基于聚类优化学习的少样本图像分类

doi:10.11896/jsjkx.230300227

Abstract

Abstract: 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.

Key words: Few-shot image classification, Feature representation, Clustering optimization

CLC Number:

TP391.41

SU Ruqi, BIAN Xiong, ZHU Songhao. Few-shot Images Classification Based on Clustering Optimization Learning[J].Computer Science, 2024, 51(6A): 230300227-7.

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