  With the increasing availability of large digitized fine art collections,
automated analysis and classification of paintings is becoming an interesting
area of research. However, due to domain specificity, implicit subjectivity,
and pervasive nuances that vaguely separate art movements, analyzing art using
machine learning techniques poses significant challenges. Residual networks, or
variants thereof, are one the most popular tools for image classification
tasks, which can extract relevant features for well-defined classes. In this
case study, we focus on the classification of a selected painting 'Portrait of
the Painter Charles Bruni' by Johann Kupetzky and the analysis of the
performance of the proposed classifier. We show that the features extracted
during residual network training can be useful for image retrieval within
search systems in online art collections.
