Study on relationship between composition and prediction of photo aesthetics using CNN
Study on relationship between composition and prediction of photo aesthetics using CNN
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DOI：
10.1080/23311916.2022.2107472
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发表时间：
2022-12-31
影响因子：
1.9
通讯作者：
Kanagawa, Akihiro
中科院分区：
文献类型：
--
作者：
Sakaguchi, Daichi;Takimoto, Hironori;Kanagawa, Akihiro
图像美学评价的目的是自动预测图像的感知质量。基于深度学习的卷积神经网络(CNN)已被用于美学评价，并显示出潜在的效果。我们的最终目标是通过使用可解释的人工智能来识别有助于估计审美质量的特征。在这项研究中，我们把写作作为第一步。通过对CNN和Grad-CAM++得到的注意图进行聚类，实验验证了CNN模型是否将构图作为美学评价的考虑因素。此外，我们还验证了人类关注的审美品质特征是否根据风景或肖像等摄影类别的不同而有所不同。
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.