A Machine Learning Paradigm for Studying Pictorial Realism: How Accurate are Constable's Clouds?
A Machine Learning Paradigm for Studying Pictorial Realism: How Accurate are Constable's Clouds?
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DOI：
10.1109/tpami.2023.3324743
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发表时间：
2022-02
影响因子：
23.6
通讯作者：
Zhuomin Zhang;Elizabeth C. Mansfield;Jia Li;John Russell;George S. Young;Catherine Adams;Kevin A Bowley;James Z. Wang
中科院分区：
文献类型：
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作者：
Zhuomin Zhang;Elizabeth C. Mansfield;Jia Li;John Russell;George S. Young;Catherine Adams;Kevin A Bowley;James Z. Wang
英国风景画家约翰·康斯特布尔被认为是19世纪欧洲绘画现实主义运动的奠基人。尤其是康斯特布尔绘制的天空，在他的同时代人看来是非常准确的，今天的许多观众也有同样的印象。然而，评估康斯特布尔这样的现实主义绘画的准确性是主观或直观的，即使对专业的艺术历史学家来说也是如此，这使得人们很难肯定地说康斯特布尔的天空与他同时代的人有什么不同。我们的目标是有助于更客观地理解康斯特布尔的现实主义。我们提出了一种基于机器学习的新范式，用于以可解释的方式研究绘画现实主义。我们的框架通过衡量以天空著称的艺术家(如康斯特布尔)绘制的云与云的照片之间的相似性来评估现实主义。云分类的实验结果表明，康斯特布尔比他的同时代人更接近于他绘画中实际云的形式特征。这项研究作为一种新的跨学科方法，结合了计算机视觉和机器学习、气象学和艺术史，是对绘画现实主义进行更广泛和更深入分析的跳板。
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.