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Discriminative deep transfer metric learning for cross-scenario person re-identification
- Source :
- Journal of Electronic Imaging. 27:1
- Publication Year :
- 2018
- Publisher :
- SPIE-Intl Soc Optical Eng, 2018.
-
Abstract
- A discriminative deep transfer metric learning method called DDTML is proposed for cross-scenario person re-identification (Re-ID). To develop the Re-ID model in a new scenario, a large number of pairwise cross-camera-view person images are deemed necessary. However, this work is very expensive due to both monetary cost and labeling time. In order to solve this problem, a DDTML for cross-scenario Re-ID is proposed using the transferring data in other scenarios to help build a Re-ID model in a new scenario. Specifically, to measure distribution difference across scenarios, a maximum mean discrepancy based on class distribution called MMDCD is proposed by embedding the discriminative information of data into the concept of the maximum mean discrepancy. Unlike most metric learning methods, which usually learn a linear distance to project data into the feature space, DDTML uses a deep neural network to develop the multilayers nonlinear transformations for learning the nonlinear distance metric, while DDTML transfers discriminative information from the source domain to the target domain. By bedding the MMDCD criteria, DDTML minimizes the distribution divergence between the source domain and the target domain. Experimental results on widely used Re-ID datasets show the effectiveness of the proposed classifiers.
- Subjects :
- 050210 logistics & transportation
Artificial neural network
Computer science
business.industry
Deep learning
Feature vector
05 social sciences
02 engineering and technology
Machine learning
computer.software_genre
Atomic and Molecular Physics, and Optics
Computer Science Applications
Data modeling
Domain (software engineering)
Discriminative model
0502 economics and business
Metric (mathematics)
0202 electrical engineering, electronic engineering, information engineering
020201 artificial intelligence & image processing
Artificial intelligence
Electrical and Electronic Engineering
Divergence (statistics)
business
computer
Subjects
Details
- ISSN :
- 10179909
- Volume :
- 27
- Database :
- OpenAIRE
- Journal :
- Journal of Electronic Imaging
- Accession number :
- edsair.doi...........636369665bfa59349c4cd59d165293c3