Deep Parts Similarity Learning for Person Re-Identification

María José Gómez-Silva, José María Armingol, Arturo de la Escalera

2018

Abstract

Measuring the appearance similarity in Person Re-Identification is a challenging task which not only requires the selection of discriminative visual descriptors but also their optimal combination. This paper presents a unified learning framework composed by Deep Convolutional Neural Networks to simultaneously and automatically learn the most salient features for each one of nine different body parts and their best weighting to form a person descriptor. Moreover, to cope with the cross-view variations, these have been coded in a Mahalanobis Matrix, in an adaptive process, also integrated into the learning framework, which takes advantage of the discriminative information given by the dataset labels to analyse the data structure. The effectiveness of the proposed approach, named Deep Parts Similarity Learning (DPSL), has been evaluated and compared with other state-of-the-art approaches over the challenging PRID2011 dataset.

Paper Citation

in Harvard Style

Gómez-Silva M., Armingol J. and Escalera A. (2018). Deep Parts Similarity Learning for Person Re-Identification. In Proceedings of the 13th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2018) - Volume 5: VISAPP; ISBN 978-989-758-290-5, SciTePress, pages 419-428. DOI: 10.5220/0006539604190428

in Bibtex Style

@conference{visapp18,
author={María José Gómez-Silva and José María Armingol and Arturo de la Escalera},
title={Deep Parts Similarity Learning for Person Re-Identification},
booktitle={Proceedings of the 13th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2018) - Volume 5: VISAPP},
year={2018},
pages={419-428},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0006539604190428},
isbn={978-989-758-290-5},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 13th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2018) - Volume 5: VISAPP
TI - Deep Parts Similarity Learning for Person Re-Identification
SN - 978-989-758-290-5
AU - Gómez-Silva M.
AU - Armingol J.
AU - Escalera A.
PY - 2018
SP - 419
EP - 428
DO - 10.5220/0006539604190428
PB - SciTePress