Capturing the Memorable Points of Cultural Heritage Images: Development of an Intelligent Metadata Annotation Tool for Cultural Heritage Image Resources Integrating Iconography and AI

Abstract

This project aims to design a new AI-enabled tool for deep semantic annotation of content-level metadata for cultural heritage images, providing a convenient, open, and intelligent metadata indexing toolkit for cultural heritage preservation institutions worldwide. Based on Panofsky's iconographic theory, the tool supports a comprehensive scope of metadata annotation. In addition to basic metadata (e.g. title, creator, creation date, genre, and material), it also covers multi-level iconographic objects and their descriptions—from abstract conceptual categories (people, places, animals, plants) to fine-grained concrete instances (e.g., maids,emperors, pine trees, river boats). One crucial feature is the automatic indexing module based on the fine-tuned SAM (Segment Anything Model) deep neural network, which delivers accurate object detection and classification. The interactive indexing plug-in enables personalized editing, revision, and reindexing, and establishes semantic links between individual records and external knowledge bases like DB pedia and China Biographical Database. Deployed in cooperated Chinese museums and libraries, the tool has significantly improved metadata cataloging, semantic indexing depth, and workflow efficiency. It provides essential technical support for digital storytelling, cultural memory preservation, image analysis, and intelligent services of Chinese cultural heritage, and inspires further exploration and optimization of intelligent metadata annotation systems for cultural heritage resources.