论文标题
通过使用Ensemble探索图像的美学得分预测基于CNN的模型
Exploring CNN-based models for image's aesthetic score prediction with using ensemble
论文作者
论文摘要
在本文中，我们提出了一个框架，该框架是构建具有不同CNN体系结构的两种类型的自动图像美学评估模型，并通过整体来改善图像的美学得分预测。此外，提取了模型对图像的注意区域，以分析与图像中受试者的一致性。实验结果验证了所提出的方法有效地改善了AS预测。此外，发现在XIHEAA数据集上训练的分类模型似乎学习了潜在的摄影原理，尽管不能说他们学习了美学意义。
In this paper, we proposed a framework of constructing two types of the automatic image aesthetics assessment models with different CNN architectures and improving the performance of the image's aesthetic score prediction by the ensemble. Moreover, the attention regions of the models to the images are extracted to analyze the consistency with the subjects in the images. The experimental results verify that the proposed method is effective for improving the AS prediction. Moreover, it is found that the AS classification models trained on XiheAA dataset seem to learn the latent photography principles, although it can't be said that they learn the aesthetic sense.
