Techniques of the Art Historical Observer (Article)
Title: Techniques of the Art Historical Observer
Author: Elizabeth Mansfield
Author: Zhuomin Zhang
Author: Jia Li
Author: John Russell
Author: George S. Young
Author: Catherine Adams
Author: James Z. Wang
Abstract: Computer vision has proven a useful tool for the technical analysis of art, yielding valuable information about condition, artistic processes, and materials, as well as insights into provenance and authenticity. When it comes to art historical interpretation—i.e., the diverse practices used to elicit historical meaning from works of visual or material culture—computer vision has been applied more tentatively, at least by art historians. This essay attempts to account for this hesitancy by providing an account of the Seeing Constable’s Clouds project from the perspective of one member of the research team, the art historian.
Year: 2022
Primary URL: https://www.19thc-artworldwide.org/spring22/practicing-art-history-techniques-of-the-art-historical-observer
Primary URL Description: Link to article in Nineteenth-Century Art Worldwide
Access Model: open access
Format: Journal
Periodical Title: Nineteenth-Century Art Worldwide
Publisher: Association of Historians of Nineteenth-Century Art

A Machine Learning Paradigm for Studying Pictorial Realism: Are Constable's Clouds More Real than His Contemporaries? (Database/Archive/Digital Edition)
Title: A Machine Learning Paradigm for Studying Pictorial Realism: Are Constable's Clouds More Real than His Contemporaries?
Author: Zhuomin Zhang
Author: Elizabeth Mansfield
Author: Jia Li
Author: John Russell
Author: George S. Young
Author: Catherine Adams
Author: James Z. Wang
Abstract: 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.
Year: 2022
Primary URL: https://arxiv.org/abs/2202.09348
Primary URL Description: Link to arXiv where draft article has been deposited.
Access Model: open access

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