M.I.M.I.R - Multi-agent Intelligent Memory & Insight Repository

# Art Progress Photo Organization Strategy **Organizing 100TB of Artwork Progress Photos into Time-Series Art Books** **Version:** 1.0 **Date:** November 25, 2025 --- ## Executive Summary Strategy for organizing 100 terabytes of unorganized artwork progress photos into chronologically-ordered art books (one per painting). Photos have inconsistent angles, lighting, and naming conventions. **Estimated Outcome:** 90-95% accuracy with human validation, 1.5-2 months processing time --- ## Technical Stack & References ### Computer Vision & Deep Learning 1. **CLIP (OpenAI)** - Image embeddings, 512-dim Radford et al., "Learning Transferable Visual Models From Natural Language Supervision" (2021) https://arxiv.org/abs/2103.00020 2. **DINOv2 (Meta AI)** - Self-supervised features, 768/1024-dim Oquab et al., "DINOv2: Learning Robust Visual Features without Supervision" (2023) https://arxiv.org/abs/2304.07193 3. **ResNet-152 (Microsoft)** - 2048-dim embeddings He et al., "Deep Residual Learning for Image Recognition" (2015) https://arxiv.org/abs/1512.03385 4. **EfficientNet (Google)** - Efficient scaling Tan & Le, "EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks" (2019) https://arxiv.org/abs/1905.11946 ### Feature Detection 5. **SIFT** - Scale-invariant keypoints Lowe, "Distinctive Image Features from Scale-Invariant Keypoints" (2004) https://www.cs.ubc.ca/~lowe/papers/ijcv04.pdf 6. **ORB** - Fast SIFT alternative Rublee et al., "ORB: An Efficient Alternative to SIFT or SURF" (2011) https://ieeexplore.ieee.org/document/6126544 7. **Canny Edge Detection** - Optimal edge detector Canny, "A Computational Approach to Edge Detection" (1986) https://ieeexplore.ieee.org/document/4767851 ### Image Hashing 8. **Perceptual Hashing (pHash)** - DCT-based robust hashing Zauner, "Implementation and Benchmarking of Perceptual Image Hash Functions" (2010) http://phash.org/docs/pubs/thesis_zauner.pdf 9. **Average/Difference/Wavelet Hashing** imagehash library: https://github.com/JohannesBuchner/imagehash ### Clustering Algorithms 10. **HDBSCAN** - Hierarchical density clustering Campello et al., "Density-Based Clustering Based on Hierarchical Density Estimates" (2013) https://link.springer.com/chapter/10.1007/978-3-642-37456-2_14 11. **DBSCAN** - Density-based clustering Ester et al., "A Density-based Algorithm for Discovering Clusters" (1996) https://www.aaai.org/Papers/KDD/1996/KDD96-037.pdf 12. **K-means** - Color palette extraction MacQueen, "Some Methods for Classification and Analysis of Multivariate Observations" (1967) https://projecteuclid.org/ebooks/berkeley-symposium-on-mathematical-statistics-and-probability/Proceedings-of-the-Fifth-Berkeley-Symposium-on-Mathematical-Statistics-and/chapter/Some-methods-for-classification-and-analysis-of-multivariate-observations/bsmsp/1200512992 ### Image Quality & Similarity 13. **SSIM** - Structural similarity index Wang et al., "Image Quality Assessment: From Error Visibility to Structural Similarity" (2004) https://ieeexplore.ieee.org/document/1284395 ### Color Spaces 14. **LAB Color Space** - Perceptually uniform CIE 1976 L*a*b* color space standard https://en.wikipedia.org/wiki/CIELAB_color_space 15. **HSV Color Space** - Hue-Saturation-Value Smith, "Color Gamut Transform Pairs" (1978) https://dl.acm.org/doi/10.1145/800248.807361 ### Machine Learning 16. **Active Learning** - Human-in-the-loop improvement Settles, "Active Learning Literature Survey" (2009) https://minds.wisconsin.edu/handle/1793/60660 17. **U-Net** - Semantic segmentation (paint layer detection) Ronneberger et al., "U-Net: Convolutional Networks for Biomedical Image Segmentation" (2015) https://arxiv.org/abs/1505.04597 18. **Bradley-Terry Model** - Pairwise comparisons Bradley & Terry, "Rank Analysis of Incomplete Block Designs" (1952) https://www.jstor.org/stable/2334029 ### Standards & Formats 19. **EXIF Standard** - Image metadata JEITA CP-3451 (Japan Electronics and Information Technology Industries Association) https://www.cipa.jp/std/documents/e/DC-008-2012_E.pdf ### Libraries & Tools 20. **PyTorch** - Deep learning framework https://pytorch.org/ 21. **OpenCV** - Computer vision library https://opencv.org/ 22. **scikit-learn** - Machine learning https://scikit-learn.org/ 23. **scikit-image** - Image processing https://scikit-image.org/ 24. **PIL/Pillow** - Python imaging https://pillow.readthedocs.io/ 25. **Transformers (HuggingFace)** - Pre-trained models https://huggingface.co/docs/transformers/ 26. **FAISS** - Fast similarity search Johnson et al., "Billion-scale similarity search with GPUs" (2017) https://arxiv.org/abs/1702.08734 27. **PostgreSQL + pgvector** - Vector database https://github.com/pgvector/pgvector 28. **Neo4j** - Graph database (relationships) https://neo4j.com/ 29. **Apache Airflow** - Workflow orchestration https://airflow.apache.org/ 30. **Prefect** - Modern workflow engine https://www.prefect.io/ 31. **DVC** - Data version control https://dvc.org/ --- ## Phase 1: Image Analysis & Feature Extraction ### 1.1 Extract Visual Features - Generate 512-1024 dimensional embeddings using CLIP/DINOv2 - Extract EXIF metadata (timestamps, camera, GPS) - Compute perceptual hashes (pHash, aHash, dHash, wHash) - Batch process on GPU clusters (1000-10000 images in parallel) ### 1.2 Extract Painting Signatures - Detect canvas boundaries (Canny edge detection + contour finding) - Extract color palettes (K-means in LAB space, 5-10 dominant colors) - Compute color histograms (HSV space for lighting invariance) - Extract SIFT/ORB features for angle-invariant matching - Calculate aspect ratios and canvas sizes --- ## Phase 2: Clustering & Grouping ### 2.1 Multi-Stage Clustering **Stage 1: Embedding-Based Clustering** ```python HDBSCAN( min_cluster_size=5, min_samples=3, metric='cosine', cluster_selection_epsilon=0.3 ) ``` **Similarity Thresholds:** - High confidence: 0.85+ (auto-assign) - Medium: 0.70-0.85 (batch review) - Low: 0.60-0.70 (manual review) - Uncertain: <0.60 (detailed investigation) **Stage 2: Composition Refinement** - Verify aspect ratio consistency (variance < 0.1) - Compare color palettes (LAB distance) - Match SIFT features across angles (>10 matches = same painting) - Use SSIM for structural similarity **Stage 3: Cross-Validation** - Validate clusters with multiple features - Split clusters with low internal similarity (<0.5 SSIM) - Flag overlapping assignments for manual review ### 2.2 Handling Ambiguity **Confidence Scoring:** ```python confidence = ( 0.5 * cluster_membership_probability + 0.3 * multi_feature_validation_score + 0.2 * sift_match_score ) ``` **Active Learning Loop:** 1. Select most uncertain samples (lowest confidence) 2. Human labeling/validation 3. Incorporate feedback 4. Retrain clustering model 5. Iterate until accuracy plateaus --- ## Phase 3: Temporal Ordering ### 3.1 Multi-Source Timeline Construction **Primary: EXIF Timestamps** - Priority: DateTimeOriginal > DateTime > DateTimeDigitized > File creation - Handle missing timestamps with interpolation **Secondary: Visual Progression** ```python completion_score = ( 0.5 * canvas_coverage + 0.3 * detail_density + 0.2 * color_saturation ) ``` - Canvas coverage: % painted vs unpainted - Detail density: Edge detection intensity - Color saturation: Builds up over layers **Tertiary: Pairwise Comparison** - Compare uncertain pairs: "Which shows more progress?" - Use Bradley-Terry model for ranking - Bootstrap from confident sequences ### 3.2 Gap Detection - Identify time gaps >7 days - Detect style/subject jumps (potential misclassification) - Flag for review ### 3.3 Temporal Consistency Validation ```python # Check for "time travel" violations for i in range(len(sequence) - 1): if completion[i] > completion[i+1]: flag_inconsistency(i, i+1) ``` --- ## Phase 4: Quality Control ### 4.1 Validation Pipeline - Verify temporal consistency (no regression in completion) - Check visual progression (smooth evolution) - Validate lighting/angle variations reasonable - Flag outliers (z-score > 3 in any metric) ### 4.2 Duplicate Handling - Group near-duplicates (pHash distance < 5) - Keep highest resolution version - Log all duplicates for reference ### 4.3 Review Queue Priority 1. Low confidence assignments (<0.6) 2. Overlapping cluster candidates 3. Temporal inconsistencies 4. High aspect ratio variance within cluster 5. Low SIFT feature matches --- ## Phase 5: Art Book Generation ### 5.1 Book Structure (Per Painting) ``` ├── Cover: Final completed artwork ├── Metadata Page: │ ├── Date range (first photo to last photo) │ ├── Estimated time investment │ ├── Technique notes (if detectable) │ ├── Canvas dimensions (if available) ├── Progress Grid: 10-20 key milestone photos ├── Detailed Sequence: All photos chronologically ├── Multi-Angle Comparison: Same stage, different angles └── Time-lapse: Animation if >20 photos ``` ### 5.2 Layout Options for Multi-Angle Coverage **Option A: Separate Sections per Angle** - Group by viewing angle - Show progression for each angle separately - Good for systematic documentation **Option B: Chronological Interleaving** - Mix all angles chronologically - Label each photo with angle indicator - Shows true timeline **Option C: Comparison Grids** - Create grids showing same progress state from multiple angles - Highlight specific development stages - Best for artistic analysis ### 5.3 Generation Pipeline ```bash # LaTeX/InDesign automation for painting in paintings: select_key_photos(painting, n=15) generate_layout(painting, template) create_pdf(painting) create_epub(painting) # optional create_print_files(painting) # optional ``` --- ## Implementation Timeline ### Week 1-2: Infrastructure Setup - Set up processing cluster (GPU nodes) - Deploy PostgreSQL + pgvector database - Install all dependencies - Create data ingestion pipeline ### Week 3-4: Feature Extraction - Batch process all 100TB - Generate embeddings (CLIP/DINO) - Extract EXIF metadata - Compute hashes - Store in database ### Week 5-6: Initial Clustering - Run HDBSCAN on embeddings - Generate confidence scores - Create review queue - Begin human validation ### Week 7-8: Refinement & Validation - Refine clusters with composition analysis - Incorporate human feedback - Active learning iterations - Split/merge clusters as needed ### Week 9-10: Temporal Ordering - Order photos within each cluster - Validate temporal consistency - Gap analysis - Final manual reviews ### Week 11: Quality Control - Final validation pass - Resolve remaining ambiguities - Generate quality reports - Document edge cases ### Week 12: Book Generation - Select key photos per painting - Generate layouts - Create PDFs and other formats - Final delivery --- ## Storage & Infrastructure Requirements ### Processing Infrastructure - **GPU Cluster**: 4-8 A100/H100 GPUs for embedding generation - **CPU Cluster**: 64-128 cores for feature extraction - **RAM**: 512GB-1TB for large-scale processing - **Storage**: 150TB (100TB source + 50TB processed/intermediate) ### Database Requirements - **PostgreSQL + pgvector**: 50GB for metadata + embeddings - **Neo4j** (optional): 10GB for relationship graph - **Object Storage (S3/MinIO)**: 100TB for images ### Network - 10Gbps+ internal network for data transfer - Parallel processing to minimize bottlenecks --- ## Cost Estimation (AWS/Cloud) ### Compute Costs - GPU instances (p4d.24xlarge): ~$32/hour × 500 hours = $16,000 - CPU instances (c6i.32xlarge): ~$5/hour × 1000 hours = $5,000 - **Total Compute: ~$21,000** ### Storage Costs - S3 Standard: 100TB × $0.023/GB/month × 2 months = $4,700 - Database: $500/month × 2 months = $1,000 - **Total Storage: ~$5,700** ### Data Transfer - Ingress: Free - Processing: Internal - Egress (final delivery): ~$1,000 - **Total Transfer: ~$1,000** **Total Estimated Cost: $27,700 (cloud) or ~$15,000 (self-hosted)** --- ## Expected Outcomes & Deliverables ### Per Painting 1. Complete chronologically-ordered photo sequence 2. High-quality art book (PDF, 50-200 pages) 3. Print-ready files (CMYK, 300 DPI) 4. Metadata JSON file with provenance 5. Time-lapse video (if >20 photos, MP4 1080p) 6. Web gallery HTML (optional) ### Overall Statistics - Estimated paintings: 100-10,000 (depends on artist productivity) - Estimated accuracy: 90-95% with human review - Processing time: 1.5-2 months - Storage after deduplication: ~60-80TB (40% reduction) ### Documentation - Complete processing logs - Confidence scores for all assignments - Manual review decisions - Edge cases and ambiguities resolved - Quality control reports --- ## Risk Mitigation ### Technical Risks 1. **Clustering errors**: Mitigated by multi-stage validation + human review 2. **Timestamp corruption**: Fall back to visual progression analysis 3. **Storage failures**: RAID + backups + cloud sync 4. **Processing bottlenecks**: Parallel processing + batch optimization ### Data Quality Risks 1. **Missing metadata**: Extract from visual features 2. **Corrupted images**: Flag and exclude, log for recovery 3. **Extreme lighting variations**: Use LAB color space + normalization 4. **Occlusions/partial views**: SIFT features handle partial matches ### Process Risks 1. **Human reviewer fatigue**: Batch reviews, active learning to minimize 2. **Scope creep**: Fixed threshold for manual review (estimate 5-15% of data) 3. **Timeline slippage**: Buffer weeks 11-12 for overruns --- ## Success Metrics ### Clustering Quality - **Precision**: % of photos correctly assigned to paintings (target: >95%) - **Recall**: % of actual paintings discovered (target: >90%) - **Purity**: Average within-cluster consistency (target: >0.85) ### Temporal Ordering Quality - **Sequence accuracy**: % of correctly ordered pairs (target: >90%) - **Timestamp reliability**: % of sequences with valid EXIF data - **Visual consistency**: No temporal regressions in completion ### Efficiency Metrics - **Processing throughput**: Images processed per hour - **Review efficiency**: % requiring manual intervention (target: <15%) - **Cost per painting**: Total cost / number of paintings ### Deliverable Quality - **Book completeness**: All discovered photos included - **Layout quality**: Professional presentation standards - **File quality**: Print-ready specifications met --- ## Conclusion This strategy provides a comprehensive, technically-grounded approach to organizing 100TB of unorganized art progress photos. By combining state-of-the-art computer vision, robust clustering algorithms, and human-in-the-loop validation, we can achieve 90-95% accuracy in both grouping photos by painting and establishing chronological order. The multi-stage approach handles the inherent ambiguities in angle, lighting, and metadata variations, while active learning continuously improves the system. The result will be a complete collection of art books documenting the creative progression of each painting. **Key Success Factors:** 1. Robust feature extraction (CLIP/DINO + SIFT) 2. Multi-stage clustering with validation 3. Confidence-based review prioritization 4. Human expertise for ambiguous cases 5. Comprehensive quality control **Timeline:** 1.5-2 months **Cost:** $15,000-$28,000 **Accuracy:** 90-95% **Deliverables:** One art book per painting with complete temporal documentation --- ## References & Further Reading ### Primary Research Papers 1. Radford et al., CLIP (2021): https://arxiv.org/abs/2103.00020 2. Oquab et al., DINOv2 (2023): https://arxiv.org/abs/2304.07193 3. He et al., ResNet (2015): https://arxiv.org/abs/1512.03385 4. Lowe, SIFT (2004): https://www.cs.ubc.ca/~lowe/papers/ijcv04.pdf 5. Campello et al., HDBSCAN (2013): https://link.springer.com/chapter/10.1007/978-3-642-37456-2_14 6. Wang et al., SSIM (2004): https://ieeexplore.ieee.org/document/1284395 7. Ronneberger et al., U-Net (2015): https://arxiv.org/abs/1505.04597 8. Johnson et al., FAISS (2017): https://arxiv.org/abs/1702.08734 9. Settles, Active Learning (2009): https://minds.wisconsin.edu/handle/1793/60660 ### Books & Surveys - Szeliski, "Computer Vision: Algorithms and Applications" (2nd ed., 2022) - Bishop, "Pattern Recognition and Machine Learning" (2006) - Goodfellow et al., "Deep Learning" (2016) ### Tools & Libraries Documentation - PyTorch: https://pytorch.org/docs/ - OpenCV: https://docs.opencv.org/ - scikit-learn: https://scikit-learn.org/stable/documentation.html - HuggingFace Transformers: https://huggingface.co/docs/transformers/ - FAISS: https://github.com/facebookresearch/faiss/wiki --- **END OF DOCUMENT**