  The British landscape painter John Constable is considered foundational for
the Realist movement in 19th-century European painting. Constable's painted
skies, in particular, were seen as remarkably accurate by his contemporaries,
an impression shared by many viewers today. Yet, assessing the accuracy of
realist paintings like Constable's is subjective or intuitive, even for
professional art historians, making it difficult to say with certainty what set
Constable's skies apart from those of his contemporaries. Our goal is to
contribute to a more objective understanding of Constable's realism. We propose
a new machine-learning-based paradigm for studying pictorial realism in an
explainable way. Our framework assesses realism by measuring the similarity
between clouds painted by artists noted for their skies, like Constable, and
photographs of clouds. The experimental results of cloud classification show
that Constable approximates more consistently than his contemporaries the
formal features of actual clouds in his paintings. The study, as a novel
interdisciplinary approach that combines computer vision and machine learning,
meteorology, and art history, is a springboard for broader and deeper analyses
of pictorial realism.
