Iconographic Classification and Content-Based Recommendation for Digitized Artworks

2026-02-23

AI Summary

This study addresses the challenge of enhancing cataloging efficiency and browsing experience in large-scale cultural heritage collections by enabling content-based artwork recommendation through automated iconographic classification. To this end, we propose a four-stage workflow: visual elements are first detected using YOLOv8, then mapped to the Iconclass iconographic thesaurus; hierarchical semantic inference is subsequently performed via rule-based reasoning; finally, recommendations are generated by integrating strategies based on hierarchical proximity, IDF-weighted overlap, and Jaccard similarity. This work presents the first deep integration of YOLOv8 with the Iconclass system, demonstrating the feasibility and potential of iconography-aware computer vision methods for automatic artwork annotation and intelligent navigation in digital art collections.

Technology Category

• Computer Vision: Object Detection & Categorization
• Data Mining & Knowledge Management: Conversational Systems for Recommendation & Retrieval
• Humans and AI: Crowd Sourcing and Human Computation

Application Category

• Web Mining and Content Analysis: Normalization, clustering, classification, and summarization of Web text
• Semantics and Knowledge: Scalable techniques for the creation, curation, publication, maintenance, and consumption of large, Web-based, structured, reusable, knowledge graphs and ontologies
• Graph Algorithms and Modeling for the Web: Querying, indexing, and retrieval in Web-related graphs

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.

Problem

• iconographic classification
• content-based recommendation
• digitized artworks
• Iconclass
• computer vision

Innovation

• iconographic classification
• content-based recommendation
• Iconclass
• YOLOv8
• symbolic reasoning