The Rijksmuseum Challenge: Museum-Centered Visual Recognition

This paper offers a challenge for visual classification and content-based retrieval of artistic content. The challenge is posed from a museum-centric point of view offering a wide range of object types including paintings, photographs, ce-ramics, furniture, etc. The freely available dataset consists of 112,039 photographic reproductions of the artworks ex-hibited in the Rijksmuseum in Amsterdam, the Netherlands. We offer four automatic visual recognition challenges con-sisting of predicting the artist, type, material and creation year. We include a set of baseline results, and make avail-able state-of-the-art image features encoded with the Fisher vector. Progress on this challenge improves the tools of a museum curator while improving content-based exploration by online visitors of the museum collection