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• W2950281010 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%." @default.
• W2950281010 created "2019-06-27" @default.
• W2950281010 creator A5016405213 @default.
• W2950281010 creator A5032100914 @default.
• W2950281010 date "2018-07-28" @default.
• W2950281010 modified "2023-09-27" @default.
• W2950281010 title "Maximum Margin Metric Learning Over Discriminative Nullspace for Person Re-identification" @default.
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