Based on the highlights of The Metropolitan Museum of Art's collection, the purpose of this paper is to examine the similarities and differences between the subject keywords tags assigned by the museum and those produced by three computer vision systems.
This paper uses computer vision tools to generate the data and the Getty Research Institute's Art and Architecture Thesaurus (AAT) to compare the subject keyword tags.
This paper finds that there are clear opportunities to use computer vision technologies to automatically generate tags that expand the terms used by the museum. This brings a new perspective to the collection that is different from the traditional art historical one. However, the study also surfaces challenges about the accuracy and lack of context within the computer vision results.
This finding has important implications on how these machine-generated tags complement the current taxonomies and vocabularies inputted in the collection database. In consequence, the museum needs to consider the selection process for choosing which computer vision system to apply to their collection. Furthermore, they also need to think critically about the kind of tags they wish to use, such as colors, materials or objects.
The study results add to the rapidly evolving field of computer vision within the art information context and provide recommendations of aspects to consider before selecting and implementing these technologies.
The authors would like to thank Jennie Choi, General Manager of Collection Information at The Metropolitan Museum of Art for her feedback and contributions to this research paper. The authors would also like to thank Patricia Harpring, Managing Editor and Gregg Garcia, Software Architect at the Getty Vocabulary Program for their help to use Art & Architecture Thesaurus on OpenRefine.
Villaespesa, E. and Crider, S. (2021), "A critical comparison analysis between human and machine-generated tags for the Metropolitan Museum of Art's collection", Journal of Documentation, Vol. 77 No. 4, pp. 946-964. https://doi.org/10.1108/JD-04-2020-0060
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