Marx, Lizzie;
Zinnen, Mathias;
Collette Ehrich, Sofia;
Tullett, William;
Bembibre, Cecilia;
Leemans, Inger;
(2023)
Seeing Smell: Sourcing Olfactory Imagery Using Artificial Intelligence.
Arts et Savoirs
(20)
10.4000/aes.6834.
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Abstract
How can artificial intelligence help to “see” smells in works of art? This article discusses the ways in which the Horizon 2020 Odeuropa project uses computer vision to search for olfactory imagery in digital heritage collections. It provides a literature review of the latest approaches to researching smell in art, and outlines the methodology for mining digital collections. It also raises questions about what it means to source smell in digital archives, the challenges encountered when working with the technology, and its possibilities. It concludes with a case study illustrating the potential of such an approach, where computer vision was used to find perfumed gloves in works of art, resulting in an olfactory guided tour of Museum Ulm.
| Type: | Article |
|---|---|
| Title: | Seeing Smell: Sourcing Olfactory Imagery Using Artificial Intelligence |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.4000/aes.6834 |
| Publisher version: | http://dx.doi.org/10.4000/aes.6834 |
| Language: | English |
| Additional information: | The text only may be used under licence CC BY-NC-ND 4.0. All other elements (illustrations, imported files) are “All rights reserved”, unless otherwise stated. |
| Keywords: | computer vision, sensory art history, cultural heritage, digital archives, iconography |
| UCL classification: | UCL UCL > Provost and Vice Provost Offices > UCL BEAMS UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of the Built Environment UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of the Built Environment > Bartlett School Env, Energy and Resources |
| URI: | https://discovery.ucl.ac.uk/id/eprint/10189090 |
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