context → This skill requires photo collections (>10 photos minimum)
- Real-time photo stream processing → Designed for batch corpus analysis
- Geographic route planning → Use mapping services; this extracts events from completed trips
- Social graph analysis → This handles temporal/spatial clustering, not relationship mapping
Delegation patterns:
- For photo aesthetic quality within events →
photo-composition-critic
- For face-based event grouping → First
photo-content-recognition-curation-expert, then this skill
- For collage layout of detected events →
collage-layout-expert
- For color harmony across event photos →
color-theory-palette-harmony-expert