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Abstract

Research limitations/implications While this is the first analysis of the complete collection of paintings in the English Wikipedia, the authors' results are conservative as many paintings are not identified as such in Wikidata, used for automatic harvesting. Tools to analyse image view specifically are not yet available and user privacy is highly protected, limiting the disaggregation of user data. This study serves to document a lack of diversity in image availability for global online consumption, favouring well-known Western objects. At the same time, the study evidences the need to diversify the use of images to reflect a more global perspective, particularly where paintings are used to represent concepts of techniques. Practical implications Museums wanting to increase visibility can target the reuse of their collections in non-art-related articles, which received 88% of all views in the authors' sample. Given the few museums collaborating with the Wikimedia Foundation and the apparent inefficiency resulting from leaving the use of paintings as illustration to the crowd, as only 3% of painting images are used, suggests further collaborative efforts to reposition museum content may be beneficial. Social implications This paper highlights the reach of Wikipedia as information source, where museum content can be positioned to reach a greater user group beyond the usual museum visitor, in turn increasing visual and digital literacy. Originality/value This is the first study that documents the frequency of use and views, the topical use and the originating institution of "all the paintings" in the English Wikipedia edition.

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Abstract

Purpose 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. Design/methodology/approach 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. Findings 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. Practical implications 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. Originality/value 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.