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

T M Feroz Ali, Subhasis Chaudhuri ; Proceedings of the European Conference on Computer Vision (ECCV), 2018, pp. 122-138

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%. This is the first time in the literature for person re-identification, a method competes to human level perfection.

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[bibtex]
@InProceedings{Ali_2018_ECCV,
author = {M Feroz Ali, T and Chaudhuri, Subhasis},
title = {Maximum Margin Metric Learning Over Discriminative Nullspace for Person Re-identification},
booktitle = {Proceedings of the European Conference on Computer Vision (ECCV)},
month = {September},
year = {2018}
}