Aesthetic perception of visual textures: a holistic exploration using texture analysis, psychological experiment, and perception modeling

Abstract

Modeling human aesthetic perception of visual textures is important and valuable in numerous industrial domains, such as product design, architectural design, and decoration. Based on results from a semantic differential rating experiment, we modeled the relationship between low-level basic texture features and aesthetic properties involved in human aesthetic texture perception. First, we compute basic texture features from textural images using four classical methods. These features are neutral, objective, and independent of the socio-cultural context of the visual textures. Then, we conduct a semantic differential rating experiment to collect from evaluators their aesthetic perceptions of selected textural stimuli. In semantic differential rating experiment, eights pairs of aesthetic properties are chosen, which are strongly related to the socio-cultural context of the selected textures and to human emotions. They are easily understood and connected to everyday life. We propose a hierarchical feed-forward layer model of aesthetic texture perception and assign 8 pairs of aesthetic properties to different layers. Finally, we describe the generation of multiple linear and non-linear regression models for aesthetic prediction by taking dimensionality-reduced texture features and aesthetic properties of visual textures as dependent and independent variables, respectively. Our experimental results indicate that the relationships between each layer and its neighbors in the hierarchical feed-forward layer model of aesthetic texture perception can be fitted well by linear functions, and the models thus generated can successfully bridge the gap between computational texture features and aesthetic texture properties.

Aesthetics by Numbers: Links between Perceived Texture Qualities and Computed Visual Texture Properties

“…Our present study differs in a number of ways from some of the earlier work from the Syntex-consortium (Thumfart et al, 2008 , 2011 ; Liu et al, 2015 ). First, we opted for a semantic differential approach in which—based on a factor analysis of a larger number of judgments—we select a small number of judgments that best represent the observers’ judgment space, rather than a priori assigning judgments to different “cognitive layers” (Thumfart et al, 2008 , 2011 ; Liu et al, 2015 ). Second, we also used factor analysis for selecting the relevant computational features (rather than the Laplacian Score employed by Liu et al, 2015 ).…”

“…Despite this widespread use, until recently there have been relatively few systematic attempts to reveal systematic relationships between such perceived aesthetic qualities and the texture’s computed visual features. The Syntex project and its derivatives also addressed the impact of visual textures on aesthetic experiences in a number of previous publications (Thumfart et al, 2008 , 2011 ; Liu et al, 2015 ).…”

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

“…In this section, we just briefly discuss four algorithms to calculate low-level textual features to describe the statistical properties of visual texture. A more elaborate description of these four methods has been provided in a previous study [ 17 – 19 ]. The average of the hue saturation value color matrix elements was calculated after the texture images were transformed into HSV from the RGB space.…”

“…We determined that the majority of the built models are nonlinear models, except for Equation 5 . We use more nonlinear terms for model building when compared with a previous study [ 17 , 18 ], although linear models are sufficient to bridge the gap between level texture features and high-level aesthetic properties. When nonlinear models are selected, the prediction error will obviously decrease; however, the model complexity will sharply increase when compared with the linear model.…”

“…Thumfart et al proposed a hierarchical feed-forward model to explore the relationship between computational texture features and the aesthetic properties of visual textures using the results of a psychological experiment [ 16 ]. And some further research studies have been continued by Liu et al [ 17 , 18 ].…”