Person re-identification(Re-ID) is integral to intelligent monitoring systems.However,due to the variability in viewing angles and illumination,it is easy to cause visual ambiguities,affecting the accuracy of person r...Person re-identification(Re-ID) is integral to intelligent monitoring systems.However,due to the variability in viewing angles and illumination,it is easy to cause visual ambiguities,affecting the accuracy of person re-identification.An approach for person re-identification based on feature mapping space and sample determination is proposed.At first,a weight fusion model,including mean and maximum value of the horizontal occurrence in local features,is introduced into the mapping space to optimize local features.Then,the Gaussian distribution model with hierarchical mean and covariance of pixel features is introduced to enhance feature expression.Finally,considering the influence of the size of samples on metric learning performance,the appropriate metric learning is selected by sample determination method to further improve the performance of person re-identification.Experimental results on the VIPeR,PRID450 S and CUHK01 datasets demonstrate that the proposed method is better than the traditional methods.展开更多
基于传统面向对象方法,提出了一种基于最优特征空间的损毁建筑物信息提取方法。采用ESP(Estimate of Scale Parameter)工具对图像进行最优尺度分割,之后通过选取样本,计算各类地物距离矩阵和最小分离距离寻求最优特征空间,最后运用最优...基于传统面向对象方法,提出了一种基于最优特征空间的损毁建筑物信息提取方法。采用ESP(Estimate of Scale Parameter)工具对图像进行最优尺度分割,之后通过选取样本,计算各类地物距离矩阵和最小分离距离寻求最优特征空间,最后运用最优特征空间对震后损毁建筑物影像进行提取实验,在QuickBird影像中提取总体精度达到了83.1%, Kappa系数达到了0.813,在无人机影像中提取总体精度为92.9%, Kappa系数达到了0.940。本文建立的提取方法与传统分类决策树方法相比,其提取精度和效率都有较大提高,在损毁建筑物信息提取方面具有较好的推广价值。展开更多
基金Supported by the National Natural Science Foundation of China (No.61976080)the Science and Technology Key Project of Science and Technology Department of Henan Province (No.212102310298)+1 种基金the Innovation and Quality Improvement Project for Graduate Education of Henan University (No.SYL20010101)the Academic Degress&Graduate Education Reform Project of Henan Province (2021SJLX195Y)。
文摘Person re-identification(Re-ID) is integral to intelligent monitoring systems.However,due to the variability in viewing angles and illumination,it is easy to cause visual ambiguities,affecting the accuracy of person re-identification.An approach for person re-identification based on feature mapping space and sample determination is proposed.At first,a weight fusion model,including mean and maximum value of the horizontal occurrence in local features,is introduced into the mapping space to optimize local features.Then,the Gaussian distribution model with hierarchical mean and covariance of pixel features is introduced to enhance feature expression.Finally,considering the influence of the size of samples on metric learning performance,the appropriate metric learning is selected by sample determination method to further improve the performance of person re-identification.Experimental results on the VIPeR,PRID450 S and CUHK01 datasets demonstrate that the proposed method is better than the traditional methods.
