Recursive Segmentation Living Image: An eXplainable AI (XAI) Approach for Computing Structural Beauty of Images or the Livingness of Space

• URL: http://arxiv.org/abs/2310.10149v2
• Date: Tue, 7 Nov 2023 15:40:25 GMT
• Title: Recursive Segmentation Living Image: An eXplainable AI (XAI) Approach for Computing Structural Beauty of Images or the Livingness of Space
• Authors: Yao Qianxiang and Bin Jiang
• Abstract summary: This study introduces the concept of "structural beauty" as an objective computational approach for evaluating the aesthetic appeal of images. The application of our method to the Scenic or Not dataset, a repository of subjective scenic ratings, demonstrates a high degree of consistency with subjective ratings in the 0-6 score range. Our method not only provides computational results but also offers transparency and interpretability, positioning it as a novel avenue in the realm of Explainable AI (XAI)
• Score: 4.959120401369489
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: This study introduces the concept of "structural beauty" as an objective computational approach for evaluating the aesthetic appeal of images. Through the utilization of the Segment anything model (SAM), we propose a method that leverages recursive segmentation to extract finer-grained substructures. Additionally, by reconstructing the hierarchical structure, we obtain a more accurate representation of substructure quantity and hierarchy. This approach reproduces and extends our previous research, allowing for the simultaneous assessment of Livingness in full-color images without the need for grayscale conversion or separate computations for foreground and background Livingness. Furthermore, the application of our method to the Scenic or Not dataset, a repository of subjective scenic ratings, demonstrates a high degree of consistency with subjective ratings in the 0-6 score range. This underscores that structural beauty is not solely a subjective perception, but a quantifiable attribute accessible through objective computation. Through our case studies, we have arrived at three significant conclusions. 1) our method demonstrates the capability to accurately segment meaningful objects, including trees, buildings, and windows, as well as abstract substructures within paintings. 2) we observed that the clarity of an image impacts our computational results; clearer images tend to yield higher Livingness scores. However, for equally blurry images, Livingness does not exhibit a significant reduction, aligning with human visual perception. 3) our approach fundamentally differs from methods employing Convolutional Neural Networks (CNNs) for predicting image scores. Our method not only provides computational results but also offers transparency and interpretability, positioning it as a novel avenue in the realm of Explainable AI (XAI).

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