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Monet-Style Images Generation Using Recurrent Neural Networks

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  • First Online:
E-Learning and Games (Edutainment 2016)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 9654))

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Abstract

An automatic Monet-style images generation method using long short term memory recurrent neural network is proposed in this paper. The method shows that long short term memory recurrent neural network can learn the structure and characteristics of Monet’s paintings properly by demonstrating its ability to generate impressionism-style images. With Monet’s paintings as input, similar style of images can be constructed using the proposed method iteratively. The experiment results indicate that the trained recurrent neural networks were able to generate Monet-style images with a small amount of training data.

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Acknowledgments

This research was funded by the grants (No. 61540062) from the Natural Science Foundation of China.

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Authors and Affiliations

  1. School of Information, Yunnan University, Kunming, 650091, Yunnan, China

    Yili Zhao & Dan Xu

  2. School of Computer and Information, Souwest Forestry University, Kunming, 650224, Yunnan, China

    Yili Zhao

Authors
  1. Yili Zhao
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  2. Dan Xu
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Corresponding author

Correspondence to Yili Zhao .

Editor information

Editors and Affiliations

  1. Computer Science, John Moores University, Liverpool, United Kingdom

    Abdennour El Rhalibi

  2. Science and Technology, Bournemouth University, Bournemouth, United Kingdom

    Feng Tian

  3. Hangzhou Normal University, Hangzhou, Zhejiang, China

    Zhigeng Pan

  4. Computer Science and Technology, University of Bedfordshire, Luton, United Kingdom

    Baoquan Liu

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© 2016 Springer International Publishing Switzerland

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Zhao, Y., Xu, D. (2016). Monet-Style Images Generation Using Recurrent Neural Networks. In: El Rhalibi, A., Tian, F., Pan, Z., Liu, B. (eds) E-Learning and Games. Edutainment 2016. Lecture Notes in Computer Science(), vol 9654. Springer, Cham. https://doi.org/10.1007/978-3-319-40259-8_18

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  • DOI: https://doi.org/10.1007/978-3-319-40259-8_18

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-40258-1

  • Online ISBN: 978-3-319-40259-8

  • eBook Packages: Computer ScienceComputer Science (R0)

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