基于语义感知的图像美学质量评估方法

当前图像美学质量评估的研究主要基于图像的视觉内容来给出评价结果，忽视了美感是人的认知活动的事实，在评价时没有考虑用户对图像语义信息的理解。为了解决这一问题，提出了一种基于语义感知的图像美学质量评估方法，将图像的物体类别信息以及场景类别信息也用于图像美学质量评估。运用迁移学习的思想，构建了一种可以融合图像多种特征的混合网络。对于每一幅输入图像，该网络可以分别提取出其物体类别特征、场景类别特征以及美学特征，并将这三种特征进行高质量的融合，以达到更好的图像美学质量评估效果。该方法在AVA数据集上的分类准确率达到89.5%，相对于传统方法平均提高了19.9%，在CUHKPQ数据集上的泛化性能也有了很大提升。实验结果表明，所提方法在图像美学质量评估问题上，能够取得更好的分类性能。

Image aesthetic quality assessment method based on semantic perception

Current researches on the assessment of image aesthetic quality are based on visual content of images to give assessment results, ignoring the fact that aesthetics is a person's cognitive activity and not considering the user's understanding towards image semantic information during the evaluating process. In order to solve this problem, an approach to image aesthetic quality assessment based on semantic perception was proposed to apply both the object category information and scene category information of images to the aesthetic quality assessment. Using the transfer learning concept, a hybrid network integrating multiple features of the images was constructed. For each input image, the object category features, scene category features, and aesthetic features were extracted respectively by network, and the three features were combined to achieve better image aesthetic quality evaluation. The classification accuracy of the method on the AVA data set reached 89.5%, which was 19.9% higher than that of the traditional method, and the generalization performance on the CUKHPQ data set was greatly improved. The experimental results show that the proposed approach can achieve better classification performance on the aesthetic quality evaluation of images.