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Photo Aesthetic Scoring Through Spatial Aggregation Perception DCNN on a New IDEA Dataset

  • Xin Jin
  • Le Wu
  • Geng Zhao
  • Xinghui Zhou
  • Xiaokun Zhang
  • Xiaodong LiEmail author
Chapter
Part of the Studies in Computational Intelligence book series (SCI, volume 810)

Abstract

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. The advantages and effectiveness of our method are proved by experiments in different data sets.

Keywords

Aesthetic assessment Neural network Computer vision 

Notes

Acknowledgements

We thank all the reviewers and ACs. This work is partially supported by the National Natural Science Foundation of China (Grant Nos. 61772047, 61772513), the Science and Technology Project of the State Archives Administrator (Grant No. 2015-B-10), the open funding project of State Key Laboratory of Virtual Reality Technology and Systems, Beihang University (Grant No. BUAA-VR-16KF-09), the Fundamental Research Funds for the Central Universities (Grant No. 3122014C017), the China Postdoctoral Science Foundation (Grant No. 2015M581841), and the Postdoctoral Science Foundation of Jiangsu Province (Grant No. 1501019A).

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Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  • Xin Jin
    • 1
    • 2
  • Le Wu
    • 1
  • Geng Zhao
    • 1
  • Xinghui Zhou
    • 1
  • Xiaokun Zhang
    • 1
  • Xiaodong Li
    • 1
    Email author
  1. 1.Department of Cyber SecurityBeijing Electronic Science and Technology InstituteBeijingChina
  2. 2.CETC Big Data Research Institute Co., Ltd.GuiyangChina

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