A Machine Learning Paradigm for Studying Pictorial Realism: Are Constable's Clouds More Real than His Contemporaries?
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European artists have sought to create life-like images since the Renaissance. The techniques used by artists to impart realism to their paintings often rely on approaches based in mathematics, like linear perspective; yet the means used to assess the verisimilitude of realist paintings have remained subjective, even intuitive. An exploration of alternative and relatively objective methods for evaluating pictorial realism could enhance existing art historical research. We propose a machine-learning-based paradigm for studying pictorial realism in an explainable way. Unlike subjective evaluations made by art historians or computer-based painting analysis exploiting inexplicable learned features, our framework assesses realism by measuring the similarity between clouds painted by exceptionally skillful 19th-century landscape painters like John 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. Our analyses suggest that artists working in the decades leading up to the invention of photography worked in a mode that anticipated some of the stylistic features of photography. The study is a springboard for deeper analyses of pictorial realism using computer vision and machine learning.
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