Innovative method of ethnic pattern based on neural style transfer network

Yu Kang Hou, Jian Lv, Xiang Liu, Tao Hu, Ze Yu Zhao

Research output: Contribution to journalArticlepeer-review

15 Scopus citations

Abstract

Aiming at the problems of low manual extraction efficiency of traditional ethnic pattern symbols as well as the difficulty of designing creative graphic combination, this paper proposed an innovative model of traditional ethnic pattern style by taking the batik and cross-stitch work of the Miao nationality as an example. This model combined the improved shape grammar with neural style transfer network based on deep learning, to extract e and encode ethnic pattern configuration frames. Then, a large number of innovative ethnic patterns was generated through shape grammars, and the basic features of ethnic patterns was extracted quickly using the style-transfer network, so as to transfer and generate innovative ethnic pattern designs based on this framework. The experimental results show that the model can generate brand-new ethnic ornament patterns on the basis of the specified frame, which are more orderly than those generated directly by means of the neural network transfer. The generated pattern will finally be applied to the design of the Miao fabric patterns, which verifies the feasibility of this method and design process.

Original languageEnglish
Pages (from-to)606-613
Number of pages8
JournalJournal of Graphics
Volume41
Issue number4
DOIs
StatePublished - 31 Aug 2020
Externally publishedYes

Keywords

  • batik
  • convolutional neural network
  • cross stitch
  • neural style transfer
  • shape grammar

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