works [@Agarwal2015; @Karayev2014; @Bar2015; @florea:17] addressed style (art movement) recognition as it is the main label associated with paintings. An intermediate topic is in the work of Monroy et al. [-@Monroy2014] which detected and extracted shapes (associated with objects) but as pre-processing for the final task which was that of restoration.

Object recognition has been in the focus of Crowley and Zisserman [-@Crowley2016] while searching the YourPaintings dataset with learning on photographic data; yet this dataset features “older” art, which is less abstracted than modern art.

Scene recognition in paintings is also named genre recognition following the labels from the WikiArt collection. This topic was approached by Condorovici et al. [-@Condorovici2013] and by Agarwal et al. [-@Agarwal2015]; both works, using the classical feature+classifier approach, tested smaller databases with a few (5) classes: 500 images - [@Condorovici2013] and 1500 images - [@Agarwal2015]. More extensive evaluation, using data from WikiArt, was performed by Saleh and Elgammal [-@saleh2015], which investigated an extensive list of visual features and metric learning to optimize the similarity measure between paintings and respectively by Tan et al. [-@Tan:16], which employed a fine tuned AlexNet architecture [@Krizhevsky:12] to recognize both style and genre of the paintings.

The process of 