Department of Mechanical, Energy and Management Engineering, University of Calabria, Cubo 43, 5th floor, Ponte P. Bucci, 87036 Arcavacata di Rende, CS, Italy; Laboratory of Cognitive Science and Mathematical Modelling, Department of Physics, University of Calabria, Cubo 17b, 6th floor, Ponte P. Bucci, 87036 Arcavacata di Rende, CS, Italy.
Laboratory of Cognitive Science and Mathematical Modelling, Department of Physics, University of Calabria, Cubo 17b, 6th floor, Ponte P. Bucci, 87036 Arcavacata di Rende, CS, Italy; Department of Physics, University of Calabria, Cubo 31, 6th floor, Ponte P. Bucci, 87036 Arcavacata di Rende, CS, Italy.
Acta Psychol (Amst). 2022 Apr;224:103530. doi: 10.1016/j.actpsy.2022.103530. Epub 2022 Feb 12.
Aesthetics and evaluation of objects is becoming increasingly important in contemporary society. Although there have been many studies on processes related to computational aesthetic, a clear formalisation and visualization of the aesthetic field is still lacking. In this paper, we present a set of Machine Learning techniques and mathematical methods to extract the most important features related to aesthetical evaluation, thus making this process automatic, without the human intervention. The techniques are then applied to a sample of 83 images of triangles, produced by artists. The results of the empirical method provide a series of measurements that allow the extrapolation of mathematical aesthetic characteristics of the images and their location in the aesthetic space.
在当代社会，对物体的美学和评价变得越来越重要。尽管已经有许多关于与计算美学相关的过程的研究，但美学领域的明确形式化和可视化仍然缺乏。在本文中，我们提出了一组机器学习技术和数学方法，以提取与审美评价相关的最重要特征，从而使这个过程自动进行，无需人为干预。然后，这些技术被应用于一组由艺术家创作的 83 个三角形图像的样本。实证方法的结果提供了一系列测量值，这些测量值允许推断图像的数学美学特征及其在美学空间中的位置。
