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图学学报

图学学报 ›› 2022, Vol. 43 ›› Issue (4): 624-631.DOI: 10.11996/JG.j.2095-302X.2022040624

• 图像处理与计算机视觉 • 上一篇    下一篇

基于随机传播图卷积模型的零样本图像分类

  

  1. 1. 中国矿业大学信息与控制工程学院，江苏 徐州 221116；
    2. 中国科学技术大学信息科学技术学院，安徽 合肥 230027
  • 出版日期:2022-08-31 发布日期:2022-08-15
  • 通讯作者: 芦楠楠(1985)，女，副教授，博士。主要研究方向为模式识别、机器学习和图像处理
  • 基金资助:
    国家自然科学基金项目(62006233，51734009)

Zero-shot image classification based on random propagation graph convolution model

  1. 1. School of Information and Control Engineering, China University of Mining and Technology, Xuzhou Jiangsu 221116, China;
    2. School of Information Science and Technology, University of Science and Technology of China, Hefei Anhui 230027, China
  • Online:2022-08-31 Published:2022-08-15
  • Contact: LU Nan-nan (1985), associate professor, Ph. D. Her main research interests conver pattern recognition, machine learning and image processing
  • Supported by:
    National Natural Science Foundation of China (62006233, 51734009)

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摘要：

零样本图像分类旨在识别训练时从未出现过的全新类别(未见类别)，为此需要利用辅助信息建模未见类和可见类之间的关系。利用图卷积网络(GCN)进行零样本分类的模型可以借助知识图显式地表达类别之间的关系，但 GCN 易受过平滑影响，导致模型性能下降。针对此问题提出了基于随机传播图卷积模型的零样本图像分类方法。该方法使用随机传播机制处理原始特征以达到特征扰动和数据扩增的目的；利用数据中类别层级生成的知识图建模类别之间的语义关系。其中，图中节点代表类别，节点间的边代表类别之间的关系。再构建 GCN对处理后的特征进行训练，从节点中输出包含未见类别的分类器参数，进而实现零样本图像分类。实验结果表明,该方法可以有效地改善零样本图像分类中的时间消耗、分类精度和泛化性能。

关键词: 零样本图像分类, 知识图, 图卷积网络, 随机传播机制, 数据扩增

Abstract:

Zero-shot image classification aims to recognize new categories, namely, unseen categories that do not appear during training. Therefore, auxiliary information is needed to model the relationship between unseen and seen categories. With the aid of knowledge graph, zero-shot classification models based on graph convolution network (GCN) can explicitly express the relationship between categories, but GCN is susceptible to over-smoothing, resulting in the degradation of model performance. To address this problem, a zero-shot classification model based on random propagation graph convolution was proposed. In this model, the raw features were processed by random propagation mechanism to achieve feature perturbation and data augmentation. The generated knowledge graph based on category hierarchy could model the semantic relationship between categories, where graph nodes stand for categories and graph edges stand for relationships. Then the GCN was constructed to train the processed features, and the classifier parameters containing unseen categories, which were the output of nodes, could achieve zero-shot classification. Experimental results show that the model can significantly decrease time consumption, and improve accuracy and generalization performance.

Key words: zero-shot image classification, knowledge graph, graph convolution network, random propagation, data augmentation

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