ARTxAI: Explainable Artificial Intelligence Curates Deep Representation
  Learning for Artistic Images using Fuzzy Techniques
Javier Fumanal-Idocin Javier Andreu-Perez Oscar Cordón Hani Hagras Humberto Bustince
Automatic art analysis employs different image processing techniques to
classify and categorize works of art. When working with artistic images, we
need to take into account further considerations compared to classical image
processing. This is because such artistic paintings change drastically
depending on the author, the scene depicted, and their artistic style. This can
result in features that perform very well in a given task but do not grasp the
whole of the visual and symbolic information contained in a painting. In this
paper, we show how the features obtained from different tasks in artistic image
classification are suitable to solve other ones of similar nature. We present
different methods to improve the generalization capabilities and performance of
artistic classification systems. Furthermore, we propose an explainable
artificial intelligence method to map known visual traits of an image with the
features used by the deep learning model considering fuzzy rules. These rules
show the patterns and variables that are relevant to solve each task and how
effective is each of the patterns found. Our results show that our proposed
context-aware features can achieve up to $6\%$ and $26\%$ more accurate results
than other context- and non-context-aware solutions, respectively, depending on
the specific task. We also show that some of the features used by these models
can be more clearly correlated to visual traits in the original image than
others.