计算机工程与应用2024，Vol.60Issue(14)：219-227,9.DOI:10.3778/j.issn.1002-8331.2304-0269
图采样泛化行人重识别算法
Generalized Pedestrian Re-Identification Method Based on Graph Sampling
摘要
Abstract
Recent study has shown that deep feature matching methods in metric learning,combined with large-scale and diverse training data,can significantly enhance the generalization ability of person re-identification.However,many existing methods generate large memory and computational costs,such as classification parameters or class memory learning.To address these issues,a new generalization person re-identification method based on correlation graph sampler(CGS)is proposed.The basic idea of CGS is to construct a nearest neighbor relationship graph for all classes using local sensitive Hashing(LSH)and feature metrics at the beginning of training.This ensures that each small batch of training samples is composed of randomly selected base classes and near-neighboring classes that are similar to the base classes to provide informative and challenging learning examples and improve the discriminative learning ability of person re-identification models.The sampling principle of CGS is influenced by the quality of features extracted by the backbone network,and therefore,the sampling ability of CGS can be enhanced with the training of the backbone network and has learnability.Through cross-evaluation of this method on large-scale datasets(including CUHK03,Market-1501,and MSMT17),exten-sive experimental results demonstrate the effectiveness of this method and showcase its potential in person re-identification applications.关键词
行人重识别/度量学习/相关性图采样/局部敏感哈希函数Key words
person re-identification/metric learning/correlation graph sampler/local sensitive Hashing(LSH)分类
信息技术与安全科学引用本文复制引用
闵锋,毛一新,况永刚,彭伟明,郝琳琳,吴波..图采样泛化行人重识别算法[J].计算机工程与应用,2024,60(14):219-227,9.基金项目
国家自然科学基金(62171328) （62171328）
武汉工程大学研究生教育创新基金(CX2022333). （CX2022333）
