中文摘要：

行人再识别是一个有着非常重要现实意义的研究问题,它可以应用于刑事侦查、在公共场所中寻找丢失的小孩、个人相册管理以及电子商务等领域.同时由于光照、视角、人的姿态以及背景的变化,同一个人的表观在不同的监控视频中往往变化很大,解决行人再识别问题也非常有挑战性.在设计行人再识别算法时,给定行人图像的特征,考虑到不同的特征分量具有不同的区分能力,学习合适的相似度度量非常重要.度量学习是一类学习相似度度量的主流算法,这些算法通过学习一个马氏距离相似度函数（Mahalanobis Similarity Function,MSF）来估计一对行人图像的相似度.然而MSF只与特征差分空间有关,忽略了一对图像中每个个体的表观特征,对于同一个人在不同场景中很大的表观变化的捕捉能力有限.为了加强相似度函数与每个个体的表观特征的联系,该文提出通过学习一个二次相似度函数（Quadratic Similarity Function,QSF）,来估计一对行人图像的相似度.QSF是MSF的泛化形式,不但描述了一对行人图像的互相关关系,而且关联了一对行人图像的自相关关系,可以更好地捕捉同一个人在不同监控视频中很大的表观变化.为了学习QSF,该文分别从分类和排序的角度出发,设计两种不同的优化目标,提出了两种不同的学习QSF的算法.由行人再识别的公共数据集VIPeR和CUHK的实验表明,这两种不同的算法都可以学习到有效的QSF,识别性能优于已有的行人再识别算法.

英文摘要：

Pedestrian re-identification is a valuable problem that has enormous potential for practical applications like criminals monitoring and investigation,lost children search in the public area,personal photo album management,e-business,to name a few.Meanwhile,it is also very challenging since the appearance of the same person between camera views changes dramatically caused by illumination,viewpoint,pose,and background variations.Given a certain pedestrian feature space,because of the different discriminative ability of different feature components,learning aproper similarity is important for pedestrian re identification.A dominant algorithm to learn a similarity is the metric learning that learns a Mahalanobis Similarity Function（MSF）to estimate the similarity of a pair of pedestrians.However,because MSF only projects a pair of pedestrians into the feature difference space and ignores the appearance of each individual,it is inadequate for modeling complex relationships of appearance variations from different cameras.In this paper,we propose to learn a Quadratic Similarity Function（QSF）that greatly strengthens the modeling ability of the similarity function.QSF is a generalization of MSF,and it not only represents the cross correlation relationship of a pair of pedestrians,but also describes the autocorrelation relationship.So QSF is better to tackle large appearance variations of the same pedestrians than MSF.To learn a QSF,inspired from the classification perspective and the ranking perspective,we design two algorithms with different optimizing object functions.Experiments on the VIPeR dataset and the CUHK campus dataset show that both algorithms are effective to learn a QSF and their performance outperforms the previous algorithms.