The Power of Visual Texture in Aesthetic Perception: An Exploration of the Predictability of Perceived Aesthetic Emotions

Quantitative Analysis of Visual Representation of Sign Elements in COVID-19 Context

Visual representation as a means of communication uses elements to build a narrative. We propose using computer analysis to perform a quantitative analysis of the elements used in the visual creations that have been produced in reference to the epidemic, using 927 images compiled from The Covid Art Museum's Instagram account. This process has been carried out with techniques based on deep learning to detect objects contained in each study image. The research reveals the elements that are repeated in images to create narratives and the relations of association that are established in the sample. The predominant discourses in the sample do not show concern for the effects of illness. On the contrary, the impact and effects of confinement, through the prominent presence of elements such as human figures, windows, and buildings, are the most expressed experiences in the creations analyzed.

Modeling visual aesthetic perception: bridges between computed texture features and perceived beauty qualities in semantic experiments

The exploration of the potential relationship between computable low-level texture, such as features extracted from color and texture, and the perceived high-level aesthetic properties, such as warm or cold, soft or hard, has been a hot research topic of neuroaesthetics. First, the selection and clustering of aesthetic antonyms used to represent the aesthetic properties of visual texture are completed through two semantic differential experiments. Subsequently, 151 visual textures are rated according to the selected aesthetic antonyms by participants in a third semantic differential experiment. Third, 106 textural features are extracted using four different image analysis algorithms to describe the low-level characteristics of visual textures. Finally, the construction and evaluation of the visual aesthetic perception model based on multiple linear and nonlinear regression algorithms are discussed. We analyzed the frequency of each aesthetic antonym selected from 20 pairs of semantic antonyms, and the most frequently mentioned 8 pairs of semantic antonyms were selected as the core set for model building. The extracted low-level features are highly correlative. Of the correlation coefficients based on absolute values, 3383 are less than 0.75, accounting for 14.84% of the total. The correlation coefficients were larger than 0.5 accounts for 27.29% of the total. Through neighborhood component analysis, the top 10 low-level features are selected with lower correlation. The gap between low-level calculated features and high-level perceived aesthetic emotions can be bridged by a brain-inspired model of visual aesthetic perception.