Paper Info
Title
Gabor-LBP Based Region Covariance Descriptor for Person Re-identification
Abstract
Person re-identification is an important problem in computer vision, which involves matching appearance of individuals between non-overlapping camera views. In this paper we present a novel appearance-based method for person re-identification problem. Color feature, Gabor, local binary pattern (LBP) are utilized to form a covariance descriptor to handle the difficulties such as varying illumination, viewpoint angle and non-rigid body, then distances of these features are computed to match these individuals. Experimental results over the challenging dataset VIPeR demonstrate that our method obtains competitive performance.
Year
DOI
Venue
2011
10.1109/ICIG.2011.40
ICIG
Keywords
Field
DocType
competitive performance,region covariance descriptor,image matching,covariance analysis,person re-identification problem,color feature,local binary pattern,viewpoint angle,gabor,individual appearance matching,person re-identification,feature extraction,covariance descriptor,cameras,color features,object recognition,computer vision,important problem,nonoverlapping camera view,gabor-lbp based region covariance descriptor,novel appearance-based method,image colour analysis,challenging dataset,lighting,covariance matrix,face recognition
Computer vision,Facial recognition system,Pattern recognition,Image matching,Computer science,Local binary patterns,Feature extraction,Artificial intelligence,Covariance matrix,Analysis of covariance,Cognitive neuroscience of visual object recognition,Covariance
Conference
ISBN
Citations 
PageRank 
978-0-7695-4541-7
25
0.99
References 
Authors
11
2
Name
Order
Citations
PageRank
Ying Zhang116325.25
Shutao Li22594139.10