Iconographic Classification and Content-Based Recommendation for Digitized Artworks
Fuente:
arXiv
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| Autori principali: | , |
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| Natura: | Preprint |
| Pubblicazione: |
2026
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| _version_ | 1866918350628585472 |
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| author | Kutt, Krzysztof Baczyński, Maciej |
| author_facet | Kutt, Krzysztof Baczyński, Maciej |
| contents | We present a proof-of-concept system that automates iconographic classification and content-based recommendation of digitized artworks using the Iconclass vocabulary and selected artificial intelligence methods. The prototype implements a four-stage workflow for classification and recommendation, which integrates YOLOv8 object detection with algorithmic mappings to Iconclass codes, rule-based inference for abstract meanings, and three complementary recommenders (hierarchical proximity, IDF-weighted overlap, and Jaccard similarity). Although more engineering is still needed, the evaluation demonstrates the potential of this solution: Iconclass-aware computer vision and recommendation methods can accelerate cataloging and enhance navigation in large heritage repositories. The key insight is to let computer vision propose visible elements and to use symbolic structures (Iconclass hierarchy) to reach meaning. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2602_19698 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Iconographic Classification and Content-Based Recommendation for Digitized Artworks Kutt, Krzysztof Baczyński, Maciej Digital Libraries Artificial Intelligence Computer Vision and Pattern Recognition Information Retrieval We present a proof-of-concept system that automates iconographic classification and content-based recommendation of digitized artworks using the Iconclass vocabulary and selected artificial intelligence methods. The prototype implements a four-stage workflow for classification and recommendation, which integrates YOLOv8 object detection with algorithmic mappings to Iconclass codes, rule-based inference for abstract meanings, and three complementary recommenders (hierarchical proximity, IDF-weighted overlap, and Jaccard similarity). Although more engineering is still needed, the evaluation demonstrates the potential of this solution: Iconclass-aware computer vision and recommendation methods can accelerate cataloging and enhance navigation in large heritage repositories. The key insight is to let computer vision propose visible elements and to use symbolic structures (Iconclass hierarchy) to reach meaning. |
| title | Iconographic Classification and Content-Based Recommendation for Digitized Artworks |
| topic | Digital Libraries Artificial Intelligence Computer Vision and Pattern Recognition Information Retrieval |
| url | https://arxiv.org/abs/2602.19698 |