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Regularized Bayesian Metric Learning for Person Re-identification

  • Venice Erin Liong
  • Jiwen Lu
  • Yongxin GeEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8927)

Abstract

Person re-identification across disjoint cameras has attracted increasing interest in computer vision due to its wide potential applications in visual surveillance. In this paper, we propose a new regularized Bayesian metric learning (RBML) method for person re-identification. While numerous metric learning methods have been proposed for person re-identification in recent years, most of them suffer from the small sample size (SSS) problem because there are not enough training samples in most practical person re-identification systems, so that the within-class and between-class variations can be well estimated to learn the distance metric. To address this, we propose a RBML method to model and regulate the eigen-spectrums of these two covariance matrices in a parametric manner, so that discriminative information can be better exploited. Experimental results on three widely used datasets demonstrate the advantage of our proposed RBML over the state-of-the-art person re-identification methods.

Keywords

Person re-identification Metric learning Regularization 

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Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  1. 1.Advanced Digital Sciences CenterSingaporeSingapore
  2. 2.Key Laboratory of Dependable Service Computing in Cyber Physical Society Ministry of EducationChongqingChina
  3. 3.School of Software EngineeringChongqing UniversityChongqingChina

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