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Found 4 papers, 1 papers with code
Multiple Kernel Fisher Discriminant Metric Learning for Person Re-identification
We derive a Mahalanobis metric induced by KFDA and argue that KFDA is efficient to be applied for metric learning in person re-identification.
A Semi-Supervised Maximum Margin Metric Learning Approach for Small Scale Person Re-identification
In this paper, we propose a semi-supervised metric learning approach that can utilize information in unlabelled data with the help of a few labelled training samples.
Cross-View Kernel Similarity Metric Learning Using Pairwise Constraints for Person Re-identification
Person re-identification is the task of matching pedestrian images across non-overlapping cameras.
Maximum Margin Metric Learning Over Discriminative Nullspace for Person Re-identification
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.
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