Journal of East China Normal University(Natural Science) >

2022 , Vol. 2022 >Issue 4: 56 - 66

DOI: https://doi.org/10.3969/j.issn.1000-5641.2022.04.006

Computer Science

A graph convolutional neural network for garment pattern classification

  • Xiaozhen ZHAO ,
  • Weiqing TONG ,
  • Yongmei LIU
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  • 1. School of Computer Science and Technology, East China Normal University, Shanghai　200062, China
    2. College of Fashion and Design, Donghua University, Shanghai　200051, China

Received date: 2021-02-04

  Online published: 2022-07-19

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Abstract

The identification and classification of garment patterns are important technologies for intelligent clothing production and management. This paper proposes a method to convert garment patterns into graphic data and subsequently proposes a lightweight graph neural network GPC-GCN (Garment Pattern Classification Graph Convolutional Network) that can process this graphic data. The proposed graph data modeling method can not only maintain information on the shape of each component in the garment pattern but also deal with the arbitrariness of the position of components in garment patterns. Experiments show that the proposed graph neural network GPC-GCN achieves a better result for the classification of garment patterns compared to convolutional neural networks and graph convolutional networks.

Key words： graph convolutional network; garment pattern; graph classification

Cite this article

Xiaozhen ZHAO , Weiqing TONG , Yongmei LIU . A graph convolutional neural network for garment pattern classification[J]. Journal of East China Normal University(Natural Science), 2022 , 2022(4) : 56 -66 . DOI: 10.3969/j.issn.1000-5641.2022.04.006

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