ENTERPRISE AI ANALYSIS
Toward enhanced unsupervised clustering of 20th century Korean paintings via multimodal features
This study introduces a novel machine learning framework for analyzing and clustering modern and contemporary Korean paintings. By integrating a pretrained vision-language architecture with multi-layered analysis, the framework efficiently extracts detailed formal characteristics, including color features from multiple spaces and quantified texture. The extracted feature vectors are then clustered and evaluated, achieving 82.4% overall accuracy, outperforming single-feature baselines. The methodology offers a quantitative approach to art-historical interpretation, capable of identifying and distinguishing visual characteristics of Korean paintings.
Executive Impact & Strategic Value
Our analysis of 'Toward enhanced unsupervised clustering of 20th century Korean paintings via multimodal features' reveals significant implications for industries dealing with large visual archives, such as art institutions, digital libraries, and creative tech. The paper’s methodology, achieving 82.4% clustering accuracy, offers a robust, objective approach to categorizing complex visual data, surpassing traditional methods. This translates directly into enhanced operational efficiency and enriched interpretability for your enterprise, providing a competitive edge in data organization and accessibility.
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82.4%
Overall Clustering Accuracy
The proposed multimodal feature fusion achieved 82.4% overall accuracy, outperforming single-feature baselines and traditional hierarchical clustering methods. This metric quantifies the effectiveness of the model in grouping visually similar artworks and aligning with ground-truth artist labels.
Proposed Framework Stages
Image Feature Extraction (RGB, HSV, GLCM, CLIP)
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Feature Concatenation & Normalization
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Dimensionality Reduction (t-SNE)
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K-means Clustering
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Majority-Label Assignment & Evaluation
| Method | Clustering Accuracy (%) | Silhouette Score | Advantages |
|---|---|---|---|
| CLIP + RGB + HSV + GLCM (Ours) | 82.4 | 0.4180 |
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| CLIP Only | 82.2 | 0.4047 |
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| Hierarchical - Ward | 54.5 | 0.0642 |
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| Histogram | 51.0 | 0.3791 |
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| LBP Only | 68.8 | 0.3618 |
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Artist-Specific Performance & Insights: Yoo Young-kuk
For Yoo Young-kuk, known for bold primary colors and geometric color planes, the model achieved 94.9% accuracy. This high performance is attributed to the effectiveness of color-centric features (RGB, HSV) in capturing his distinctive stylistic determinants. The multimodal fusion further enhanced robustness, outperforming single-feature baselines significantly. This highlights the framework's ability to discern artists with strong, consistent color palettes.
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