深度卷积神经网络嵌套fine-tune的图像美感品质评价

针对使用卷积神经网络对图像美感品质研究中图像数据库过小的问题,使用fine-tune的迁移学习方法,分析卷积神经网络结构和图像内容对图像美感品质评价的影响。在按图像内容进行美感品质评价研究时,针对图像数据再次减小的问题,提出连续两次fine-tune的嵌套fine-tune方法,并在数据库Photo Quality上进行试验。试验结果表明,嵌套fine-tune方法得到的美感品质评价正确率比传统提取人工设计特征方法平均高出5.36%,比两种深度学习方法分别平均高出3.35%和2.33%,有效解决了卷积神经网络在图像美感品质研究中因图像数据库过小而带来的训练问题。

Image aesthetic quality evaluation based on embedded fine-tune deep CNN

The image database was not big enough for using convolutional neural networks to research the image aesthetic quality. Aiming at this problem, a fine-tune transfer learning method was used to analyze the effect of convolutional neural networks architecture and image contents on image aesthetic quality evaluation. During the research of image aesthetic quality evaluation by image contents, the problem of image data decrease rose again. The embedded fine-tune method using fine-tune twice continuously was proposed to solve the problem. The experiments were performed on Photo Quality, a small image database, and got a good effect. The results indicated that the accuracy of image aesthetic quality evaluation by embedded fine-tune was an average of 5.36% higher than by traditional artificially designed feature extraction method, 3.35% and 2.33% higher than by the other two deep learning methods respectively. The embedded fine-tune deep convolutional neural networks solved the problem of small database in image aesthetic quality evaluation research effectively and accurately.