Bourached, Anthony;
Stork, DG;
Cann, GH;
Griffiths, RR;
(2021)
Computational identification of significant actors in paintings through symbols and attributes.
arXiv.org: Ithaca (NY), USA.
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Abstract
The automatic analysis of fine art paintings presents a number of novel technical challenges to artificial intelligence, computer vision, machine learning, and knowledge representation quite distinct from those arising in the analysis of traditional photographs. The most important difference is that many realist paintings depict stories or episodes in order to convey a lesson, moral, or meaning. One early step in automatic interpretation and extraction of meaning in artworks is the identifications of figures (actors). In Christian art, specifically, one must identify the actors in order to identify the Biblical episode or story depicted, an important step in understanding the artwork. We designed an automatic system based on deep convolutional neural networks and simple knowledge database to identify saints throughout six centuries of Christian art based in large part upon saints symbols or attributes. Our work represents initial steps in the broad task of automatic semantic interpretation of messages and meaning in fine art.
| Type: | Working / discussion paper |
|---|---|
| Title: | Computational identification of significant actors in paintings through symbols and attributes |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.48550/arXiv.2102.02732 |
| Publisher version: | https://doi.org/10.48550/arXiv.2102.02732 |
| Language: | English |
| Additional information: | This version is the version of record. For information on re-use, please refer to the publisher's terms and conditions. |
| UCL classification: | UCL UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Population Health Sciences > Institute of Health Informatics |
| URI: | https://discovery.ucl.ac.uk/id/eprint/10170832 |
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