Multiple Kernel Fisher Discriminant Metric Learning for Person Re-identification
Abstract
Person re-identification addresses the problem of matching pedestrian images across disjoint camera views. Design of feature descriptor and distance metric learning are the two fundamental tasks in person re-identification. In this paper, we propose a metric learning framework for person re-identification, where the discriminative metric space is learned using Kernel Fisher Discriminant Analysis (KFDA), to simultaneously maximize the inter-class variance as well as minimize the intra-class variance. We derive a Mahalanobis metric induced by KFDA and argue that KFDA is efficient to be applied for metric learning in person re-identification. We also show how the efficiency of KFDA in metric learning can be further enhanced for person re-identification by using two simple yet efficient multiple kernel learning methods. We conduct extensive experiments on three benchmark datasets for person re-identification and demonstrate that the proposed approaches have competitive performance with state-of-the-art methods.
Cite
@article{arxiv.1910.03923,
title = {Multiple Kernel Fisher Discriminant Metric Learning for Person Re-identification},
author = {T M Feroz Ali and Kalpesh K Patel and Rajbabu Velmurugan and Subhasis Chaudhuri},
journal= {arXiv preprint arXiv:1910.03923},
year = {2019}
}