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An Abstract Painting Generation Method Based on Deep Generative Model

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Abstract

Computer technology provides new conditions and possibilities for art creation and research, and also expands the forms of artistic expression. Computer-created art has thus become one of the important forms of art. In this paper, we proposed a novel method of generating abstract paintings. We used the public painting dataset WikiArt and designed a K-Means algorithm that automatically finds the optimal K value to perform color segmentation on these images, and divide the picture into different color blocks. We proposed the concept of the collection of color block (CoCB), which records all color block information of the segmented image and serves as an intermediate vector for the generation of abstract painting. We extracted the CoCB as an empirical sample and used a learning model based on deep learning to automatically generate brand-new CoCBs. We then converted the CoCBs into an abstract painting, so that the generated abstract painting also followed certain aesthetic rules. Experiments showed that the resulting abstract painting have great visual impact, and some of them have been installed as decorations in public and private spaces, as well as art institutions. Also, some artists and designers have used the results in their work.

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References

  1. Franke HW (1971) Computers and visual art. Leonardo 4(4):331–338

    Article  Google Scholar 

  2. Judelman G (2004) Aesthetics and inspiration for visualization design: bridging the gap between art and science. In: Proceedings, eighth international conference on information visualisation. IEEE, pp 245–250

  3. Marcos AF (2007) Digital art: when artistic and cultural muse merges with computer technology. IEEE Comput Graph Appl 27(5):98–103

    Article  Google Scholar 

  4. King M (2002) Computers and modern art: digital art museum. In: Proceedings of the 4th conference on creativity & cognition, pp 88–94

  5. Kandinsky W (2013) Point and line to plane, 1–4. Renmin University of China Press, Beijing

    Google Scholar 

  6. Berezhnoy IE, Postma EO, Herik JVD (2005) Computerized visual analysis of paintings. In: International conference on association for history and computing, pp 28–32

  7. Laposky BF (1969) Oscillons: electronic abstractions. Leonardo 2:345–354

    Article  Google Scholar 

  8. Dietrich F (1986) Visual intelligence: the first decade of computer art (1965–1975). Leonardo 19(2):159–169

    Article  Google Scholar 

  9. Nake F (2007) Computer art: creativity and computability. In: Proceedings of the 6th ACM SIGCHI conference on creativity & cognition, pp 305–306

  10. Molnar V (1975) Toward aesthetic guidelines for paintings with the aid of a computer. Leonardo 8:185–189

    Article  Google Scholar 

  11. Taylor RP (2002) Order in pollock’s chaos. Sci Am 287(6):116–121

    Article  Google Scholar 

  12. Taylor RP, Micolich AP, Jonas D (1999) Fractal analysis of Pollock’s drip paintings. Nature 399(6735):422

    Article  Google Scholar 

  13. Taylor RP, Micolich AP, Jonas D (2002) The construction of Jackson Pollock’s fractal drip paintings. Leonardo 35(2):203–207

    Article  Google Scholar 

  14. Zheng Y, Nie X, Meng Z, Feng W, Zhang K (2015) Layered modeling and generation of Pollock’s drip style. Vis Comput 31(5):589–600

    Article  Google Scholar 

  15. Zhang K, Yu J (2014) Generation of Kandinsky art. Leonardo 49(1):589–600

    MathSciNet  Google Scholar 

  16. Tao W, LiuY, Zhang K (2014) Automatically generating abstract paintings in Malevich style. In: IEEE/ACIS 13th international conference on computer and information science (ICIS), pp 201–205

  17. Xiong L, Zhang K (2016) Generation of Miro’s surrealism. In: Proceedings of the 9th international symposium on visual information communication and interaction, pp 130–137

  18. Kingma DP, Welling M (2013) Auto-encoding variational bayes. arXiv preprint arXiv:1312.6114

  19. Radford A, Metz L, Chintala S (2015) Unsupervised representation learning with deep convolutional generative adversarial networks. arXiv preprint arXiv:1511.06434

  20. Arjovsky M, Chintala S, Bottou L (2017) Wasserstein GAN. arXiv preprint arXiv:1701.07875

  21. Gulrajani I, Ahmed F, Arjovsky M, Dumoulin V, Courville A (2017) Improved training of Wasserstein GANs. In: Advances in neural information processing systems, pp 5769–5779

  22. Yu Y, Gong Z, Zhong P, Shan J (2017) Unsupervised representation learning with deep convolutional neural network for remote sensing images. arXiv preprint arXiv

  23. Jian CL, Zhang Y, Li Y (2015) Non-divergence of stochastic discrete time algorithms for PCA neural network. IEEE Trans Neural Netw Learn Syst 26(2):394–399

    Article  MathSciNet  Google Scholar 

  24. Goodfellow I, Pouget-Abadie J, Mirza M, Xu B, Warde-Farley D, Ozair S, Courville A, Bengio Y (2014) Generative adversarial networks. In: Advances in neural information processing systems, pp 2672–2680

  25. Elgammal A, Liu B, Elhoseiny M, Mazzone M (2017) Can: creative adversarial networks, generating“ art” by learning about styles and deviating from style norms. arXiv preprint arXiv:1706.07068

  26. Gatys LA, Ecker AS, Bethge M (2015) A neural algorithm of artistic style. Comput Sci. arXiv preprint arXiv

  27. Arnheim (2006) Art and visual perception 13–31. Sichuan People’s Publishing Press, Chengdu

    Google Scholar 

  28. Gray R, Linde Y (1982) Vector quantizers and predictive quantizers for Gauss–Markov sources. IEEE Trans Commun 30:381–389

    Article  Google Scholar 

  29. Kingma DP, Ba J (2014) Adam: a method for stochastic optimization. arXiv preprint arXiv,1412.6980

Download references

Acknowledgements

This work is supported by National Natural Science Fund for Distinguished Young Scholars (Grant No. 61625204) and partially supported by the State Key Program of the National Science Foundation of China (Grant Nos. 61836006 and 61432014).

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Authors and Affiliations

  1. Sichuan University, No.24 South Section 1, Yihuan Road, Chengdu, China

    Mao Li, Jiancheng Lv, Jian Wang & Yongsheng Sang

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  1. Mao Li
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  2. Jiancheng Lv
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  3. Jian Wang
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  4. Yongsheng Sang
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Correspondence to Jiancheng Lv.

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Li, M., Lv, J., Wang, J. et al. An Abstract Painting Generation Method Based on Deep Generative Model. Neural Process Lett 52, 949–960 (2020). https://doi.org/10.1007/s11063-019-10063-3

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