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Large margin deep embedding for aesthetic image classification

  • Guanjun Guo
  • Hanzi WangEmail author
  • Yan Yan
  • Liming Zhang
  • Bo Li
Letter
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Abstract

We present an LMDE method with a novel network structure and an effective joint loss function, which takes advantage of both the triplet loss function and the hinge loss function. The minimization of the joint loss function ensures that the intra-class variability of the features belonging to the same class is reduced and the inter-class separability of the features from different classes is increased. As shown in the experiments, the proposed LMDE method significantly outperforms several other state-of-the-art aesthetic classification methods in terms of classification accuracy.

Notes

Acknowledgements

This work was supported by National Natural Science Foundation of China (Grant Nos. U1605252, 61872307, 61472334, 61571379), National Key R&D Program of China (Grant No. 2017YFB1302400), and UM Multi-Year Research (Grant No. MYRG2017-00218-FST).

Supplementary material

11432_2018_9567_MOESM1_ESM.pdf (697 kb)
Large margin deep embedding for aesthetic image classification

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Copyright information

© Science China Press and Springer-Verlag GmbH Germany, part of Springer Nature 2019

Authors and Affiliations

  • Guanjun Guo
    • 1
  • Hanzi Wang
    • 1
    Email author
  • Yan Yan
    • 1
  • Liming Zhang
    • 2
  • Bo Li
    • 3
  1. 1.Fujian Key Laboratory of Sensing and Computing for Smart City, School of Information Science and EngineeringXiamen UniversityXiamenChina
  2. 2.Faculty of Science and TechnologyUniversity of MacauMacauChina
  3. 3.Beijing Key Laboratory of Digital Media, School of Computer Science and EngineeringBeihang UniversityBeijingChina

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