Using a CNN Model to Assess Visual Artwork's Creativity

• URL: http://arxiv.org/abs/2408.01481v2
• Date: Fri, 16 Aug 2024 17:52:34 GMT
• Title: Using a CNN Model to Assess Visual Artwork's Creativity
• Authors: Zhehan Zhang, Meihua Qian, Li Luo, Ripon Saha, Qianyi Gao, Xinxin Song,
• Abstract summary: We develop 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.
• Score: 0.5926480964767554
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• 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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