The Machine Vision Algorithm Beating Art Historians at Their Own Game

This is an interesting application of image processing. The machine learning algorithms are trained on a subset of paintings taken from a data set of more than 80,000. The resulting feature set has over 400 dimensions.

When presented with a painting it has not seen before, it correctly guessed the artist more than 60% of the time. It has also detected additional links between different styles and periods:

It links expressionism and fauvism, which might be expected given that the latter movement is often thought of as a type of expressionism. It links the mannerist and Renaissance styles, which clearly reflects that fact that mannerism is a form of early Renaissance painting.

However, it also apparently confuses certain styles:

… it often confuses examples of abstract expressionism and action paintings, in which artists drip or fling paint and step on the canvas. Saleh and Elgammal [the creators of the ML algorithms] … say that this kind of mix-up would be entirely understandable for a human viewer. “’Action painting’ is a type or subgenre of “abstract expressionism,’” they point out.

Of course, this could also mean that the machine is correct and different “genres” of abstact paintings are completely arbitrary. But what is does highlight is that machine learning has a way to go before it can start offering subjective opinions.

via The Machine Vision Algorithm Beating Art Historians at Their Own Game | MIT Technology Review.

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