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.

Statistics

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Issue
No. 9 (2023)
Section
Artikel
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.