Unlocking the secrets of scenic beauty: a quantitative analysis of object variety and connections in scenic images

The perception of beauty, though often subjective, is influenced by identifiable structural and spatial patterns that shape how individuals experience their surroundings. This study explores the roles of object variety and connections in scenic images in shaping perceptions of environmental aesthetics, using advanced computer vision techniques and regression analysis. Drawing on data from the Scenic-Or-Not project and leveraging the Segment Anything Model, we analysed landscape photographs to understand how object diversity and spatial arrangement affect aesthetic judgments. Our findings reveal a positive correlation between object diversity and perceived scenicness, emphasizing the importance of visual richness and complexity in enhancing scenic appeal. However, excessive object diversity can introduce visual clutter and diminish aesthetic value. Our analysis of object connections, measured through graph-based metrics like network density and clustering coefficient, reveals that denser and more interconnected arrangements enhance scenic appeal, while overly efficient local connections reduce visual interest. These results demonstrate the importance of balancing complexity, coherence and interconnectedness in scenic design. By situating these findings within established theoretical frameworks, this study provides insights for disciplines such as environmental science, urban planning and landscape management, offering guidance for creating environments that evoke positive aesthetic experiences while maintaining visual harmony and interest.

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Institutions

Publication Details

Journal
Journal of The Royal Society Interface
Published
2025-04-01
DOI
https://doi.org/10.1098/rsif.2025.0045
Citations
2
Primary Topic
Urban Green Space and Health
Type
article
Field-Weighted Citation Impact
2.40

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article

Unlocking the secrets of scenic beauty: a quantitative analysis of object variety and connections in scenic images

Junjun Yin, Guangqing Chi, Bin Jiang
2 citations
Journal of The Royal Society Interface
Urban Green Space and Health
2.40
article

Unlocking the secrets of scenic beauty: a quantitative analysis of object variety and connections in scenic images

Junjun Yin, Guangqing Chi, Bin Jiang
article en
2 citations

Abstract

The perception of beauty, though often subjective, is influenced by identifiable structural and spatial patterns that shape how individuals experience their surroundings. This study explores the roles of object variety and connections in scenic images in shaping perceptions of environmental aesthetics, using advanced computer vision techniques and regression analysis. Drawing on data from the Scenic-Or-Not project and leveraging the Segment Anything Model, we analysed landscape photographs to understand how object diversity and spatial arrangement affect aesthetic judgments. Our findings reveal a positive correlation between object diversity and perceived scenicness, emphasizing the importance of visual richness and complexity in enhancing scenic appeal. However, excessive object diversity can introduce visual clutter and diminish aesthetic value. Our analysis of object connections, measured through graph-based metrics like network density and clustering coefficient, reveals that denser and more interconnected arrangements enhance scenic appeal, while overly efficient local connections reduce visual interest. These results demonstrate the importance of balancing complexity, coherence and interconnectedness in scenic design. By situating these findings within established theoretical frameworks, this study provides insights for disciplines such as environmental science, urban planning and landscape management, offering guidance for creating environments that evoke positive aesthetic experiences while maintaining visual harmony and interest.

Journal of The Royal Society InterfaceVol. 22(225)
Pennsylvania State University (US), Hong Kong University of Science and Technology (HK), Agricultural & Applied Economics Association (US), University of Hong Kong (HK)
National Science Foundation, U.S. Department of Agriculture, Hong Kong University of Science and Technology, National Institute of Food and Agriculture, National Institute of Child Health and Human Development, Eunice Kennedy Shriver National Institute of Child Health and Human Development
Sustainable cities and communities
Openalex Percentile: Top 14%
Urban Green Space and Health
2.40
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