判别式
人工智能
计算机科学
特征(语言学)
鉴定(生物学)
模式识别(心理学)
行人
局部结构
局部二进制模式
直方图
地理
图像(数学)
物理
考古
化学物理
哲学
生物
植物
语言学
作者
Haijia Zhang,Tongzhen Si,Zhong Zhang,Ronghua Zhang,Hao Ma,Shuang Liu
出处
期刊:IEEE Access
[Institute of Electrical and Electronics Engineers]
日期:2020-01-01
卷期号:8: 83685-83692
被引量:7
标识
DOI:10.1109/access.2020.2991838
摘要
Local features could learn semantic information for pedestrian images and they are very important for person re-identification (Re-ID) in harsh environments. However, most approaches only optimize one kind of local feature, which results in incomplete local features. In this paper, we propose Local Heterogeneous Features (LHF) to extract discriminative local features from three aspects. To this end, we utilize three kinds of losses to learn three kinds of local features, i.e., local discriminative features, local relative features, local compact features. As for local discriminative features, we split the attention maps into three horizontal sub-regions and perform the classification operation. Then, we divide the attention maps into two horizontal sub-regions, and we synchronously apply the triplet loss and center loss to learn local relative features and local compact features. Finally, we utilize local discriminative features to represent pedestrian. We evaluate LHF on public person Re-ID datasets and prove LHF is meaningful for local feature learning.
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