Computer science putting art analysis on a faster track
AT RUTGERS University in New Jersey, scientists are training a computer to do instantly what might take art historians years: analyse thousands of paintings to understand which artists influenced others.
The software scans digital images of paintings, looking for common features—composition, color, line and objects shown in the piece, among others. It identifies paintings that share visual elements, suggesting that the earlier painting’s artist influenced the later one’s.
The project is part of a broader effort at Rutgers to apply computer science techniques to the humanities. This year, the university established a Digital Humanities Lab, based in its Computational Biomedicine Imaging and Modelling Centre. The art application is among its first projects.
The field is growing. The Getty Foundation in Los Angeles provides grants to researchers in digital art history; George Mason University’s Roy Rosenzweig Centre for History and New Media, one of the recipients, received US$155,000 (RM511,000).
And Washington’s Folger Shakespeare Library recently was awarded a grant to digitise its collection of manuscripts and artwork. The goal is to let outside researchers download the entire database and analyse it, according to Michael Wit more, the library’s director. But some art historians — including Lisa Strong, director of Georgetown University’s art and museum studies program — are sceptical about visual algorithms such as the one in development at Rutgers.
“You can’t really impose a scientific framework so profitably on an exercise like painting analysis,” she said. “It’s not something where raw data tells you something. It’s all subjective.”
Still, the software has revealed some connections that art historians had not — at least, according to the team’s survey of existing art history literature, said Ahmed Elgammal, an associate professor of computer science, who has been working on the project for about three years.
“The advantage is it can easily mine thousands and millions of art works in a very (efficient) way.” — WP-Bloomberg