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.

翻译：图像的美学魅力能吸引我们的感官，但美学偏好背后的复杂机制仍难以捉摸。本研究开创性地采用聚焦已知影响偏好的美学属性的机器学习模型，通过数据挖掘方法将这些属性作为输入来预测图像美学评分。为深入探究驱动美学偏好的因素并获得可解释性说明，我们采用流行的可解释人工智能（XAI）技术——Shapley加法解释（SHAP）。研究方法包括应用随机森林、XGBoost、支持向量回归与多层感知器等多种机器学习模型，比较其在准确预测美学评分方面的性能，并与SHAP结合进行一致性观测。我们在三个图像美学基准数据集上进行实验，揭示了属性及其交互作用的影响机制。本研究旨在通过机器学习阐明图像美学偏好的复杂本质，深化对影响美学判断属性的理解。