Evolution of Entropy in Art Painting Based on the Wavelet Transform.

Hongyi Yang,Han Yang

doi:10.3390/e23070883

Abstract

Quantitative studies of art and aesthetics are representative of interdisciplinary research. In this work, we conducted a large-scale quantitative study of 36,000 paintings covering both Eastern and Western paintings. The information entropy and wavelet entropy of the images were calculated based on their complexity and energy. Wavelet energy entropy is a feature that can characterize rich information in images, and this is the first study to introduce this feature into aesthetic analysis of art paintings. This study shows that the process of entropy change coincides with the development process of art painting. Further, the experimental results demonstrate an important change in the evolution of art painting, and since the rise of modern art in the twentieth century, the entropy values in painting have started to become diverse. In comparison with Western paintings, Eastern paintings have distinct low entropy characteristics in which the wavelet entropy feature of the images has better results in the machine learning classification task of Eastern and Western paintings (i.e., the F1 score can reach 97%). Our study can be the basis for future quantitative analysis and comparative research in the context of Western and Eastern art aesthetics.

Highlights

For a long time, aesthetics has been regarded as a philosophical or psychological field
With the rapid development of computer technology, concepts related to computational aesthetics have been proposed in the context of computer science [1], where researchers hope that computers can learn and simulate human visual and aesthetic habits to quantitatively analyze paintings [2,3], literature [4,5], and music [6,7]
Among them, painting is an important part of art history and an object of study for computational aesthetics in the visual field

Summary

Introduction

Aesthetics has been regarded as a philosophical or psychological field. With the rapid development of computer technology, concepts related to computational aesthetics have been proposed in the context of computer science [1], where researchers hope that computers can learn and simulate human visual and aesthetic habits to quantitatively analyze paintings [2,3], literature [4,5], and music [6,7]. Among them, painting is an important part of art history and an object of study for computational aesthetics in the visual field. Quantitative studies of paintings can provide auxiliary information for the appreciation of art [8,9,10] and enable machines to learn human perceptual behaviors for imitative creation [11,12]. The German aesthete Fechner first introduced experimental psychology to study aesthetics in 1876, and the field of experimental aesthetics as a study was created [13]. Computational aesthetics can be traced back to the American mathematician Birkhoff’s book Aesthetic Measure, which was published in 1933 [14]

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