Paper ID: 2408.01481

Using a CNN Model to Assess Visual Artwork's Creativity

Zhehan Zhang, Meihua Qian, Li Luo, Ripon Saha, Qianyi Gao, Xinxin Song

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.

Submitted: Aug 2, 2024