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arXiv · 2408.01481

Using a CNN Model to Assess Paintings' Creativity

Zhehan Zhang·Meihua Qian·Li Luo·Qianyi Gao·Xianyong Wang·Ripon Saha·Xinxin Song
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Abstract

Assessing artistic creativity has long challenged researchers, with traditional methods proving time-consuming. Recent studies have applied machine learning to evaluate creativity in drawings, but not paintings. Our research addresses this gap by developing a CNN model to automatically assess the creativity of human paintings. Using a dataset of six hundred paintings by professionals and children, our model achieved 90% accuracy and faster evaluation times than human raters. This approach demonstrates the potential of machine learning in advancing artistic creativity assessment, offering a more efficient alternative to traditional methods.

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Zhehan Zhang, Meihua Qian, Li Luo, Qianyi Gao, Xianyong Wang, Ripon Saha, Xinxin Song. 2024-08-02. Using a CNN Model to Assess Paintings' Creativity. https://arxiv.org/abs/2408.01481

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