基于深度学习的图像美观度评价

Image Aesthetic Assessment Based on Deep Learning

基于深度卷积神经网络的特征提取方法比传统手工特征提取方法更加贴近人类大脑的视觉感受。为此,建立一种两通道组合图像美观度评价模型。使用美学信息通道和场景信息通道的组合来自动提取图像中美学信息和场景类别信息,通过融合两类信息最终形成美感分类器。在AVA库上进行训练和测试,结果表明,与图像局部特征提取方法相比,该模型结构较简洁,且具有较高的分类准确率。

The method of extracting features based on deep Convolutional Neural Network(CNN)is closer to the visual perception of the human brain than the traditional manual extraction feature method.Therefore,a two-channel combination model is proposed.The combination of the aesthetic information channel and the scene information channel is used to automatically extract the aesthetic information and scene category information in the image,and finally combine the two types of information to form an aesthetic classifier.The training and testing on the AVA library show that compared with the image local feature extraction method,the model structure is simple and has high classification accuracy.