Two open-source projects for image aesthetic quality assessment

  • Le Wu
  • Xin JinEmail author
  • Geng Zhao
  • Xinghui Zhou
News & Views


  1. 1.
    Jin X, Chi J Y, Peng S W, et al. Deep image aesthetics classification using inception modules and finetuning connected layer. In: Proceedings of the 8th International Conference on Wireless Communications and Signal Processing (WCSP), Yangzhou, 2016Google Scholar
  2. 2.
    Jin X, Wu L, Li X D, et al. ILGNet: inception modules with connected local and global features for efficient image aesthetic quality classification using domain adaptation. IET Comput Vision, 2018. doi: 10.1049/iet-cvi.2018.5249Google Scholar
  3. 3.
    Jin X, Wu L, Li X D, et al. Predicting aesthetic score distribution through cumulative Jensen-Shannon divergence. In: Proceedings of AAAI Conference on Artificial Intelligence, New Orleans, 2018Google Scholar
  4. 4.
    Wang J L, Lu Y H, Liu J B, et al. A robust threestage approach to large-scale urban scene recognition. Sci China Inf Sci, 2017, 60: 103101CrossRefGoogle Scholar

Copyright information

© Science China Press and Springer-Verlag GmbH Germany, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Beijing Electronic Science and Technology InstituteBeijingChina
  2. 2.State Key Laboratory of CryptologyBeijingChina
  3. 3.China Electronics Technology Group Corporation Big Data Research Institute Co., Ltd.GuiyangChina

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