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Artificial intelligence for content and context metadata retrieval in photographs and image groups

Author

Listed:
  • Fornaro, Peter

    (University of Basel, Digital Humanities Lab, Switzerland)

  • Chiquet, Vera

    (University of Basel, Digital Humanities Lab, Switzerland)

Abstract

Cataloguing is a frequent bottleneck in the digitisation of analogue images as it is impossible to scale the necessary content-related knowledge. As this paper discusses, however, it is possible to use well-trained artificial intelligence to semi-automate metadata enhancement for photographic collections. The paper describes a study in which participants indexed historical collections of photographs. In a subsequent interdisciplinary project with contributions from cultural anthropologists, computer scientists, digital humanities researchers and art historians, these descriptions and indexes were then used to train machine-learning components. With this interdisciplinary approach, it is hoped that cataloguing practices can be enhanced, generating new insights into AI and semantic metadata.

Suggested Citation

  • Fornaro, Peter & Chiquet, Vera, 2021. "Artificial intelligence for content and context metadata retrieval in photographs and image groups," Journal of Digital Media Management, Henry Stewart Publications, vol. 9(4), pages 297-304, June.
    • Handle: RePEc:aza:jdmm00:y:2021:v:9:i:4:p:297-304
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    More about this item

    Keywords

    Machine learning; analogue and digital photography; cultural heritage; semantic metadata; cbir; iiif; curation;
    All these keywords.

    JEL classification:

    • M11 - Business Administration and Business Economics; Marketing; Accounting; Personnel Economics - - Business Administration - - - Production Management
    • M15 - Business Administration and Business Economics; Marketing; Accounting; Personnel Economics - - Business Administration - - - IT Management

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