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Open AccessFeature PaperArticle

Learning Large Margin Multiple Granularity Features with an Improved Siamese Network for Person Re-Identification

1
School of Telecommunication and Information Engineering, Xi’an University of Posts and Telecommunications, Xi’an 710121, China
2
Ministry of Public Security Key Laboratory of Electronic Information Application Technology for Scene Investigation, Xi’an 710121, China
3
Faculty of Science & Technology, Federation University Australia, Gippsland, VIC 3842, Australia
*
Author to whom correspondence should be addressed.
Symmetry 2020, 12(1), 92; https://doi.org/10.3390/sym12010092
Received: 1 December 2019 / Revised: 25 December 2019 / Accepted: 25 December 2019 / Published: 3 January 2020
(This article belongs to the Special Issue Recent Advances in Social Data and Artificial Intelligence 2019)
Person re-identification (Re-ID) is a non-overlapping multi-camera retrieval task to match different images of the same person, and it has become a hot research topic in many fields, such as surveillance security, criminal investigation, and video analysis. As one kind of important architecture for person re-identification, Siamese networks usually adopt standard softmax loss function, and they can only obtain the global features of person images, ignoring the local features and the large margin for classification. In this paper, we design a novel symmetric Siamese network model named Siamese Multiple Granularity Network (SMGN), which can jointly learn the large margin multiple granularity features and similarity metrics for person re-identification. Firstly, two branches for global and local feature extraction are designed in the backbone of the proposed SMGN model, and the extracted features are concatenated together as multiple granularity features of person images. Then, to enhance their discriminating ability, the multiple channel weighted fusion (MCWF) loss function is constructed for the SMGN model, which includes the verification loss and identification loss of the training image pair. Extensive comparative experiments on four benchmark datasets (CUHK01, CUHK03, Market-1501 and DukeMTMC-reID) show the effectiveness of our proposed method and its performance outperforms many state-of-the-art methods. View Full-Text
Keywords: person re-identification; multiple granularity features; Siamese Multiple Granularity Network; multi-channel weighted fusion loss person re-identification; multiple granularity features; Siamese Multiple Granularity Network; multi-channel weighted fusion loss
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Li, D.-X.; Fei, G.-Y.; Teng, S.-W. Learning Large Margin Multiple Granularity Features with an Improved Siamese Network for Person Re-Identification. Symmetry 2020, 12, 92.

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