Can we detect harmony in artistic compositions? A machine learning approach
This research addresses the challenge of objectively quantifying subjective artistic harmony, which could benefit artists and art critics by providing a computational tool for analysis.
This paper explores the numerical representation of artistic compositions' harmony levels. By having humans rate grayscale images for harmony and extracting specially designed features, the researchers trained machine learning models to classify images. The best model, an SVM, achieved 80% accuracy in distinguishing harmonic from disharmonic images.
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.