A critical comparison analysis between human and machine-generated tags for the Metropolitan Museum of Art's collection

Abstract

PurposeBased 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.Design/methodology/approachThis 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.FindingsThis 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.Practical implicationsThis 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.Originality/valueThe 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.

Exhibiting the Heritage of COVID-19—A Conversation with ChatGPT

“…ChatGPT is seen as a potential tool for message generation, content marketing, audience interaction, and sentiment analysis of general inquiries, all of which can be readily ported to a museum context [97][98][99]. Furthermore, ChatGPT (or future iterations) can be readily used as a generic document and content analysis tool to extract and summarize pertinent data from images [100] or longer documents or inventory data [101], which can then be used to develop object tags and exhibit labels [102], exhibition texts and catalog information [103,104], museum guides [105], as well as scripts for audio guides [106] and augmented reality tours [107]. Some authors have expanded this to examine the use of generative AI in museum or exhibition design and planning [100,[108][109][110].…”