as efficient as non-neural style transfers solutions. Evermore, a CNN finds as informative the original photographs without any style transfer applied.

The remainder of the paper is organized as follows: section \[Sect:Related\] presents previous relevant works, section \[Sect:CNN\] summarizes the CNN choices made and section \[Sect:PaintingUnderstanding\] will discuss different aspects of painting understanding. Section \[Sect:Databases\] presents the used databases, while implementation details and results are presented in section \[Sect:Results\]. The paper ends with discussions about the impact of the results.

Related Work  {#Sect:Related}
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This work investigates the capabilities of CNNs’ to recognize the subject of paintings as compared with the performance of humans. Thus, relevant prior work refers to solutions for object and scene recognition in paintings. As paintings are an abstraction of real images, scene recognition in photographs is also relevant. At last we aim to adapt information from photographs to paintings by means of style transfer.

**Object and scene recognition in paintings.** Computer based painting analysis has been in the focus of the computer vision community for a long period. A summary of various directions approached, algorithms and results for not-so-recent solutions can be found in the review of Bentowska and Coddington [-@Bentowska2010]. However, a plurality of 