  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.
