The Rijksmuseum Challenge: Museum-Centered Visual Recognition
By Thomas Mensink, Jan van Gemert. 2014
147 citations recorded by the source corpus.
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, ceramics, furniture, etc. The freely available dataset consists of 112,039 photographic reproductions of the artworks exhibited in the Rijksmuseum in Amsterdam, the Netherlands. We offer four automatic visual recognition challenges consisting of predicting the artist, type, material and creation year. We include a set of baseline results, and make available 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.
Subfields: Cognitive Neuroscience, Computer Vision and Pattern Recognition, Conservation
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Cognitive Neuroscience
- Nikolay Banar
- Walter Daelemans
- Aaron Hertzmann
- Trevor Darrell
- Mike Kestemont
- Matthia Sabatelli
- Pierre Geurts
In Computer Vision and Pattern Recognition
- Luc Van Gool
- Trevor Darrell
- Ming-Hsuan Yang
- Jitendra Malik
- Xiaogang Wang
- Dacheng Tao
- Bernt Schiele
- Andrew Zisserman
Citations in the corpus
- Recognizing Image Style (2013, background): “Mensink [21] provides a larger dataset of artworks, but does not consider style classification.”
- Machine Learning for Cultural Heritage: A Survey (2020, methodology): “The experimental evaluations was performed on two dataset of paintings: the Rijksmuseum Challenge 2014 dataset [28] and a much smaller dataset obtaining by random sampling the DAMS (Digital Asset Management System) repository, which aggregates several digital collections come from the city of…”
- Transfer Learning with Style Transfer between the Photorealistic and Artistic Domain (2021, methodology): “First of all, the available (annotated) datasets from art history are typically much smaller; luckily, a number of domain-specific, larger-scale resources have been released that help mitigate this situation [6, 7, 8, 9, 10, 11, 12, 13, 14].”