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

TL;DR

This paper introduces NK3ML, a novel metric learning framework that effectively addresses the small sample size problem in person re-identification, significantly improving accuracy by leveraging high-dimensional nullspace and kernel methods.

Contribution

The paper presents a new metric learning approach using nullspace and kernel maximum margin criteria, achieving superior performance in person re-identification tasks.

Findings

Achieves 99.8% rank-1 accuracy on VIPeR dataset

Outperforms all existing methods on four benchmark datasets

Effectively addresses small sample size problem in person re-identification

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%.