基于聚类优化学习的少样本图像分类

Few-shot Images Classification Based on Clustering Optimization Learning

少样本图像分类的目标是在训练少量标记训练数据的基础上实现新类别图像的分类,然而这一目的在现有条件下很难实现。因此,目前的少样本学习方法主要借鉴迁移学习的思想,其核心是利用情景训练式的元训练构建先验知识,从而解决未知新任务。然而,研究工作表明,相较于复杂的少样本学习方法,具有强大特征表示的嵌入模型学习方法更为简单、有效。受此启发,提出一种新的基于直推式聚类优化学习的少样本图像分类方法。该方法首先利用样本数据的内部特征结构信息实现每个类别的综合表示;然后优化每个类别的中心,形成更具区别性的特征表示,从而有效增加不同类别之间的特征差异。大量实验结果表明,所提的基于直推式聚类优化学习的少镜头图像分类方法有效提高了各种训练条件下的图像分类精度。

The goal of few-shot image classificationis to achieve the classification of new imagecategories on the basis of training a small number of labeled training dataset.However,this goal is difficult to achieve under existing conditions.Therefore,the current few-shot learning method mainly mainly draws on the idea of transfer learning,and its core is to construct prior knowledge by using situational meta-training,so as to realize the solution of unknown new tasks.However,the latest research shows that the embedded model learning method with strong feature representation is simpler and more effective than the complex few-shot learning method.Inspired by this,this paper proposes a novel few-shot image classification methodbased on direct clustering optimization learning.This proposed method first utilizes the internal feature structure information of sample data to realize the comprehensive representation of each category,and then optimizes the center of each category to form a more distinctive feature representation,thus effectively increasing the feature differences between different categories.A large number of experimental results demonstrate that the proposed image classification method based on the clustering optimization learningcan effectively improve the accuracy of image classification under various training conditions.