Enterprise AI Analysis
Uncovering historical ceramic color patterns via visual analytics for design and heritage
This paper introduces an interactive visual analytics system that merges design principles with computational innovation, employing computer vision and network science to transform unstructured image data into structured color networks. It allows researchers and designers to explore color associations, extract historical color schemes, and integrates digital humanities analytics with modern design practices.
Executive Impact: Quantified
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1441
Ceramic Works Analyzed
0.5%
Color Proportion Threshold (%)
ΔΕ 30
Adjustable CIEDE2000 ΔΕ Range
7
Representative Core Color Palettes
Deep Analysis & Enterprise Applications
Select a topic to dive deeper, then explore the specific findings from the research, rebuilt as interactive, enterprise-focused modules.
Our system integrates computer vision and network science to transform raw image data into structured color networks. This enables systematic analysis of ceramic color patterns, overcoming limitations of traditional qualitative approaches. Key stages include data preprocessing, network construction, and interactive visualization.
The core of our approach is the Ceramic-Color Bipartite Network, which models affiliations between ceramic works and their constituent colors. This is then projected into a one-mode color co-occurrence network, where edge weights quantify similarity. Centrality analysis identifies key 'base' and 'bridge' colors, revealing historical color scheme hierarchies.
The system empowers designers to extract historically grounded palettes for contemporary applications. A case study demonstrates how Qing Dynasty enamel color patterns were translated into modern aromatherapy product designs, showcasing the tool's ability to bridge data insights with design innovation.
Future work will expand the ceramic color database, incorporate multimodal association networks (color-pattern-artifact), integrate intelligent palette recommendations, and develop plug-ins for mainstream design software. This aims to further bridge digital humanities with modern design practice.
Ceramic Color Design System Workflow
Data Preprocessing
→
Build System
→
Application Practice
1441
Ceramic Images Processed (Ming & Qing Dynasties)
| Primary Category | HSV Quantitative Conditions | Characteristic Description |
|---|---|---|
| Rich & vibrant scheme |
|
Highly saturated and with diverse hues. Commonly seen in Wucai, Doucai, Guangcai and Jincai porcelains. |
| Blue & white scheme |
|
Blue-white contrast and clear patterns. Commonly seen in blue and white porcelain, underglaze blue and blue and white with pastel colors. |
| Celadon scheme | H: 140-200° S: 10-60% V: 50-90% | Blue-green hues are common in celadon from famous kilns such as Longquan and Ru. |
| Monochrome glaze scheme | Standard Deviation of H: < 22 S:Any V:Any | Pure colors and rich textures. Commonly seen in various colored glazes, such as red, white and green. |
| Pale & elegant scheme | H: Any S: 0-30% V: 70-100% Proportion: >60% | Soft colors with high brightness and low saturation. Commonly seen in pastel and light-toned underglaze red. |
| Comprehensive/special scheme | H/S/V: Irregular & complex variation or none | Varied gloss and unique textures. Highly reflective surfaces or glaze discoloration are common in Jianzhan, Tianmu, kiln-fired glazes and crystallized glazes. |
Case Study: Qing Dynasty Enamel Color Patterns to Modern Aromatherapy
This analysis focused on enamel porcelain from the Qing Dynasty, particularly the Yongzheng, Qianlong, and Kangxi periods, which represent the heyday of colorful and gorgeous styles. Through network analysis, light colors (e.g., light yellow, white) were identified as 'base colors' due to high degree centrality, while bright colors (e.g., specific greens and reds) were 'core colors' due to high eigenvector centrality. The system revealed 7 dominant chromatic clusters, guiding the design of a modern aromatherapy series. This directly translates historical color patterns into contemporary product design, providing evidence-based inspiration and preserving cultural heritage in innovative forms.
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Projected Annual Savings & Efficiency Gains
Annual Cost Savings
$0
Annual Hours Reclaimed
0
Your AI Implementation Roadmap
A clear path to unlocking the full potential of AI-driven cultural heritage analysis and design.
Phase 1: Discovery & Strategy
Initial consultation to understand your specific heritage data, design goals, and integration requirements. We define KPIs and tailor the AI analysis framework.
Phase 2: Data Integration & Custom Model Training
Secure ingestion of your ceramic image data and metadata. Our engineers train and fine-tune computer vision and network models for optimal pattern recognition and color extraction.
Phase 3: Interactive Visual Analytics Deployment
Deployment of your custom visual analytics platform. Teams gain access to interactive dashboards for exploring color networks, patterns, and historical contexts.
Phase 4: Design Integration & Iteration
Support for designers to extract, apply, and iterate on AI-generated color schemes. Feedback loops ensure continuous improvement and alignment with creative outputs.
Phase 5: Continuous Optimization & Expansion
Ongoing monitoring, performance optimization, and updates. Explore expansion to multimodal data (patterns, forms) and integration with existing design software APIs.
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