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Erschienen in: Neural Processing Letters 3/2019

12.03.2018

QRKISS: A Two-Stage Metric Learning via QR-Decomposition and KISS for Person Re-Identification

verfasst von: Cairong Zhao, Yipeng Chen, Zhihua Wei, Duoqian Miao, Xinjian Gu

Erschienen in: Neural Processing Letters | Ausgabe 3/2019

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Abstract

Person re-identification is a challenging task in the field of intelligent video surveillance because there are wide variations between pedestrian images. As a classical metric learning method, Keep It Simple and Straightforward (KISS) has shown good performance for person re-identification. However, when the dimension of data is high, the KISS method may perform poorly because of small sample size problem. A common solution to this problem is to apply dimensionality reduction technologies to original data before the KISS metric learning, such as Principal Component Analysis (PCA) and Linear Discriminant Analysis (LDA). In this paper, to learn a discriminant and robust metric, we propose a novel two-stage metric learning via QR-Decomposition and KISS, named QRKISS. The first stage of QRKISS is to project original data into a lower dimensional space by QR decomposition. In this lower dimensional space, the trace of the covariance matrix of interpersonal differences can reach maximum. Based on KISS method, the second stage of QRKISS obtains a Mahalanobis matrix in the low-dimension space. We conduct thorough validation experiments on the VIPeR, PRID 450S and CUHK01 datasets, which demonstrate that QRKISS method is better than other KISS-based metric learning methods and achieves state-of-the-art performance.

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Metadaten
Titel
QRKISS: A Two-Stage Metric Learning via QR-Decomposition and KISS for Person Re-Identification
verfasst von
Cairong Zhao
Yipeng Chen
Zhihua Wei
Duoqian Miao
Xinjian Gu
Publikationsdatum
12.03.2018
Verlag
Springer US
Erschienen in
Neural Processing Letters / Ausgabe 3/2019
Print ISSN: 1370-4621
Elektronische ISSN: 1573-773X
DOI
https://doi.org/10.1007/s11063-018-9820-x

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