DocumentCode :
3748861
Title :
Multi-Scale Learning for Low-Resolution Person Re-Identification
Author :
Xiang Li;Wei-Shi Zheng;Xiaojuan Wang;Tao Xiang;Shaogang Gong
Author_Institution :
Sch. of Inf. Sci. &
fYear :
2015
Firstpage :
3765
Lastpage :
3773
Abstract :
In real world person re-identification (re-id), images of people captured at very different resolutions from different locations need be matched. Existing re-id models typically normalise all person images to the same size. However, a low-resolution (LR) image contains much less information about a person, and direct image scaling and simple size normalisation as done in conventional re-id methods cannot compensate for the loss of information. To solve this LR person re-id problem, we propose a novel joint multi-scale learning framework, termed joint multi-scale discriminant component analysis (JUDEA). The key component of this framework is a heterogeneous class mean discrepancy (HCMD) criterion for cross-scale image domain alignment, which is optimised simultaneously with discriminant modelling across multiple scales in the joint learning framework. Our experiments show that the proposed JUDEA framework outperforms existing representative re-id methods as well as other related LR visual matching models applied for the LR person re-id problem.
Keywords :
"Image resolution","Feature extraction","Cameras","Face","Training","Measurement","Adaptation models"
Publisher :
ieee
Conference_Titel :
Computer Vision (ICCV), 2015 IEEE International Conference on
Electronic_ISBN :
2380-7504
Type :
conf
DOI :
10.1109/ICCV.2015.429
Filename :
7410786
Link To Document :
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