摘要
为了提高行人重识别距离度量MLAPG算法的鲁棒性,该文提出基于等距度量学习策略的行人重识别Equid-MLAPG算法。MLAPG算法中正负样本对在映射空间的分布不均衡导致间距超参数受负样本对距离影响更大,因此该文设计的Equid-MLAPG算法要求正样本对映射成为变换空间中的一个点,即正样本对在变换空间中距离为零,使算法收敛时正负样本对距离分布不存在交叉部分。实验表明Equid-MLAPG算法能在常用的行人重识别数据集上取得良好的实验效果,具有更好的识别率和广泛的适用性。
In order to improve the robustness of MLAPG algorithm,a person re-identification algorithm,called Equid-MLAPG algorithm is proposed,which is based on the equidistance measurement learning strategy.Due to the imbalanced distribution of positive and negative sample pairs in the mapping space,sample spacing hyper-parameter of MLAPG algorithm is more affected by the distance of negative sample pairs.Therefore,Equid-MLAPG algorithm tends to map the positive sample pair to be a point in the transform space.That is,the distance of a positive sample pair in the transform space is mapped to be zero,resulting in no intersection in the distribution of positive and negative sample pairs in the transform space when algorithm convergences.Experiments show that the Equid-MLAPG algorithm can achieve better experimental results on commonly used person re-identification datasets with better recognition rate and wide applicability.
作者
周智恒
刘楷怡
黄俊楚
陈增群
ZHOU Zhiheng;LIU Kaiyi;HUANG Junchu;CHEN Zengqun(School of Electronic and Information Engineering,South China University of Technology,Guangzhou 510000,China)
出处
《电子与信息学报》
EI
CSCD
北大核心
2019年第2期477-483,共7页
Journal of Electronics & Information Technology
基金
国家自然科学基金(U1401252
61871188)
国家重点研发计划(2018YFC0309400)
中央高校基本科研业务费专项资金(2017MS062)
广州市产学研协同创新重大专项(201604016133)~~
