Image Feature Types and Their Predictions of Aesthetic Preference and Naturalness

Frontiers in Psychology
Frank F IbarraMarc G Berman

Abstract

Previous research has investigated ways to quantify visual information of a scene in terms of a visual processing hierarchy, i.e., making sense of visual environment by segmentation and integration of elementary sensory input. Guided by this research, studies have developed categories for low-level visual features (e.g., edges, colors), high-level visual features (scene-level entities that convey semantic information such as objects), and how models of those features predict aesthetic preference and naturalness. For example, in Kardan et al. (2015a), 52 participants provided aesthetic preference and naturalness ratings, which are used in the current study, for 307 images of mixed natural and urban content. Kardan et al. (2015a) then developed a model using low-level features to predict aesthetic preference and naturalness and could do so with high accuracy. What has yet to be explored is the ability of higher-level visual features (e.g., horizon line position relative to viewer, geometry of building distribution relative to visual access) to predict aesthetic preference and naturalness of scenes, and whether higher-level features mediate some of the association between the low-level features and aesthetic preference or naturaln...Continue Reading

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Citations

Sep 13, 2020·Attention, Perception & Psychophysics·Kathryn E SchertzMarc G Berman
Mar 16, 2019·Scientific Reports·Chanuki Illushka SeresinheHelen Susannah Moat
Feb 19, 2021·Scientific Reports·Fariba MostajeranSimone Kühn
Oct 2, 2021·Scientific Reports·Claudia DamianoWilliam A Cunningham

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Software Mentioned

Photoshop
ART
MATLAB Image Processing Toolbox
MATLAB
PROCESS Macro
SPSS
Circular Statistics Toolbox
Grasshopper
Rhinoceros

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