Impressionism, expressionism, surrealism: Automated recognition of painters and schools of art
By Lior Shamir, Tomasz J. Macura, D. Mark Eckley, Ilya G. Goldberg. 2010
185 citations recorded by the source corpus.
Subfields: Cognitive Neuroscience, Computer Graphics and Computer-Aided Design, Computer Vision and Pattern Recognition
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
- Weiming Dong
- Pierre Geurts
In Computer Graphics and Computer-Aided Design
In Computer Vision and Pattern Recognition
- Luc Van Gool
- Ming-Hsuan Yang
- Trevor Darrell
- Jitendra Malik
- Xiaogang Wang
- Dacheng Tao
- Andrew Zisserman
- Andrea Vedaldi
Citations in the corpus
- Recognizing Image Style (2013, background): “Several previous authors have developed systems to classify classic painting styles, including [15, 25].”
- Selective clustering for representative paintings selection (2019, background): “…a tremendous progress in the automatic analysis of artworks targeting a diverse range of tasks, such as identifying western paintings [36], inferring painting styles [1, 6, 35], distinguishing authentic drawing from imitations [17], and automatically generating artworks in specific style [10, 25].”
- Transfer Learning with Style Transfer between the Photorealistic and Artistic Domain (2021, methodology): “Machine learning methods were also applied to attribution problems [25, 26, 27], genre classification [28] and other kinds of style-based classification [29, 30, 31].”