Algorithm Finds Hidden Connections Between Paintings at the Met
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Algorithm finds hidden connections between paintings at the Met:
Art is often heralded as the greatest journey into the past, solidifying a moment in time and space; the beautiful vehicle that lets us momentarily escape the present.
With the boundless treasure trove of paintings that exist, the connections between these works of art from different periods of time and space can often go overlooked. It's impossible for even the most knowledgeable of art critics to take in millions of paintings across thousands of years and be able to find unexpected parallels in themes, motifs, and visual styles.
To streamline this process, a group of researchers from MIT's Computer Science and Artificial Intelligence Laboratory (CSAIL) and Microsoft created an algorithm to discover hidden connections between paintings at the Metropolitan Museum of Art (the Met) and Amsterdam's Rijksmuseum.
Inspired by a special exhibit "Rembrandt and Velazquez" in the Rijksmuseum, the new "MosAIc" system finds paired or "analogous" works from different cultures, artists, and media by using deep networks to understand how "close" two images are. In that exhibit, the researchers were inspired by an unlikely, yet similar pairing: Francisco de Zurbaran's "The Martyrdom of Saint Serapion"and Jan Asselijn's "The Threatened Swan," two works that portray scenes of profound altruism with an eerie visual resemblance.
"These two artists did not have a correspondence or meet each other during their lives, yet their paintings hinted at a rich, latent structure that underlies both of their works," says CSAIL PhD student Mark Hamilton, the lead author on a paper about "MosAIc."
[...] "Going forward, we hope this work inspires others to think about how tools from information retrieval can help other fields like the arts, humanities, social science, and medicine," says Hamilton. "These fields are rich with information that has never been processed with these techniques and can be a source for great inspiration for both computer scientists and domain experts. This work can be expanded in terms of new datasets, new types of queries, and new ways to understand the connections between works."
Journal Reference:
Mark Hamilton, Stephanie Fu, William T. Freeman, et al. Conditional Image Retrieval (arXiv: 2007.07177)
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