Unveiling The Factors of Aesthetic Preferences with Explainable AI
Derya Soydaner Johan Wagemans
The allure of aesthetic appeal in images captivates our senses, yet the
underlying intricacies of aesthetic preferences remain elusive. In this study,
we pioneer a novel perspective by utilizing machine learning models that focus
on aesthetic attributes known to influence preferences. Through a data mining
approach, our models process these attributes as inputs to predict the
aesthetic scores of images. Moreover, to delve deeper and obtain interpretable
explanations regarding the factors driving aesthetic preferences, we utilize
the popular Explainable AI (XAI) technique known as SHapley Additive
exPlanations (SHAP). Our methodology involves employing various machine
learning models, including Random Forest, XGBoost, Support Vector Regression,
and Multilayer Perceptron, to compare their performances in accurately
predicting aesthetic scores, and consistently observing results in conjunction
with SHAP. We conduct experiments on three image aesthetic benchmarks,
providing insights into the roles of attributes and their interactions.
Ultimately, our study aims to shed light on the complex nature of aesthetic
preferences in images through machine learning and provides a deeper
understanding of the attributes that influence aesthetic judgements.