标题： 自动化Iconclass：大型宗教木刻的大规模分类中的LLMs和RAG 显示英文标题

标题： Automating Iconclass: LLMs and RAG for Large-Scale Classification of Religious Woodcuts

摘要： 本文提出了一种新的方法，通过结合大型语言模型（LLMs）和向量数据库以及检索增强生成（RAG），对早期现代宗教图像进行分类。 该方法利用了神圣罗马帝国书籍插图的全页上下文，使LLM能够生成包含视觉和文本元素的详细描述。 然后通过混合向量搜索将这些描述与相关的Iconclass代码匹配。 该方法在五个和四个分类级别上的精度分别达到87%和92%，显著优于传统的图像和关键词搜索。 通过使用全页描述和RAG，系统提高了分类准确性，为早期现代视觉档案的大规模分析提供了一个强大的工具。 这种跨学科方法展示了LLMs和RAG在推进艺术史和数字人文学科研究方面的日益增长的潜力。 显示英文摘要

摘要： This paper presents a novel methodology for classifying early modern religious images by using Large Language Models (LLMs) and vector databases in combination with Retrieval-Augmented Generation (RAG). The approach leverages the full-page context of book illustrations from the Holy Roman Empire, allowing the LLM to generate detailed descriptions that incorporate both visual and textual elements. These descriptions are then matched to relevant Iconclass codes through a hybrid vector search. This method achieves 87% and 92% precision at five and four levels of classification, significantly outperforming traditional image and keyword-based searches. By employing full-page descriptions and RAG, the system enhances classification accuracy, offering a powerful tool for large-scale analysis of early modern visual archives. This interdisciplinary approach demonstrates the growing potential of LLMs and RAG in advancing research within art history and digital humanities.