Classifying a painting by artist and style is tricky for humans; spotting the links between different artists and styles is harder still. So it should be impossible for machines, right?
Few areas of academic inquiry have escaped the influence of computer science and machine learning. But one of them is the history of art. The challenge of analyzing paintings, recognizing their artists, and identifying their style and content has always been beyond the capability of even the most advanced algorithms.
That is now changing thanks to recent advances in machine learning based on approaches such as deep convolutional neural networks. In just a few years, computer scientists have created machines capable of matching and sometimes outperforming humans in all kinds of pattern recognition tasks.
Today, we see just how advanced these approaches have become in the hands of Babak Saleh and Ahmed Elgammal at Rutgers University in New Jersey. These guys have used these new machine learning techniques to train algorithms to recognize the artist and style of a fine-art painting with an accuracy that has never been achieved before.
What’s more, the results reveal connections between artists, and between entire painting styles, that art historians have labored for years to understand.
Saleh and Elgammal begin with a database of images of more than 80,000 paintings by more than a 1,000 artists spanning 15 centuries. These paintings cover 27 different styles, each with more than 1,500 examples. The researchers also classify the works by genre, such as interior, cityscape, landscape, and so on…