Iconography refers to the methodical study and interpretation of thematic content in the visual arts, distinguishing it, e.g., from purely formal or aesthetic considerations. In iconographic studies, Iconclass is a widely used taxonomy that encapsulates historical, biblical, and literary themes, among others. However, given the hierarchical nature and inherent complexity of such a taxonomy, it is highly desirable to use automated methods for (Iconclass-based) image classification. Previous studies either focused narrowly on certain subsets of narratives or failed to exploit Iconclass’s hierarchical structure. In this paper, we propose a novel approach for Hierarchical Multi-label Classification (HMC) of iconographic concepts in images. We present three strategies, including Language Models (LMs), for the generation of textual image descriptions using keywords extracted from Iconclass. These descriptions are utilized to pre-train a Vision-Language Model (VLM) based on a newly introduced data set of 477,569 images with more than 20,000 Iconclass concepts, far more than considered in previous studies. Furthermore, we present five approaches to multi-label classification, including a novel transformer decoder that leverages hierarchical information from the Iconclass taxonomy. Experimental results show the superiority of this approach over reasonable baselines.

In this paper, we introduce iART: an open Web platform for art-historical research that facilitates the process of comparative vision. The system integrates various machine learning techniques for keyword- and content-based image retrieval as well as category formation via clustering. An intuitive GUI supports users to define queries and explore results. By using a state-of-the-art cross-modal deep learning approach, it is possible to search for concepts that were not previously detected by trained classification models. Art-historical objects from large, openly licensed collections such as Amsterdam Rijksmuseum and Wikidata are made available to users.