| Summary: | The purpose of image aesthetics assessment is to automatically predict the perceived quality of an image. Convolutional neural network (CNN) based on deep learning has been used for aesthetics assessment and has displayed potential results. Our final objective is to identify features that contribute to the estimation of aesthetic quality by using explainable AI. In this study, we focused on composition as the first step. By applying clustering to the attention maps obtained by CNN and Grad-CAM++, it was experimentally verified whether the CNN model considers composition for aesthetics assessment. In addition, we verified whether the aesthetic quality features that humans pay attention to differ according to photographic categories such as landscape or portrait.
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