基于局部正交特征融合的小样本图像分类

涂泽良; 程良伦; 黄国恒

doi:10.12052/gdutxb.230015

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基于局部正交特征融合的小样本图像分类

计算机科学与技术 | 更新时间：2026-05-27

- 基于局部正交特征融合的小样本图像分类
- Local Orthogonal Feature Fusion for Few-Shot Image Classification
- 广东工业大学学报 2024年41卷第2期页码：73-83
- 作者机构：
  
  广东工业大学计算机学院,广东,广州,510006
- 作者简介：
  
  涂泽良(1994–) ，男，硕士研究生，主要研究方向为计算机视觉、小样本学习，E-mail：2356472797@qq.com
  黄国恒(1985–) ，男，副教授，博士，主要研究方向为计算机视觉、机器学习、模式识别、医学图像处理，E-mail: kevinwong@gdut.edu.cn
- 基金信息：
  
  广东省重点领域研发计划项目(2019B010153002)
- DOI：10.12052/gdutxb.230015
  中图分类号： TP391
- 纸质出版：2024
- 稿件说明：
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涂泽良, 程良伦, 黄国恒. 基于局部正交特征融合的小样本图像分类[J]. 广东工业大学学报, 2024, 41(2): 73-83.

Tu Ze-liang, Cheng Liang-lun, Huang Guo-heng. Local Orthogonal Feature Fusion for Few-Shot Image Classification[J]. 2024, 41(2): 73-83.
涂泽良, 程良伦, 黄国恒. 基于局部正交特征融合的小样本图像分类[J]. 广东工业大学学报, 2024, 41(2): 73-83. DOI： 10.12052/gdutxb.230015.

Tu Ze-liang, Cheng Liang-lun, Huang Guo-heng. Local Orthogonal Feature Fusion for Few-Shot Image Classification[J]. 2024, 41(2): 73-83. DOI： 10.12052/gdutxb.230015.

摘要

针对目前基于度量学习的小样本图像分类方法中难以充分提取重要特征问题，提出一种基于局部正交特征融合的小样本图像分类方法。首先，利用特征提取网络同时提取局部细节丰富的浅层特征和语义化强的深层特征；然后，通过一个通道注意力模块和一个多尺度特征自适应融合模块分别在浅层特征的通道维度和空间尺度上进行特征增强，以生成更显著且包含更多尺度信息的局部特征。最后，通过一个局部正交特征融合模块对得到的多尺度局部特征和初始深层语义特征进行局部正交特征提取和注意力融合，以充分利用图像的局部和全局特征信息，生成更能代表目标类别的特征表示。在miniImageNet、tieredImageNet 和 CUB-200-2011三个公开数据集上的实验结果表明：提出的方法可以获得更好的分类效果，在5way-5shot任务上的准确率分别达到81.69%、85.36%和89.78%，与baseline模型相比，分类准确率分别提升 5.23%、3.19%和5.99%。

Abstract

How to extract important features by existing metric-based few-shot image classification models is a difficulty. A few-shot image classification method based on local orthogonal feature fusion is proposed. First

the feature extraction network is used to simultaneously extract shallow features with rich local details and deep features with strong semantics. Then

a channel attention module and a multi-scale feature adaptive fusion module are used to perform feature enhancement on the channel and scale dimensions of the shallow features

respectively

in order to generate the feature with more salient local features and more scale information. Finally

according to local orthogonal feature extraction and attention fusion

the obtained multi-scale local features and initial deep semantic features are extracted and fused by a local orthogonal feature fusion module. In this way

we can make full use of the local and global feature information of the image. And a feature representation is generated

which can be more representative of the target category. The experimental results on the three public datasets of miniImageNet

tieredImageNet and CUB-200-2011 show that the proposed method can achieve better classification results. The accuracy rate of the proposed method on the 5way-5shot task reaches 81.69%

85.36% and 89.78% respectively. Compared with the baseline model

the classification accuracy increased by 5.23%

3.19% and 5.99% respectively.

关键词

Keywords

references

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