针对传统欧氏距离在特征相似性度量中存在区分能力弱的缺陷，提出了基于加权欧氏距离度量的目标再识别算法． 首先，针对现有目标再识别算法中目标分割易受衣着和背景颜色干扰的缺陷以及忽略人体头部特征的现象，提出了一种简单的比例分割方法，即根据 VIPeＲ 和 i-LIDS 数据集上目标各部件的比例统计将目标按比例分割成 3 部分． 然后提取各部件的多种互补特征来增加其对光照变化等因素的鲁棒性． 在部件特征描述过程中，文中提出了以显著性因子为权重的显著性局部二值模式( SLBP) 特征来增加局部二值模式( LBP) 特征对目标显著性的描述． 最后综合各部件的相似性度量结果来判断目标是否匹配． 在 VIPeＲ 和 i-LIDS 数据集上的对比实验结果显示，文中算法的目标再识别准确率优于其他算法．

As the traditional Euclidean distance has a weak distinctive ability in the feature similarity measure，an
object re-identification algorithm based on the weighted Euclidean distance metric is proposed． First，aiming at the problems of the existing object re-identification algorithm，which are that the object segmentation is sensitive to clothing and background color and the human head information is ignored，a simple segmentation method is proposed，which divides a person into three parts according to the statistics of the proportion of each part in VIPeR and i-LIDS data-sets． Then，various complementary features of each part are extracted to improve the robustness of the proposed algorithm to illumination changes and other factors． A significant local binary pattern (SLBP) with a significant factor as the weight is proposed to increase the description ability of the local binary pattern (LBP) to the significance of the object in the part feature description process． Finally，the comprehensive result of the similarity measure of each part is used to determine whether the object is matched． The results of comparative experiments on VIPeR and i-LIDS datasets show that the proposed algorithm is superior to other algorithms in terms of accuracy．