  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).
