Taichi Distance For Person Re-Identification

Keywords

metric learning; person re-identification; TAICHI distance

Abstract

Metric learning is an important issue in person re-identification, and Mahalanobis-distance based metric learning methods prevail in this field. All of these approaches can be considered as equivalently projecting all samples to a new metric space and calculating the Euclidean distance there. However, the performance of distinguishing similar samples from dissimilar ones via absolute distance is limited. In this paper, we suggest using relative distance instead. We adopt a bi-target perspective. The core idea is to construct a virtual opposite target for each original target. Then, the similarity between a sample and the others is judged by using both the original and opposite targets of the sample. In this way, we propose a bi-target metric method, named TAICHI distance. Considering simplicity and efficiency, we follow the KISSME metric in this paper. Extensive evaluations on challenging datasets confirm the effectiveness of the proposed method.

Publication Date

6-16-2017

Publication Title

ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings

Number of Pages

2052-2056

Document Type

Article; Proceedings Paper

Personal Identifier

scopus

DOI Link

https://doi.org/10.1109/ICASSP.2017.7952517

Socpus ID

85023758877 (Scopus)

Source API URL

https://api.elsevier.com/content/abstract/scopus_id/85023758877

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