IDEA: A new dataset for image aesthetic scoring
Multimedia Tools and Applications(2018)
摘要
The aesthetic quality assessment of image is a challenging work in computer vision field. The recent research work used the deep convolutional neural network to evaluate the aesthetic quality of images. However, the score of image data sets has a strongly normal distribution, which makes the training of neural network easy to be over-fitting. In addition, traditional deep learning methods usually pre-process images, which destroy the original aesthetic features of the picture, so that the network can only learn some superficial aesthetic features. This paper presents a new data set what images distributed evenly for aesthetics (IDEA). This data set has less statistical characteristics, which is helpful for the neural network to learn the deeper features. We propose a new spatial aggregation perception neural network architecture which can control channel weights automatically. Our experiments in different data sets can prove the advantages and effectiveness of our method.
更多查看译文
关键词
Aesthetic assessment,Neural network,Computer vision
AI 理解论文
溯源树
样例
生成溯源树,研究论文发展脉络