Using Image Statistics to Predict Landscape Preference
Date Issued
2015
Date
2015
Author(s)
Ho, Li-Chih
Abstract
When we see beautiful scenery, light particles enter our eyes and induce pleasant feelings. Landscape preference is the study of this phenomenon. Two paradigms are used to study landscape preference: the psychophysical paradigm and the cognitive paradigm. The psychophysical paradigm focuses on understanding the relationship between the physical attributes of the environment and landscape preference, while the cognitive paradigm focuses on understanding the relationship between the cognitive attributes of the environment and landscape preference. That being the case, what is the relationship between the light particles and landscape preference? The purpose of this research was to establish a landscape prediction model based on the visual signal computational process of the brain. Visual signals are composed of the phase spectrum and the power spectrum. The power spectrum represents the modulation of light intensity, which contains the global spatial structure of a scene. The property, known as spatial envelope property (SEP), is a type of image statistics. Because these properties are similar to factors that affect landscape preference, we therefore suppose these properties could predict landscape preferences and be used to construct a computational model of landscape preferences. The 480 images used in this research were composed of eight types of scenes including highways, tall buildings, streets, inner cities, coasts, forests, the countryside, and mountains. Principle component analysis was applied to extract the SEPs from the power spectrum of these images. Three components were extracted in this research: spatial texture, spatial direction, and spatial depth. The prediction model of landscape preference was constructed from these SEPs. We used multiple regression to test the prediction ability of the model. Two modes were used for testing: the mixed category mode, which tested all the images, and the single category mode, which tested the category of the image. The results show that the model could predict landscape preference in the mixed category mode but the R-square of the model was lower. In the single category mode, the computational model could predict landscape preference in the following three categories: highways, inner cities, and forests. Spatial texture was found to affect landscape preference in the inner city categories; whereas, spatial direction and spatial depth affect landscape preference in the highway and forest categories. However, why does spatial texture induce landscape preference? We suppose that the reason is the fractal structure of spatial texture. Because our brain can process signals composed of fractal structures easily, we feel pleasure when seeing such scenes. The relationship between spatial direction and spatial depth to landscape preference may be explained by prospect-refuge theory, because creatures could protect themselves from being eaten in an open scene. According to the relationship between spatial texture and the variation of vegetation, it could be used as the index for monitoring the environment. In addition, spatial texture could be used in a landscape perception experiment for examining the relationship between preference and distribution of spatial texture. This exploratory research aimed to understand the signal information contained in the environmental light particles, which induce our landscape preference. We found a small component of the answer but further research is required to construct the complete picture of landscape preference.
Subjects
Visual signal
Image statistics
Landscape preference
Power spectrum
Spatial envelope property
Computational model
SDGs
Type
thesis
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