一种面向服装样板图分类的图卷积神经网络

A graph convolutional neural network for garment pattern classification

服装样板图的识别和分类是服装智能化生产和管理的重要技术.提出了一种将服装样板图转化为图数据的建模法和一种能处理这类图数据的轻量级图神经网络GPC-GCN(Garment Pattern Classification Graph Convolutional Network).所提出的图数据的建模法不但能够保留服装样板图中每个部件的形状信息,还解决了服装样版图中服装部件可以随意放置的问题.实验表明,与既有的卷积神经网络和图神经网络相比较,所提出的图数据建模法和图神经网络GPC-GCN对服装样板图的分类具有更优的性能.

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.