Journal of East China Normal University(Natural Science) ›› 2022, Vol. 2022 ›› Issue (4): 56-66.doi: 10.3969/j.issn.1000-5641.2022.04.006

• Computer Science • Previous Articles     Next Articles

A graph convolutional neural network for garment pattern classification

Xiaozhen ZHAO1, Weiqing TONG1,*(), Yongmei LIU2   

  1. 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:2021-02-04 Online:2022-07-25 Published:2022-07-19
  • Contact: Weiqing TONG


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

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