[@Zhou:14]. We will refer the reader to a recent work [@Heranz2016] for the latest results on the topic. We will still note that the introduction of the SUN database [@xiao2010sun] (followed by the subsequent expansions) placed a significant landmark (and benchmark) on the issue. In this context, it was shown that using domain transfer (e.g from the Places database), the performance may be improved [@Zhou:14].

**Style transfer.** Any image claiming artistic representations adheres to some non-trivial ideas about content, color and composition; these ideas are often grouped in *styles*. It has been an important theme in computer vision [@bae2006] to develop methods that are able to transfer the style from an artistic image to a normal, common image. The methods are called *style transfer*. Currently, the methods may be classified as non-neural (such as the Laplacian style transfer [@Aubry2014]) and neural artistic style transfers, initiated by the method of Gatys et al. [-@Gatys:2015]. We will detail these algorithms in section \[Sect:PaintingUnderstanding\]. For the moment, we will note that the efficiency of the transfer was quantified, till now, only subjectively as that it was not rigourously defined the concept of the style so to evaluate objectively.

**Scene recognition by humans.** While 