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．