The task in person re-identification is to match snapshots of people from non-overlapping camera views at different times and places. Intra-class images from different cameras show varying appearances due to variations in illumination

background

occlusion

viewpoint

and pose. Feature representation and metric learning are two major research directions in person re-identification. On the one hand

some studies focus on feature descriptors

which are discriminative for different classes and robust against intra-class variations. On the other hand

numerous metric learning algorithms have achieved good performance in person re-identification. The comparison of all the samples with a single global metric is inappropriate for handling heterogeneous data. Several researchers have proposed local metric learning. However

these methods generally require complicated computations to solve convex optimization problems. To improve the performance of metric learning algorithms and avoid complex computation

this study applies the concept of local metric learning and combines global metric learning algorithms

such as cross-view quadratic discriminant analysis (XQDA) and metric learning by accelerated proximal gradient (MLAPG). In the training stage

all the samples are softly partitioned into several clusters using the Gaussian mixture model (GMM). Local metrics are learned on each cluster using metric learning methods

such as XQDA and MLAPG. Meanwhile

a global metric is also learned for the entire training set. In the testing stage

the posterior probabilities of the testing samples that are aligned to each GMM component are computed. For each pair of samples

the local metrics weighted by their posterior probabilities of GMM components and the global metric weighted by a cross-validated parameter are integrated into the final metric for similarity evaluation. In this manner

we use different metrics to measure various pairs of samples

which is more suitable for heterogeneous data sets. In particular

we also propose an effective local metric learning strategy for MLAPG by modifying the weights of the loss values of the sample pairs in the loss function with the posterior probabilities of the samples aligned to each GMM component. We conduct experiments on three challenging data sets of person re-identification (i.e.

VIPeR

PRID 450S

and QMUL GRID). Experimental results show that the proposed approach achieves better performance compared with traditional global metric learning methods. It performs significantly better on the VIPeR data set

providing more complex variations of backgrounds and clothes than on the other data sets

thereby improving matching accuracy by approximately 2.0%. In addition

we also conduct experiments on different types of feature representations for person re-identification to verify the generalized effectiveness of the proposed method. The matching accuracy is improved by approximately 1.3% to 3.4% with different feature descriptors. This result shows that the proposed approach can improve performance regardless of which feature descriptor is used. We propose a novel framework for integrating global and local metric learning methods by taking advantages of both metric learning approaches. Numerous recent global metric learning approaches can be integrated into the proposed framework to obtain improved performance in the person re-identification problem. Compared with certain local metric learning approaches

the proposed framework integrates global metric learning methods flexibly and effectively. It doesn't require complicated computation unlike other local metric learning approaches. Moreover

the proposed metric learning framework can be applied to many feature representation approaches.