A graph convolutional neural network for garment pattern classification

doi:10.3969/j.issn.1000-5641.2022.04.006

Abstract

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

CLC Number:

TP391.4

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.

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组件	输出	参数设置
GMMConv1	$\left\|\alpha \right\|=32$	$ \left\|A\right\|=2\times 6 $
		$ \left\|C\right\|=1\times 4 $
		$ J=16 $
SAG Pooling1	$ \left\|V\right\|=0.4N $	$ \left\|A\right\|=1\times 6 $
GMMConv2	$\left\|\alpha \right\|=64$	$ \left\|A\right\|=2\times 6 $
		$ \left\|C\right\|=1\times 4 $
		$ J=32 $
SAG Pooling2	$ \left\|V\right\|=0.16N $	$ \left\|A\right\|=1\times 6 $
GMMConv3	$\left\|\alpha \right\|=128$	$ \left\|A\right\|=2\times 6 $
		$ \left\|C\right\|=1\times 4 $
		$ J=64 $
全连接层	$ \left\|c\right\|=C $ , $ C $ 为类型数	$ \left\|A\right\|=1\times C $ , $ C $ 为类型数

数据集	大衣样板数/幅	长裙样板数/幅	长大衣样板数/幅	裤子样板数/幅	衬衣样板数/幅	短裙样板数/幅	上衣样板数/幅	样板数总计/幅
GPDSet-A	24128	20544	12592	3472	4960	4736	6896	77328
GPDSet-B	10016	30896	10592	14992	0	6416	11024	83936

神经网络	Top 1 准确率	平均推断时间/ms
ResNeSt50	0.908	15.6
GIN	0.342	5.3
GCN	0.452	4.5
GPC-GCN	0.955	9.1

神经网络	Top 1 准确率	平均推断时间/ms
ResNeSt50	0.511	15.6
GIN	0.287	5.3
GCN	0.283	4.5
GPC-GCN	0.715	9.1

神经网络	Top 1 准确率	平均推断时间/ms
ResNeSt50	0.133	15.6
GIN	0.271	5.3
GCN	0.282	4.5
GPC-GCN	0.694	9.1