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Maximum Margin Metric Learning over Discriminative Nullspace for Person Re-identification

  • T. M. Feroz AliEmail author
  • Subhasis Chaudhuri
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11217)

Abstract

In this paper we propose a novel metric learning framework called Nullspace Kernel Maximum Margin Metric Learning (NK3ML) which efficiently addresses the small sample size (SSS) problem inherent in person re-identification and offers a significant performance gain over existing state-of-the-art methods. Taking advantage of the very high dimensionality of the feature space, the metric is learned using a maximum margin criterion (MMC) over a discriminative nullspace where all training sample points of a given class map onto a single point, minimizing the within class scatter. A kernel version of MMC is used to obtain a better between class separation. Extensive experiments on four challenging benchmark datasets for person re-identification demonstrate that the proposed algorithm outperforms all existing methods. We obtain 99.8% rank-1 accuracy on the most widely accepted and challenging dataset VIPeR, compared to the previous state of the art being only 63.92%.

Keywords

Person re-identification Metric learning Small sample size problem 

Notes

Acknowledgement

This research work is supported by Ministry of Electronics and Information Technology (MeitY), Government of India, under Visvesvaraya Ph.D. Scheme.

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Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  1. 1.Indian Institute of Technology BombayMumbaiIndia

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