Modeling Human Aesthetic Perception of Visual Textures

Stefan Thumfart*, Richard H. A. H. Jacobs, Edwin Lughofer, Christian Eitzinger, Frans W. Cornelissen, Werner Groissboeck, Roland Richter

*Corresponding author for this work

    Research output: Contribution to journalArticleAcademicpeer-review

    24 Citations (Scopus)

    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.

    Original languageEnglish
    Article number27
    Number of pages29
    JournalAcm transactions on applied perception
    Volume8
    Issue number4
    DOIs
    Publication statusPublished - Nov-2011

    Keywords

    • Algorithms
    • Experimentation
    • Human Factors
    • Texture analysis
    • human perception
    • modeling
    • machine learning
    • mediator analysis
    • human judgments
    • aesthetic space
    • IMAGE STATISTICS
    • DISCRIMINATION
    • CLASSIFICATION
    • FEATURES
    • RETRIEVAL
    • FILTERS
    • SYSTEM

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