IMGS.AI.

A Multimodal Search Engine for Digital Art History

  • Fabian Offert (Author)
    Asst. Prof., University of California, Santa Barbara
  • Peter Bell (Author)
    Prof., Philipps-Universität Marburg

Identifiers (Article)

DOI: https://doi.org/10.11588/dahj.2023.9.91295

Abstract

We present a web application that facilitates multimodal search within institutional image collections using current-generation machine learning models like CLIP. Further, we discuss image retrieval as a combined computer vision/human-computer interaction problem, and propose that the standardization of feature extraction is one of the main problems that digital art history faces today.

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Published
2024-11-19
Issue
No. 9 (2023)
Section
Project Spotlight
Categories
Responsiveness and Responsibility in Times of AI
Language
English
Keywords
big image data, computer vision, feature extraction, machine learning, image retrieval
How to Cite
Offert, Fabian, and Peter Bell. 2024. “IMGS.AI.: A Multimodal Search Engine for Digital Art History”. International Journal for Digital Art History, no. 9 (November):5.28-5.39. https://doi.org/10.11588/dahj.2023.9.91295.

License

Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.