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Manifold ranking weighted local maximal occurrence descriptor for person re-identification

Manifold ranking weighted local maximal occurrence descriptor for person re-identification

Wang, Foqin, Zhang, Xuehan, Ma, Jixin, Tang, Jin and Zheng, Aihua (2017) Manifold ranking weighted local maximal occurrence descriptor for person re-identification. In: 2017 IEEE 15th International Conference on Software Engineering Research, Management and Applications (SERA). IEEE, pp. 111-114. ISBN 978-1509057566 (doi:https://doi.org/10.1109/SERA.2017.7965715)

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Abstract

Person re-identification is an important task of matching pedestrians across non-overlapping camera views. In this paper, we exploit a weighted feature descriptor for person re-identification.We firstly compute the weights on the superpixel level via graph-based manifold ranking algorithm, then integrate the computed weights into a patch-based feature descriptor, named local maximal occurrence. Finally, the weighted descriptors are fed into a top-push distance learning to mitigate the cross-view gaps. We evaluate the proposed method on three benchmark datasets iLIDS-VID, PRID 450S and VIPeR. The promising experimental results demonstrate the effectiveness of the proposed method comparing with the state-of-the-arts.

Item Type: Conference Proceedings
Title of Proceedings: 2017 IEEE 15th International Conference on Software Engineering Research, Management and Applications (SERA)
Uncontrolled Keywords: person re-identification, manifold ranking, local maximal occurrence, weighted descriptor
Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Faculty / Department / Research Group: Faculty of Architecture, Computing & Humanities
Faculty of Architecture, Computing & Humanities > Centre for Computer & Computational Science
Faculty of Architecture, Computing & Humanities > Department of Computing & Information Systems
Last Modified: 13 Aug 2019 16:02
Selected for GREAT 2016: None
Selected for GREAT 2017: None
Selected for GREAT 2018: None
Selected for GREAT 2019: GREAT 6
URI: http://gala.gre.ac.uk/id/eprint/24322

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