When A Machine Learning Algorithm Studied Fine Art Paintings, It Saw Things Art Historians Had Never Noticed

At the heart of their method, is a new technique developed at Dartmouth College in New Hampshire and Microsoft research in Cambridge, UK, for classifying pictures according to the visual concepts that they contain. These concepts are called classemes and include everything from simple object description such as duck, frisbee, man, wheelbarrow to shades of colour to higher-level descriptions such as dead body, body of water, walking and so on.


For each painting, they limit the number of concepts and points of interest generated by their method to 3000 in the interests of efficient computation. This process generates a list of describing words that can be thought of as a kind of vector. The task is then to look for similar vectors using natural language techniques and a machine learning algorithm.

(source accessed 11.01.2016)