Can we detect harmony in artistic compositions? A machine learning approach

TL;DR

This study explores whether harmony in artistic visual compositions can be quantified using machine learning by correlating human ratings with specially designed features, achieving 80% accuracy in classification.

Contribution

The paper introduces a novel set of features for representing artistic harmony and demonstrates that machine learning models can effectively classify compositions based on perceived harmony.

Findings

SVM achieved 80% accuracy in harmony classification

Designed features correlate with human harmony ratings

Harmony can be approximated mathematically in visual art

Abstract

Harmony in visual compositions is a concept that cannot be defined or easily expressed mathematically, even by humans. The goal of the research described in this paper was to find a numerical representation of artistic compositions with different levels of harmony. We ask humans to rate a collection of grayscale images based on the harmony they convey. To represent the images, a set of special features were designed and extracted. By doing so, it became possible to assign objective measures to subjectively judged compositions. Given the ratings and the extracted features, we utilized machine learning algorithms to evaluate the efficiency of such representations in a harmony classification problem. The best performing model (SVM) achieved 80% accuracy in distinguishing between harmonic and disharmonic images, which reinforces the assumption that concept of harmony can be expressed in a mathematical way that can be assessed by humans.