Modeling human aesthetic perception of visual textures

Abstract

Texture is extensively used in areas such as product design and architecture to convey specific aesthetic information. Using the results of a psychological experiment, we model the relationship between computational texture features and aesthetic properties of visual textures. Contrary to previous approaches, we build a layered model, which provides insights into hierarchical relationships involved in human aesthetic texture perception. This model uses a set of intermediate judgements to link computational texture features with aesthetic texture properties. We pursue two different approaches for modeling. (1) Supervised machine-learning methods are used to generate linear and nonlinear models from the experimental data automatically. The quality of these models is discussed, mainly focusing on interpretability and accuracy. (2) We apply a psychological-based approach that models the processing pathways in human perception of naturalness, introducing judgement dimensions (principal components) mediating the relationship between texture features and naturalness judgements. This multiple mediator model serves as a verification of the machine-learning approach. We conclude with a comparison of these two approaches, highlighting the similarities and discrepancies in terms of identified relationships between computational texture features and aesthetic properties of visual textures.