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4th Critical Space at the Belvedere Research Center

On the 22nd of September 2020 the DAD team met with Christian Huemer and Johanna Aufreiter from the Belvedere Research Center to discuss our results concerning Belvedere’s online collection. One focus of the meeting was our engagement with the room on “Viennese Portraiture in the Biedermeier Period” in Belvedere’s permanent exhibition.

Applying our algorithm to find pathways of semantic meaning [Flexer 2020] between works of art, we are able to suggest additional works for the liminal spaces between individiual positions in the curatorial narrative, opening up new sub-narratives for the room. Based on a word embedding [Mikolov et al 2013] of the keywords associated with the paintings, our algorithm suggests works of art which follow a pathway between the respective semantic meanings. Moreover we are able to further constrain our liminal curation by requiring all art works to fit an additional overall topic chosen by a human curator, again translated to the language of Belvedere’s keyword system via word embedding. As an example see a “Gender” constraint applied to the Biedermeier room.

A conceivable outcome is a revision of the Biedermeier room achieved via a joint curation of human and machine. This, as well as other approaches towards the Belvedere collection, will be the center of further exchange between DAD and the Belvedere.

All depicted paintings in this blog post by Belvedere, Vienna, Austria (CC BY-SA 4.0).

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3rd Critical Space on discovering semantic pathways through a fine arts collection

DAD´s Arthur Flexer gave a semi-virtual lecture on “Discovering X Degrees of Keyword Separation in a Fine Arts Collection” at the Austrian Research Institute for Artificial Intelligence (OFAI, 24.6.2020). The presented work is inspired by the project ‘X Degrees of Separation‘ by ‘Google Arts and Culture’, which explores the “hidden paths through culture” by analyzing visual features of artworks to find pathways between any two artifacts through a chain of artworks. In his work, Arthur Flexer is more interested in finding pathways of the semantic meaning of works of art rather than just their visual features. Therefore he used word embedding [Mikolov et al 2013], which encodes semantic similarities between words by modelling the context to their neighboring words in a large training text corpus. This is used to embed keywords of Belvedere´s online fine arts collection and obtain pathways through the resulting semantic space.

Keywords from left to right: [‘Resurrection’, ‘Christ’], [‘Christ’], [‘Death’, ‘Skeleton’], [‘Vulture’], [‘Angel’, ‘Air’, ‘Martyrdom’, ‘Suffering’, ‘Failure’, ‘Death’, ‘Andreas’, ‘Multiple Layer Room’]. All images by Belvedere, Vienna, Austria (CC BY-SA 4.0).

The above exemplary result starts with a sculpture with keywords ‘Resurrection’ and ‘Christ’ where the painting in the end position has keywords around the topic of ‘Death’ and ‘Martyrdom’. The second artwork in the pathway is a relief showing ‘Christ’, while the third is a painting tagged with ‘Death’ and ‘Skeleton’, hence already semantically closer to the topics of ‘Martyrdom’, ‘Suffering’ and ‘Death’ of the end artwork. In fourth position is an etching with the only keyword ‘Vulture’, which is semantically close to ‘Angel’, ‘Air’ and ‘Death’ of the ending artwork.

In the ensuing discussion of results it was found remarkable how machine learning via word embedding replicates existing biases and prejudice in the society. In the above query with the word “Homosexuality” the most similar word out of 22 million terms in the word embedding model is “Paedophilia”, one of the worst prejudice against homosexual people. The word embedding model has been trained on the Wikipedia and Common Crawl corpus [Mikolov et al 2018], which helps explaining the replication of very common and persisting prejudice in our society.

OFAI´s Brigitte Krenn found it interesting how the very reglemented and almost scientific language in Belvedere’s keywords (stemming from the Iconclass project) is contrasted with everyday language via usage of word embedding. As can be seen above, the most similar keywords to “Homosexuality” are “Rape”, “Religion”, “Violence” and “Islam” (all translated from German). This is of course a direct result of the biases inherent to the word embedding model. DAD’s Alexander Martos called this phenomenon “re-socialising of arts via natural language processing” or rather “re-a-socialising” since it uncovers asocial societal tendencies and (re-?) introduces them to the world of fine arts.