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Technology of Graphic & Image
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App. Res. of Comp. Printed Article
Vol. 38, 2021 No. 5
1581-1585,1594

Fabric classification based on graph convolutional network

Peng Taoa
Peng Dib
Liu Junpinga
Hu Xinronga
Zhang Zilib
Chen Changnianb
Jiang Minghuab
a. Hubei Province Garment Information Engineering Technology Research Center, b. Dept. of Mathematics & Computer Science, Wuhan Textile University, Wuhan 430200, China
DOI: 10.19734/j.issn.1001-3695.2020.05.0152

Abstract

Fabric classification research has been widely used in the field of fabric production, clothing design and so on. This paper introduced a novel method combining fabric force model, the multi-frame timing information with GCN for fabric classification. This method used 30 different fabrics' moving videos with the wind driving as the experimental datasets. Firstly, it took each frame of the video as a graph node, and then according to the video sequence connectioned for the similar fabric edge nodes. Secondly, it used the fabric force model to preprocess the original video image to extract force flow features as visual words and store them, then used the visual words to explore the potential connections between similar and different types of fabrics, so as to transform the Euclidean fabric video data into non-Euclidean fabric graph structured data. Finally, these data processed by GCN network. This work has a good performance without the effectiveness of light, texture and color, breaks through the limitation that traditional classification method can only classify a few fabrics, and has good classification effect.

Foundation Support

湖北省自然科学基金资助项目(2014CFB764)
湖北省教育厅青年项目(Q201316)
湖北省教育厅科研计划重点项目(D20191708)

Publish Information

DOI: 10.19734/j.issn.1001-3695.2020.05.0152
Publish at: Application Research of Computers Printed Article, Vol. 38, 2021 No. 5
Section: Technology of Graphic & Image
Pages: 1581-1585,1594
Serial Number: 1001-3695(2021)05-057-1581-05

Publish History

[2021-05-05] Printed Article

Cite This Article

彭涛, 彭迪, 刘军平, 等. 基于图卷积神经网络的织物分类研究 [J]. 计算机应用研究, 2021, 38 (5): 1581-1585,1594. (Peng Tao, Peng Di, Liu Junping, et al. Fabric classification based on graph convolutional network [J]. Application Research of Computers, 2021, 38 (5): 1581-1585,1594. )

About the Journal

  • Application Research of Computers Monthly Journal
  • Journal ID ISSN 1001-3695
    CN  51-1196/TP

Application Research of Computers, founded in 1984, is an academic journal of computing technology sponsored by Sichuan Institute of Computer Sciences under the Science and Technology Department of Sichuan Province.

Aiming at the urgently needed cutting-edge technology in this discipline, Application Research of Computers reflects the mainstream technology, hot technology and the latest development trend of computer application research at home and abroad in a timely manner. The main contents of the journal include high-level academic papers in this discipline, the latest scientific research results and major application results. The contents of the columns involve new theories of computer discipline, basic computer theory, algorithm theory research, algorithm design and analysis, blockchain technology, system software and software engineering technology, pattern recognition and artificial intelligence, architecture, advanced computing, parallel processing, database technology, computer network and communication technology, information security technology, computer image graphics and its latest hot application technology.

Application Research of Computers has many high-level readers and authors, and its readers are mainly senior and middle-level researchers and engineers engaged in the field of computer science, as well as teachers and students majoring in computer science and related majors in colleges and universities. Over the years, the total citation frequency and Web download rate of Application Research of Computers have been ranked among the top of similar academic journals in this discipline, and the academic papers published are highly popular among the readers for their novelty, academics, foresight, orientation and practicality.

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