Computer scientists designed an algorithm to analyse famous paintings and identify the artists' influences based solely on what was on the canvas. The result is pretty amazing.
Article by Zach Sokol for the Creators Project
Could a computer program influence how we understand art history and the canon? Or, could an artificially intelligent algorithm do the work of art experts for them? A recent researcher project doesn't quite suggest such a reality, but it does demonstrate that machines can highlight subtleties within arts and culture that humans have previously never noticed.
In a paper titled "Toward Automated Discovery Of Artistic Influence" by Babak Saleh and a team of computer science researchers at Rutgers, the academics explained how they used nuanced imaging technology and classification systems to robotize the process of understanding how famous artists have influenced and inspired one another.
For their research, the team chose 1,700 paintings by 66 artists, covering the 15th to the late 20th century. Using a technique that analyzes visual concepts called "classemes"—wherein objects, color shades, subjects' movement, and more are marked—the researchers created a list of 3,000 classemes for each painting, data which The Physics arXiv Blog compares to a vector. Then, they used an artificially intelligent algorithm to evaluate the vectors and look for similarities or overlapping qualities among the 1,700 paintings. ArXiv adds, "To create a ground truth against which to measure their results, they also collate expert opinions on which these artists have influenced the others."
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