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Automatic art analysis employs different image processing techniques toclassify and categorize works of art. When working with artistic images, weneed to take into account further considerations compared to classical imageprocessing. This is because such artistic paintings change drasticallydepending on the author, the scene depicted, and their artistic style. This canresult in features that perform very well in a given task but do not grasp thewhole of the visual and symbolic information contained in a painting. In thispaper, we show how the features obtained from different tasks in artistic imageclassification are suitable to solve other ones of similar nature. We presentdifferent methods to improve the generalization capabilities and performance ofartistic classification systems. Furthermore, we propose an explainableartificial intelligence method to map known visual traits of an image with thefeatures used by the deep learning model considering fuzzy rules. These rulesshow the patterns and variables that are relevant to solve each task and howeffective is each of the patterns found. Our results show that our proposedcontext-aware features can achieve up to $6\%$ and $26\%$ more accurate resultsthan other context- and non-context-aware solutions, respectively, depending onthe specific task. We also show that some of the features used by these modelscan be more clearly correlated to visual traits in the original image thanothers.

ARTxAI: Explainable Artificial Intelligence Curates Deep Representation Learning for Artistic Images using Fuzzy Techniques