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[2602.19698] Iconographic Classification and Content-Based Recommendation for Digitized Artworks
Summary
This article presents a proof-of-concept system for automating iconographic classification and content-based recommendations for digitized artworks using AI methods and the Iconclass vocabulary.
Why It Matters
The integration of AI in art classification can significantly enhance the efficiency of cataloging and navigating large digital art repositories. This research highlights the potential for improved accessibility and understanding of cultural heritage through technology.
Key Takeaways
- The system automates classification using Iconclass vocabulary.
- It employs YOLOv8 for object detection and integrates various recommendation algorithms.
- The prototype demonstrates potential for enhancing navigation in digital art collections.
- Symbolic structures are used to derive meanings from visual elements.
- Further engineering is needed to refine the system's capabilities.
Computer Science > Digital Libraries arXiv:2602.19698 (cs) [Submitted on 23 Feb 2026] Title:Iconographic Classification and Content-Based Recommendation for Digitized Artworks Authors:Krzysztof Kutt, Maciej Baczyński View a PDF of the paper titled Iconographic Classification and Content-Based Recommendation for Digitized Artworks, by Krzysztof Kutt and Maciej Baczy\'nski View PDF HTML (experimental) Abstract: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. Comments: Subjects: Digital Libraries (cs.DL); Artificial Intelligence (cs.AI); Computer Vision and Pattern Recognition (cs.CV); Information Retrieval (cs.IR) Cite as...
