Orientation judgment for abstract paintings

Abstract

Artists decide the orientation at which an abstract painting should be hung based on their ideas, but the correct orientation is not obvious to other viewers. Some studies have found that abstract paintings at the correct orientations generally get higher aesthetic ratings from viewers. This encourages us to deal with the problem of orientation judgment for abstract paintings through machine learning. First, we design a group of methods to extract features from paintings based on the theories in abstract art. Then a machine leaning framework is proposed using Naive Bayes classifier and BP neural network classifier for training and orientation testing. Experiments show that it can classify abstract paintings into up and non-up ones with performance comparable to human. This is the first work of orientation judgment for abstract paintings through computer simulation, and the results demonstrate the validity of abstract art theories used for feature definition. This work provides a new scheme for exploring the relationship between aesthetic quality of abstract paintings and their computational visual features.

Relations Between Balance, Prototypicality, and Aesthetic Appreciation for Japanese Calligraphy

“…For answering the first question, we inspected the order of pictures with respect to balance. Our impression was that perceived stability (Liu et al., 2017; van der Helm, 2015) might explain some of the variance. Therefore, in our second experiment, we asked our participants to assess the stability of the calligraphies.…”

“…In this experiment, we collected ratings of stability (Liu et al., 2017; van der Helm, 2015) as well as of prototypicality (Rosch, 1975) for the Japanese calligraphies already used in our first experiment. Stability ratings were used to examine to what extent this dimension corresponds to the concept of perceptual balance for the calligraphies.…”

“…It seemed to us that the stimuli assessed as less balanced show some kind of instability and flexibility, whereas calligraphies rated as more balanced look more stable and rigid. In art theory, stability is considered as a visual and aesthetic habit that plays an important role in composing pictures (Liu et al., 2017). This habit is formed in individuals through their lifetime by living with gravitation, which forces them to arrange things in a stable way.…”

“…This suggests that the concept of balance for Japanese calligraphies differs from that reflected by the measures. To investigate these issues further, we conducted a second experiment in which we examined perceived “stability” (Liu, Dong, Zhang, & Jiang, 2017) as an alternative concept of balance.…”

A deep-learning framework for human perception of abstract art composition

“…The exact mechanisms underlying orientation judgements are not fully understood. Some authors have suggested that the perception of orientation depends more on low-level stimulus properties than higher level object recognition and/or image interpretation ( Lindauer, 1987 ), prompting others to investigate the potential role of relatively simple cues, such as Fourier amplitude spectrum slope ( Mather, 2012 ), or image statistics based on explicit rules gathered from several art theories incorporated into a machine learning algorithm ( Liu et al., 2017 ) (see Elgammal et al., 2018 ; Rodriguez et al., 2018 for related applications).…”