Predicting Aesthetic Radar Map Using a Hierarchical Multi-task Network
The aesthetic quality assessment of images is a challenging work in the field of computer vision because of its complex subjective semantic information. The recent research work can utilize the deep convolutional neural network to evaluate the overall score of the image. However, the focus in the field of aesthetic is often not limited to the total score of image, and multiple attribute of the aesthetic evaluation can obtain image richer aesthetic characteristics. The multi-attribute rating called Aesthetic Radar Map. In addition, traditional deep learning methods can only be predicted by classification or simple regression, and cannot output multi-dimensional information. In this paper, we propose a hierarchical multi-task dense network to make multiple regression of the properties of images. According to the total score, the scoring performance of each attribute is enhanced, and the output effect is better by optimizing the network structure. Through this method, the more sufficient aesthetic information of the image can be obtained, which is of certain guiding significance to the comprehensive evaluation of image aesthetics.
Aesthetic evaluation Neural network Computer vision
Xin Jin Le Wu Xinghui Zhou Geng Zhao Xiaokun Zhang Xiaodong Li Shiming Ge
Department of Cyber Security,Beijing Electronic Science and Technology Institute,Beijing 100070,Chin Department of Cyber Security,Beijing Electronic Science and Technology Institute,Beijing 100070,Chin Institute of Information Engineering,Chinese Academy of Sciences,Beijing 100093,China
国际会议
广州
英文
41-50
2018-11-23（万方平台首次上网日期，不代表论文的发表时间）