Few-shot Images Classification Based on Clustering Optimization Learning
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 cur-rent 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 opti-mization learning.This proposed method first utilizes the internal feature structure information of sample data to realize the com-prehensive representation of each category,and then optimizes the center of each category to form a more distinctive feature rep-resentation,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.
NETL
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