Integrated Global And Local Multi-Metric Learning For Person Re-Identification

Person re-identification is an important research subject of computer vision and intelligent surveillance,the task of which is to match snapshots of people from non-overlapping camera views at different time and places.Intra-class images from different cameras show different appearances due to variations of illumination,backgrounds,occlusions,viewpoints and poses.Feature representation and metric learning are two major research directions of person re-identification.On one hand,some researches focus on feature descriptors which are discriminative for different classes and robust against intra-class variations.On the other hand,many metric learning algorithms have achieved good performance on person re-identification.However,comparing all of the samples with a single global metric is inappropriate to handle heterogeneous dataset.Some researchers propose local metric learning.But these methods cannot be directly used for person re-identification due to some research challenges.In order to improve the matching performance on heterogeneous data,a series of novel multi-metric learning approaches which combine the idea of local metric learning and some existing global metric learning algorithms are proposed.The proposed methods improve the matching accuracy and can be widely used on person re-identification.The specific research works are as follows:1)A multi-metric learning approach based on Gaussian mixture model(GMM)and positive semi-definite(PSD)constraint is proposed.The approach is aimed at minimizing a log logistic loss function of the training set.GMM is used to fit the distribution of training samples.The posterior probabilities of each Gaussian component are used to improve the weights of different sample pairs' loss in the entire loss function.Each component is corresponding to a different optimizing objective function with PSD constraint for the metric matrix.Accelerated proximal gradient algorithm is used to solve the optimizing problems.And multiple metrics are obtained,which are used to compute multiple similaritiy distances to be combined.2)The idea of local metric learning is used on the person re-identification problems by partitioning the entire training set into multiple local subsets with overlapping samples.It successfully overcomes the research challenges and difficulties of using local metric learning on person re-identification issue.3)Local metric learning is combined with some recently proposed global metric learning approaches such as cross-view quadratic discriminant analysis(XQDA)and a PSD constrained asymmetric metric learning approach termed as MLAPG.In the training stage,all of the samples are partitioned into several clusters by GMM softly.Also,the dividing strategy proposed in 2)is used.Local metrics are learned on each subset respectively by existing metric learning methods.Meanwhile,a global metric is also learned on the entire training set.In the testing stage,for each pair of samples,the local metrics weighted by their posterior probabilities aligned to different GMM components and the global metric weighted by a cross-validated parameter are integrated into the final metric for similarity evaluation.The experimental results on three challenging datasets of person re-identification(VIPeR,PRID 450 S and Market-1501)show the effectiveness of the proposed multi-metric learning,local metric learning and integrated global-local metric learning approaches.Especially,on VIPeR dataset with large variations of backgrounds and clothes,the proposed approaches perform much better.The matching accuracy achieves at more than 42.0%,over 2.0% increase comparing to the exsiting global metric learning methods.The proposed methods perform better than or equal to the state-of-the-art methods.In addition,the approaches could be generalized to different scenarios,as they can improve the performance regardless of using what feature descriptors.