Iconographic Classification and Content-Based Recommendation for Digitized Artworks
This work addresses the challenge of efficiently cataloging and navigating large digitized art collections for heritage institutions, though it is incremental as it builds on existing methods like YOLOv8 and Iconclass.
The authors tackled the problem of automating iconographic classification and content-based recommendation for digitized artworks by developing a proof-of-concept system using the Iconclass vocabulary and AI methods, resulting in a prototype that integrates object detection, rule-based inference, and multiple recommenders to accelerate cataloging and enhance navigation in heritage repositories.
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.