Expanding Perspectives to Improve Access to Visual Archives through Multimodal Image Enrichment

The research tackles key challenges for improving the discovery of large-scale visual collections within the Cultural Heritage (CH) domain, particularly in museums and archives. Its contribution is a multimodal content understanding approach designed for image collections that lack relevant metadata, which hinders effective discovery. The proposed method utilises AI-assisted image classification and unified vision–language understanding, combining visual features with semantic context to generate rich and meaningful metadata. The proposed approach enables experts to enrich and visualise large-scale datasets of image collections by assigning both expert and non-expert labels, aligning with the Findable, Accessible, Interoperable and Reusable (FAIR) principles. Thus, the novel workflow broadens access to this visual material for diverse audiences through search and browse interfaces in a web browser. The proposed approach is demonstrated using the previously unclassified Design Archives’ glass plate negatives dataset, which consists of approximately ∼10,000 digitised images depicting 20th-century historical designs. Through an Artificial Intelligence (AI) driven workflow, the dataset is enriched with expert and non-expert information. Users can search and browse results using 2D and 3D visualisations, as well as text-based search. The research also explores the advantages and current limitations of the proposed visualisation approach in creating more meaningful search and browsing functionalities for large Cultural Heritage (CH) visual collections. The results demonstrate that while 3D visualisations offer more affordances than their 2D counterpart, users require further support to interact with the large-scale datasets meaningfully. Hence, there is a need for discovery interfaces that support interactivity, visual cues and text-based search to enhance the users’ discovery journey.