Multitask painting categorization by deep multibranch neural network
作者：
Highlights：
• A novel deep multibranch multitask neural network architecture.
• The different branches process the input image at different scales.
• A trainable crop strategy to feed branches with the most informative regions.
• The injection of hand-crafted features inside the network for painting categorization.
• A new dataset composed of 100k paintings from 1508 artists, 125 styles, 41 genres.
摘要
•A novel deep multibranch multitask neural network architecture.•The different branches process the input image at different scales.•A trainable crop strategy to feed branches with the most informative regions.•The injection of hand-crafted features inside the network for painting categorization.•A new dataset composed of 100k paintings from 1508 artists, 125 styles, 41 genres.
论文关键词：Painting categorization,Painting style classification,Painter recognition,Deep convolutional neural network,Multiresolution,Multitask
论文评审过程：Received 27 July 2018, Revised 10 April 2019, Accepted 25 May 2019, Available online 1 June 2019, Version of Record 14 June 2019.
论文官网地址：https://doi.org/10.1016/j.eswa.2019.05.036
