Identifying MoMA artworks using Machine Learning
By Google Arts & Culture
Given years of experience and some diligent research, identifying each work of art in an old exhibition photo doesn’t sound so hard, does it? Now imagine you have more than 30,000 photos, dating back to 1929. Google Arts & Culture and MoMA’s Digital Media team set out to face this daunting challenge—or at least get a head start—using machine learning and computer vision technology.
Google Arts & Culture used an algorithm to comb through over 30,000 exhibition photos, looking for matches with the more than 65,000 works in our online collection in. In total, it recognized over 27,000 artworks in these images, and we used those results to create thousands of new links between our exhibition history and online collection.
Now a photo from a 1929 painting exhibition opens a window into an iconic work by Paul Cézanne; a 1965 shot of Robert Rauschenberg prints connects you to those same works in MoMA’s 2017 Rauschenberg retrospective; and one corner of a 2013 design exhibition becomes a portal into poster art across two centuries. While hardly comprehensive, it’s a great start—and a remarkable feat given the sheer volume of information involved.