Unlocking the secrets of scenic beauty: a quantitative analysis of object variety and connections in scenic images
Year: 2025
Type: article
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.
Authors: Junjun Yin, Guangqing Chi, Bin Jiang
Language: English
Subfield: Cognitive Neuroscience
Field: Neuroscience
Domain: Life Sciences
Open Access status: bronze
Funders: National Science Foundation, U.S. Department of Agriculture, Hong Kong University of Science and Technology, National Institutes of Health, 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
Awards: RR: The Generalizability and Replicability of Twitter Data for Population Research, G2546232660, G28523178, RAPID: Collaborative Research: COVID-19 Preparedness in Remote Fishing Communities in Rural Alaska, NNA Research: Collaborative Research: Arctic, Climate, and Earthquakes (ACE): Seismic Resilience and Adaptation of Arctic Infrastructure and Social Systems amid Changing Climate, G360815713, NNA Track 1: Pursuing Opportunities for Long-term Arctic Resilience for Infrastructure and Society (POLARIS), EAGER: SAI: Collaborative Research: Community-Driven Innovation for Resilient Bridges in Remote Communities, The Population Research Institute, G5284736384, G5850161134, RAPID: Using Mobile Phone Data to Understand the Impacts of the COVID-19 Pandemic on Food Assistance Use in Alaska, G7110277825, G7555060587
OpenAlex ID: W4409088601