Exploration of artistic creation of Chinese ink style painting based on deep learning framework and convolutional neural network model

  • Shuangshuang ChenEmail author


For the purpose of applying information technology to the creation of ink style painting, the algorithm of ink painting rendering based on the deep learning framework and convolutional neural network model is designed and improved. Firstly, the ink style rendering program is written in Python. Secondly, VGG under Caffe architecture and Illustration 2Vec models are transplanted to TensorFlow architecture, and the image is rendered in ink style based on deep learning framework and convolutional neural network model. Finally, based on Node.js, the server-side program for image ink style rendering is built. Among them, Express is adopted as the Web-side framework, and the front-end page effect is completed. The results show that the ink rendering logic program is applicable, and the expected purpose is achieved.


Deep learning Convolutional neural network Ink style rendering 


Compliance with ethical standards

Conflict of interest

All authors declare that they have no conflict of interest.

Ethical approval

This article does not contain any studies with human participants or animals performed by any of the authors.

Informed consent

Informed consent was obtained from all individual participants included in the study.


  1. Bagnall A, Lines J, Hills J, Bostrom A (2015) Time-series classification with cote: the collective of transformation-based ensembles. IEEE Trans Knowl Data Eng 27(9):2522–2535CrossRefGoogle Scholar
  2. Bakker ATMD, Tissier MFS, Ruessink BG (2016) Beach steepness effects on nonlinear infragravity—wave interactions: a numerical study. J Geophys Res Oceans 121(1):554–570CrossRefGoogle Scholar
  3. Bellodi E, Riguzzi F (2015) Structure learning of probabilistic logic programs by searching the clause space. Theory Pract Log Program 15(2):169–212CrossRefzbMATHGoogle Scholar
  4. Bisson J, Mcalpine JB, Friesen JB, Chen SN, Graham J, Pauli GF (2016) Can invalid bioactives undermine natural product-based drug discovery? J Med Chem 59(5):1671CrossRefGoogle Scholar
  5. Bossaerts P, Plott C (2016) Basic principles of asset pricing theory: evidence from large-scale experimental financial markets. Soc Sci Electron Publ 8(1070):135–169zbMATHGoogle Scholar
  6. Cecile B, David CJ, Matteo M, Barbora M (2015) Clustering attributed graphs: models, measures and methods. Netw Sci 3(3):408–444CrossRefGoogle Scholar
  7. Chong HY, Wang X (2016) The outlook of building information modeling for sustainable development. Clean Technol Environ Policy 18(6):1–11CrossRefGoogle Scholar
  8. Duraisamy S, Emperumal S (2017) Computer-aided mammogram diagnosis system using deep learning convolutional fully complex-valued relaxation neural network classifier. IET Comput Vis 11(8):656–662CrossRefGoogle Scholar
  9. Elgammal A, Saleh B (2015) Quantifying creativity in art networks. Int J Online Eng 7(2):29–35Google Scholar
  10. Falomir Z, Museros L, Sanz I, Gonzalez-Abril L (2018) Categorizing paintings in art styles based on qualitative color descriptors, quantitative global features and machine learning (QArt-Learn). Expert Syst Appl 97:83–94CrossRefGoogle Scholar
  11. Gao F, Huang T, Wang J, Sun J, Hussain A, Yang E (2017) Dual-branch deep convolution neural network for polarimetric SAR image classification. Appl Sci 7(5):447CrossRefGoogle Scholar
  12. Guo Z (2016) Chinese women’s basketball team player to attack based on goal programming technology and method of exploration. J Comput Theor Nanosci 13(12):10072–10075CrossRefGoogle Scholar
  13. Hölbling D, Friedl B, Eisank C (2015) An object-based approach for semi-automated landslide change detection and attribution of changes to landslide classes in northern Taiwan. Earth Sci Inf 8(2):327–335CrossRefGoogle Scholar
  14. Ionescu B, Uijlings J, Sebe N (2016) Fisher kernel temporal variation-based relevance feedback for video retrieval. Comput Vis Image Underst 143(C):38–51Google Scholar
  15. Liu J, Yang W, Sun X, Zeng W (2018) Photo stylistic brush: robust style transfer via superpixel-based bipartite graph. IEEE Trans Multimed 20(7):1724–1737CrossRefGoogle Scholar
  16. Ludwig T, Reuter C, Pipek V (2015) Social haystack: dynamic quality assessment of citizen-generated content during emergencies. ACM Trans Comp Hum Interact 22(4):1–27CrossRefGoogle Scholar
  17. Luz EJ, Schwartz WR, Cámara-Chávez G, Menotti D (2016) Ecg-based heartbeat classification for arrhythmia detection: a survey. Comput Methods Progr Biomed 127(C):144–164CrossRefGoogle Scholar
  18. Ma Y, Liu K, Guan Z, Xu X, Qian X, Bao H (2018) Background augmentation generative adversarial networks (BAGANs): effective data generation based on GAN-augmented 3D synthesizing. Symmetry 10(12):734CrossRefGoogle Scholar
  19. Roux F, Maryhuard T, Barillot E, Wenes E, Botran L, Durand S et al (2016) Cytonuclear interactions affect adaptive traits of the annual plant arabidopsis thaliana in the field. Proc Natl Acad Sci USA 113(13):3687CrossRefGoogle Scholar
  20. Standl E, Schnell O, Mcguire DK (2016) Heart failure considerations of antihyperglycemic medications for type 2 diabetes. Circ Res 118(11):1830–1843CrossRefGoogle Scholar
  21. Stapleton G, Plimmer B, Delaney A, Rodgers P (2015) Combining sketching and traditional diagram editing tools. ACM Trans Intell Syst Technol 6(1):1–29CrossRefGoogle Scholar
  22. Tsimpourlas F, Papadopoulos L, Bartsokas A, Soudris D (2018) A design space exploration framework for convolutional neural networks implemented on edge devices. IEEE Trans Comput Aided Des Integr Circuits Syst 37(11):2212–2221CrossRefGoogle Scholar
  23. Vaidya K, Campbell J (2016) Multidisciplinary approach to defining public e-procurement and evaluating its impact on procurement efficiency. Inf Syst Front 18(2):333–348CrossRefGoogle Scholar
  24. Xie N, Yang Y, Shen HT, Zhao TT (2018) Stroke-based stylization by learning sequential drawing examples. J Vis Commun Image Represent 51:29–39CrossRefGoogle Scholar
  25. Yang W, Schuster C, Beahan CT, Charoensawan V, Peaucelle A, Bacic A et al (2016) Regulation of meristem morphogenesis by cell wall synthases in arabidopsis. Curr Biol 26(11):1404–1415CrossRefGoogle Scholar
  26. Zalli A, Jovanova O, Hoogendijk WJG, Tiemeier H, Carvalho LA (2016) Low-grade inflammation predicts persistence of depressive symptoms. Psychopharmacology 233(9):1669–1678CrossRefGoogle Scholar
  27. Zhuo Y, Feng Y, Cheng C, Fu J, Zhou X, Yuan J (2018) Extensive exploration of comprehensive vehicle attributes using D-CNN with weighted multi-attribute strategy. IET Intel Transp Syst 12(3):186–193CrossRefGoogle Scholar

Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2019

Authors and Affiliations

  1. 1.School of DesignSangmyung UniversityCheonanKorea

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