湖北民族大学学报（自然科学版）2026，Vol.44Issue(2)：173-178,6.DOI:10.13501/j.cnki.42-1908/n.2026.03.016
基于受控正样本采样与平衡负样本挖掘的无监督图像分类算法
Unsupervised Image Classification Algorithm Based on Controlled Positive Sample Sampling and Balanced Negative Sample Mining
摘要
Abstract
Contrastive learning often suffered from false positive samples and false negative samples during positive sample augmentation and negative sample mining,which could lead to degraded performance of unsupervised image classification algorithms.To address this problem,a controlled positive sample sampling and balanced negatives sample mining(CPS-BN)algorithm for unsupervised image classification was proposed.Specifically,the CPS algorithm was designed to generate semantically consistent yet moderately diverse positive sample pairs through controlled view sampling,thereby reducing incorrect positive sample matching in fine-grained scenarios.Meanwhile,the BN algorithm was formulated to construct a negative sample selection probability based on similarity differences,and ineffective samples as well as false negative samples were adaptively filtered to enhance effective gradients and stabilize training.The results on the Canadian Institute for Advanced Research-10(CIFAR-10)image dataset showed that the clustering accuracy(ACC)of the CPS-BN algorithm was improved by 4.1 percentage points compared with the strongly augmented contrastive clustering(SACC)algorithm.On the ImageNet fine-grained dog dataset,the ACC of the proposed algorithm was further improved by 3.8 percentage points over the SACC algorithm.The CPS-BN algorithm effectively alleviated sample selection bias in contrastive learning and improved the quality of feature representation and clustering performance,which was well suited for unsupervised image classification scenarios.关键词
无监督图像分类/对比学习/自监督学习/正样本采样/负样本挖掘Key words
unsupervised image classification/contrastive learning/self-supervised learning/positive sample sampling/negative sample mining分类
信息技术与安全科学引用本文复制引用
李慧贤,刘三民,叶力玮,林鑫,陈冉冉..基于受控正样本采样与平衡负样本挖掘的无监督图像分类算法[J].湖北民族大学学报（自然科学版）,2026,44(2):173-178,6.基金项目
安徽省自然科学基金项目(2308085MF220) （2308085MF220）
安徽省高校自然科学研究重点项目(2022AH050972,KJ2021A0516). （2022AH050972,KJ2021A0516）
