This is not an apple! Benefits and challenges of applying computer vision to museum collections

Villaespesa, Elena and Murphy, Oonagh. 2021. This is not an apple! Benefits and challenges of applying computer vision to museum collections. Museum Management and Curatorship, 36(4), pp. 362-383. ISSN 0964-7775 [Article]

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Abstract or Description

The application of computer vision on museum collection data is at an experimental stage with predictions that it will grow in significance and use in the coming years. This research, based on the analysis of five case studies and semi-structured interviews with museum professionals, examined the opportunities and challenges of these technologies, the resources and funding required, and the ethical implications that arise during these initiatives. The case studies examined in this paper are drawn from: The Metropolitan Museum of Art (USA), Princeton University Art Museum (USA), Museum of Modern Art (USA), Harvard Art Museums (USA), Science Museum Group (UK). The research findings highlight the possibilities of computer vision to offer new ways to analyze, describe and present museum collections. However, their actual implementation on digital products is currently very limited due to the lack of resources and the inaccuracies created by algorithms. This research adds to the rapidly evolving field of computer vision within the museum sector and provides recommendations to operationalize the usage of these technologies, increase the transparency on their application, create ethics playbooks to manage potential bias and collaborate across the museum sector.

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Additional Information:

This work was supported by Arts and Humanities Research Council: [Grant Number AH/S012516/1].


AI, Artificial Intelligence, Computer Vision, Machine Vision, Collections Management, Museums, Museum Studies, Digital Culture, Bias, Ethics, Data Management

Departments, Centres and Research Units:

Institute for Cultural and Creative Entrepreneurship (ICCE)


6 January 2021Accepted
27 January 2021Published

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Date Deposited:

05 Aug 2021 09:27

Last Modified:

17 Jan 2022 15:30

Peer Reviewed:

Yes, this version has been peer-reviewed.


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