Paper Info
Title
Joint Feature and Similarity Deep Learning for Vehicle Re-identification.
Abstract
In this paper, a joint feature and similarity deep learning (JFSDL) method for vehicle reidentification is proposed. The proposed JFSDL method applies a siamese deep network to extract deep learning features for an input vehicle image pair simultaneously. The siamese deep network is learned under the joint identification and verification supervision. The joint identification and verification supervision is realized by linearly combining two softmax functions and one hybrid similarity learning function. Moreover, based on the hybrid similarity learning function, the similarity score between the input vehicle image pair is also obtained by simultaneously projecting the element-wise absolute difference and multiplication of the corresponding deep learning feature pair with a group of learned weight coefficients. Extensive experiments show that the proposed JFSDL method is superior to multiple state-of-the-art vehicle re-identification methods on both the VehicleID and VeRi data sets.
Year
DOI
Venue
2018
10.1109/ACCESS.2018.2862382
IEEE ACCESS
Keywords
Field
DocType
Vehicle re-identification,feature representation,similarity learning,deep learning
Similarity learning,Facial recognition system,Pattern recognition,Softmax function,Computer science,Euclidean distance,Feature extraction,Multiplication,Artificial intelligence,Deep learning,Absolute difference,Distributed computing
Journal
Volume
ISSN
Citations 
6
2169-3536
2
PageRank 
References 
Authors
0.40
0
6
Name
Order
Citations
PageRank
Jianqing Zhu1733.91
Huanqiang Zeng239536.94
Yongzhao Du3115.61
Zhen Lei43613157.95
Lixin Zheng571.16
Canhui Cai633927.80