CORE
🇺🇦
make metadata, not war
Services
Services overview
Explore all CORE services
Access to raw data
API
Dataset
FastSync
Content discovery
Recommender
Discovery
OAI identifiers
OAI Resolver
Managing content
Dashboard
Bespoke contracts
Consultancy services
Support us
Support us
Membership
Sponsorship
Community governance
Advisory Board
Board of supporters
Research network
About
About us
Our mission
Team
Blog
FAQs
Contact us
research
Unsupervised learning of generative topic saliency for person re-identification
Authors
S Gong
H Wang
T Xiang
Publication date
1 January 2014
Publisher
Doi
Cite
Abstract
(c) 2014. The copyright of this document resides with its authors. It may be distributed unchanged freely in print or electronic forms.© 2014. The copyright of this document resides with its authors. Existing approaches to person re-identification (re-id) are dominated by supervised learning based methods which focus on learning optimal similarity distance metrics. However, supervised learning based models require a large number of manually labelled pairs of person images across every pair of camera views. This thus limits their ability to scale to large camera networks. To overcome this problem, this paper proposes a novel unsupervised re-id modelling approach by exploring generative probabilistic topic modelling. Given abundant unlabelled data, our topic model learns to simultaneously both (1) discover localised person foreground appearance saliency (salient image patches) that are more informative for re-id matching, and (2) remove busy background clutters surrounding a person. Extensive experiments are carried out to demonstrate that the proposed model outperforms existing unsupervised learning re-id methods with significantly simplified model complexity. In the meantime, it still retains comparable re-id accuracy when compared to the state-of-the-art supervised re-id methods but without any need for pair-wise labelled training data
Similar works
Full text
Open in the Core reader
Download PDF
Available Versions
CiteSeerX
See this paper in CORE
Go to the repository landing page
Download from data provider
oai:CiteSeerX.psu:10.1.1.676.8...
Last time updated on 29/10/2017
Crossref
See this paper in CORE
Go to the repository landing page
Download from data provider
info:doi/10.5244%2Fc.28.48
Last time updated on 05/06/2019
Supporting member
Queen Mary Research Online
See this paper in CORE
Go to the repository landing page
Download from data provider
oai:qmro.qmul.ac.uk:123456789/...
Last time updated on 05/04/2016