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揭开风景之美的秘密:对风景图像中物体多样性和关联性的定量分析。

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

作者信息

Yin Junjun, Chi Guangqing, Jiang Bin

机构信息

Population Research Institute and Social Science Research Institute, Pennsylvania State University, University Park, PA, USA.

Department of Agricultural Economics, Sociology, and Education, Pennsylvania State University, University Park, PA, USA.

出版信息

J R Soc Interface. 2025 Apr;22(225):20250045. doi: 10.1098/rsif.2025.0045. Epub 2025 Apr 2.

DOI:10.1098/rsif.2025.0045
PMID:40169026
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC11961261/
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.

摘要

对美的感知虽然往往是主观的,但会受到可识别的结构和空间模式的影响,这些模式塑造了个体体验周围环境的方式。本研究利用先进的计算机视觉技术和回归分析,探讨风景图像中物体多样性和连接性在塑造环境美学感知方面的作用。基于“风景与否”项目的数据并借助“分割一切模型”,我们分析了风景照片,以了解物体多样性和空间布局如何影响审美判断。我们的研究结果揭示了物体多样性与感知到的风景优美度之间存在正相关,强调了视觉丰富性和复杂性在增强风景吸引力方面的重要性。然而,过多的物体多样性会导致视觉混乱并降低审美价值。我们通过基于图的指标(如网络密度和聚类系数)对物体连接性进行分析,结果表明更密集、连接更紧密的布局会增强风景吸引力,而过于高效的局部连接会降低视觉趣味性。这些结果证明了在风景设计中平衡复杂性、连贯性和相互连接性的重要性。通过将这些研究结果置于既定的理论框架内,本研究为环境科学、城市规划和景观管理等学科提供了见解,为创造能唤起积极审美体验同时保持视觉和谐与趣味性的环境提供了指导。

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本文引用的文献

1
Structural Beauty: A Structure-Based Computational Approach to Quantifying the Beauty of an Image.结构之美:一种基于结构的计算方法用于量化图像之美。
J Imaging. 2021 Apr 23;7(5):78. doi: 10.3390/jimaging7050078.
2
Image Segmentation Using Deep Learning: A Survey.基于深度学习的图像分割技术综述。
IEEE Trans Pattern Anal Mach Intell. 2022 Jul;44(7):3523-3542. doi: 10.1109/TPAMI.2021.3059968. Epub 2022 Jun 3.
3
Happiness is Greater in More Scenic Locations.风景优美的地方会让人更快乐。
Sci Rep. 2019 Mar 14;9(1):4498. doi: 10.1038/s41598-019-40854-6.
4
Using deep learning to quantify the beauty of outdoor places.利用深度学习量化户外场所的美感。
R Soc Open Sci. 2017 Jul 19;4(7):170170. doi: 10.1098/rsos.170170. eCollection 2017 Jul.
5
Quantifying the Impact of Scenic Environments on Health.量化风景环境对健康的影响。
Sci Rep. 2015 Nov 25;5:16899. doi: 10.1038/srep16899.