Terminology sources, such as controlled vocabularies, thesauri and classification systems, play a key role in digitizing cultural heritage. However, Information Retrieval (IR) systems that allow to query and explore these lexical resources often lack an adequate representation of the semantics behind the user's search, which can be conveyed through multiple expression modalities (e.g., images, keywords or textual descriptions). This paper presents the implementation of a new search engine for one of the most widely used iconography classification system, Iconclass. The novelty of this system is the use of a pre-trained vision-language model, namely CLIP, to retrieve and explore Iconclass concepts using visual or textual queries.

https://export.arxiv.org/pdf/2306.16529v1.pdf

Multimodal Search on Iconclass using Vision-Language Pre-Trained Models

. This paper describes our experience in combining a large corpus of material that was classified manually over decades of Art Historical and Book History research using the ICONCLASS 4 subject classification system with heuristics based on a current state-of-the-art neural network (CLIP from OpenAI), leveraging visual similarity to provide suggestions for automated classification of cultural heritage content. The effectiveness of the approach is demonstrated through an evaluation of the underlying extreme multi-label classification problem.

The Art Historian’s Bicycle Becomes an E-Bike

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The Art Historian’s Bicycle Becomes an E-Bike