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Deep Hybrid Similarity Learning for Person Re-Identification.
- Source :
- IEEE Transactions on Circuits & Systems for Video Technology; Nov2018, Vol. 28 Issue 11, p3183-3193, 11p
- Publication Year :
- 2018
-
Abstract
- Person re-identification (Re-ID) aims to match person images captured from two non-overlapping cameras. In this paper, a deep hybrid similarity learning (DHSL) method for person Re-ID based on a convolution neural network (CNN) is proposed. In our approach, a light CNN learning feature pair for the input image pair is simultaneously extracted. Then, both the elementwise absolute difference and multiplication of the CNN learning feature pair are calculated. Finally, a hybrid similarity function is designed to measure the similarity between the feature pair, which is realized by learning a group of weight coefficients to project the elementwise absolute difference and multiplication into a similarity score. Consequently, the proposed DHSL method is able to reasonably assign complexities of feature learning and metric learning in a CNN, so that the performance of person Re-ID is improved. Experiments on three challenging person Re-ID databases, QMUL GRID, VIPeR, and CUHK03, illustrate that the proposed DHSL method is superior to multiple state-of-the-art person Re-ID methods. [ABSTRACT FROM AUTHOR]
Details
- Language :
- English
- ISSN :
- 10518215
- Volume :
- 28
- Issue :
- 11
- Database :
- Complementary Index
- Journal :
- IEEE Transactions on Circuits & Systems for Video Technology
- Publication Type :
- Academic Journal
- Accession number :
- 132893971
- Full Text :
- https://doi.org/10.1109/TCSVT.2017.2734740