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

Adam Vandor, Marie van Vollenhoven, Gerhard Weiss, Gerasimos Spanakis

Research output: Chapter in Book/Report/Conference proceedingConference article in proceedingAcademicpeer-review

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.
Original languageEnglish
Title of host publicationProceedings of the 13th International Conference on Agents and Artificial Intelligence - (Volume 2)
Subtitle of host publicationICAART 2021
EditorsAna Paula Rocha, Luc Steels, Jaap van den Herik
Pages187-195
Number of pages9
Volume2
DOIs
Publication statusPublished - 2021
Event13th International Conference on Agents and Artificial Intelligence - Vienna, Austria
Duration: 4 Feb 20216 Feb 2021

Conference

Conference13th International Conference on Agents and Artificial Intelligence
Abbreviated titleICAART 2021
Country/TerritoryAustria
CityVienna
Period4/02/216/02/21

Keywords

  • Artistic Compositions
  • Feature Extraction
  • Machine Learning

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