it is beyond the purpose of this paper to discuss detailed aspects of the human neuro-mechanisms involved in scene recognition, following the integrating work of Sewards [-@Sewards2011] we stress only one important aspect: compared to object recognition, where localized structures are used, for scene recognition the process is significantly more tedious and complex. Object recognition “is solved in the brain via a cascade of reflexive, largely feedforward computations that culminate in a powerful neuronal representation in the inferior temporal cortex” [@DiCarlo12]. In contrast, scene recognition includes numerous and complex areas, as the process starts with peripheral object recognition, continues with central object recognition, activating areas such as the entorhinal cortex, hippocampus and subiculum [@Sewards2011].

Concluding, there is a consensus, from both the neuro-science and the computer vision communities that scene recognition is a particularly difficult task. This task becomes even harder when the subject images are heavily abstracted paintings produced within the movements of modern art.

CNNs: Architectures and Training {#Sect:CNN}
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Following AlexNet [@Krizhevsky:12] performance in the ImageNet challenge, in the recent years we have witnessed a steep increase in popularity of Convolutional Neural Networks (CNNs) when considering the task of image classification. Especially after Donahue et al. [-@donahue2014] 