Deep Learning Research With an Expectation-Maximization Model for Person Re-Identification

In existing person re-identification methods based on deep learning, the extraction of good features is still a key step. Some efforts divide the image of a person into multiple parts to extract more detailed information from semantically coherent parts but ignore their correlation with each other....
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Gespeichert in:

Autor*in:	Fei Zhou [verfasserIn] Wenfeng Chen [verfasserIn] Yani Xiao [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2020

Schlagwörter:	Person re-identification deep learning attention non-local expectation maximization Batch DropBlock

Übergeordnetes Werk:	In: IEEE Access - IEEE, 2014, 8(2020), Seite 157762-157772
Übergeordnetes Werk:	volume:8 ; year:2020 ; pages:157762-157772

Links:	Link aufrufen Link aufrufen Link aufrufen Journal toc

DOI / URN:	10.1109/ACCESS.2020.3019100

Katalog-ID:	DOAJ071769277

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520			\|a In existing person re-identification methods based on deep learning, the extraction of good features is still a key step. Some efforts divide the image of a person into multiple parts to extract more detailed information from semantically coherent parts but ignore their correlation with each other. Others adopt self attention to reallocate weights of pixels for learning the association between different regions. This association can improve the accuracy of the person re-identification task, but the features obtained by this type of algorithm have high redundancy, which is not conducive to the expression of feature information. In order to address the above challenges, we propose a feature extraction method based on a novel attention mechanism which combines the expectation maximization (EM) algorithm and non-local operation. We embed the attention module into the ResNet50 backbone network. The attention module captures the correlation between different regional features through non-local operation and then reconstructs these features through the EM algorithm. In addition, we divide the network into a global branch and a local branch, where the global branch extracts the complete features, and the local branch uses the Batch DropBlock method to erase a portion of the features to achieve feature diversity. Finally, extensive experiments validate the superiority of the proposed model for person re-ID over a wide variety of state-of-the-art methods on three large-scale benchmarks, including DukeMTMC-ReID, Market-1501 and CUHK03.
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spelling	10.1109/ACCESS.2020.3019100 doi (DE-627)DOAJ071769277 (DE-599)DOAJ8a596c4e6b9a49f294945b821f18650b DE-627 ger DE-627 rakwb eng TK1-9971 Fei Zhou verfasserin aut Deep Learning Research With an Expectation-Maximization Model for Person Re-Identification 2020 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier In existing person re-identification methods based on deep learning, the extraction of good features is still a key step. Some efforts divide the image of a person into multiple parts to extract more detailed information from semantically coherent parts but ignore their correlation with each other. Others adopt self attention to reallocate weights of pixels for learning the association between different regions. This association can improve the accuracy of the person re-identification task, but the features obtained by this type of algorithm have high redundancy, which is not conducive to the expression of feature information. In order to address the above challenges, we propose a feature extraction method based on a novel attention mechanism which combines the expectation maximization (EM) algorithm and non-local operation. We embed the attention module into the ResNet50 backbone network. The attention module captures the correlation between different regional features through non-local operation and then reconstructs these features through the EM algorithm. In addition, we divide the network into a global branch and a local branch, where the global branch extracts the complete features, and the local branch uses the Batch DropBlock method to erase a portion of the features to achieve feature diversity. Finally, extensive experiments validate the superiority of the proposed model for person re-ID over a wide variety of state-of-the-art methods on three large-scale benchmarks, including DukeMTMC-ReID, Market-1501 and CUHK03. Person re-identification deep learning attention non-local expectation maximization Batch DropBlock Electrical engineering. Electronics. Nuclear engineering Wenfeng Chen verfasserin aut Yani Xiao verfasserin aut In IEEE Access IEEE, 2014 8(2020), Seite 157762-157772 (DE-627)728440385 (DE-600)2687964-5 21693536 nnns volume:8 year:2020 pages:157762-157772 https://doi.org/10.1109/ACCESS.2020.3019100 kostenfrei https://doaj.org/article/8a596c4e6b9a49f294945b821f18650b kostenfrei https://ieeexplore.ieee.org/document/9174970/ kostenfrei https://doaj.org/toc/2169-3536 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 8 2020 157762-157772
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allfieldsSound	10.1109/ACCESS.2020.3019100 doi (DE-627)DOAJ071769277 (DE-599)DOAJ8a596c4e6b9a49f294945b821f18650b DE-627 ger DE-627 rakwb eng TK1-9971 Fei Zhou verfasserin aut Deep Learning Research With an Expectation-Maximization Model for Person Re-Identification 2020 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier In existing person re-identification methods based on deep learning, the extraction of good features is still a key step. Some efforts divide the image of a person into multiple parts to extract more detailed information from semantically coherent parts but ignore their correlation with each other. Others adopt self attention to reallocate weights of pixels for learning the association between different regions. This association can improve the accuracy of the person re-identification task, but the features obtained by this type of algorithm have high redundancy, which is not conducive to the expression of feature information. In order to address the above challenges, we propose a feature extraction method based on a novel attention mechanism which combines the expectation maximization (EM) algorithm and non-local operation. We embed the attention module into the ResNet50 backbone network. The attention module captures the correlation between different regional features through non-local operation and then reconstructs these features through the EM algorithm. In addition, we divide the network into a global branch and a local branch, where the global branch extracts the complete features, and the local branch uses the Batch DropBlock method to erase a portion of the features to achieve feature diversity. Finally, extensive experiments validate the superiority of the proposed model for person re-ID over a wide variety of state-of-the-art methods on three large-scale benchmarks, including DukeMTMC-ReID, Market-1501 and CUHK03. Person re-identification deep learning attention non-local expectation maximization Batch DropBlock Electrical engineering. Electronics. Nuclear engineering Wenfeng Chen verfasserin aut Yani Xiao verfasserin aut In IEEE Access IEEE, 2014 8(2020), Seite 157762-157772 (DE-627)728440385 (DE-600)2687964-5 21693536 nnns volume:8 year:2020 pages:157762-157772 https://doi.org/10.1109/ACCESS.2020.3019100 kostenfrei https://doaj.org/article/8a596c4e6b9a49f294945b821f18650b kostenfrei https://ieeexplore.ieee.org/document/9174970/ kostenfrei https://doaj.org/toc/2169-3536 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 8 2020 157762-157772
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source	In IEEE Access 8(2020), Seite 157762-157772 volume:8 year:2020 pages:157762-157772
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topic_facet	Person re-identification deep learning attention non-local expectation maximization Batch DropBlock Electrical engineering. Electronics. Nuclear engineering
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container_title	IEEE Access
authorswithroles_txt_mv	Fei Zhou @@aut@@ Wenfeng Chen @@aut@@ Yani Xiao @@aut@@
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callnumber-first	T - Technology
author	Fei Zhou
spellingShingle	Fei Zhou misc TK1-9971 misc Person re-identification misc deep learning misc attention misc non-local misc expectation maximization misc Batch DropBlock misc Electrical engineering. Electronics. Nuclear engineering Deep Learning Research With an Expectation-Maximization Model for Person Re-Identification
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topic_title	TK1-9971 Deep Learning Research With an Expectation-Maximization Model for Person Re-Identification Person re-identification deep learning attention non-local expectation maximization Batch DropBlock
topic	misc TK1-9971 misc Person re-identification misc deep learning misc attention misc non-local misc expectation maximization misc Batch DropBlock misc Electrical engineering. Electronics. Nuclear engineering
topic_unstemmed	misc TK1-9971 misc Person re-identification misc deep learning misc attention misc non-local misc expectation maximization misc Batch DropBlock misc Electrical engineering. Electronics. Nuclear engineering
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title	Deep Learning Research With an Expectation-Maximization Model for Person Re-Identification
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title_sort	deep learning research with an expectation-maximization model for person re-identification
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title_auth	Deep Learning Research With an Expectation-Maximization Model for Person Re-Identification
abstract	In existing person re-identification methods based on deep learning, the extraction of good features is still a key step. Some efforts divide the image of a person into multiple parts to extract more detailed information from semantically coherent parts but ignore their correlation with each other. Others adopt self attention to reallocate weights of pixels for learning the association between different regions. This association can improve the accuracy of the person re-identification task, but the features obtained by this type of algorithm have high redundancy, which is not conducive to the expression of feature information. In order to address the above challenges, we propose a feature extraction method based on a novel attention mechanism which combines the expectation maximization (EM) algorithm and non-local operation. We embed the attention module into the ResNet50 backbone network. The attention module captures the correlation between different regional features through non-local operation and then reconstructs these features through the EM algorithm. In addition, we divide the network into a global branch and a local branch, where the global branch extracts the complete features, and the local branch uses the Batch DropBlock method to erase a portion of the features to achieve feature diversity. Finally, extensive experiments validate the superiority of the proposed model for person re-ID over a wide variety of state-of-the-art methods on three large-scale benchmarks, including DukeMTMC-ReID, Market-1501 and CUHK03.
abstractGer	In existing person re-identification methods based on deep learning, the extraction of good features is still a key step. Some efforts divide the image of a person into multiple parts to extract more detailed information from semantically coherent parts but ignore their correlation with each other. Others adopt self attention to reallocate weights of pixels for learning the association between different regions. This association can improve the accuracy of the person re-identification task, but the features obtained by this type of algorithm have high redundancy, which is not conducive to the expression of feature information. In order to address the above challenges, we propose a feature extraction method based on a novel attention mechanism which combines the expectation maximization (EM) algorithm and non-local operation. We embed the attention module into the ResNet50 backbone network. The attention module captures the correlation between different regional features through non-local operation and then reconstructs these features through the EM algorithm. In addition, we divide the network into a global branch and a local branch, where the global branch extracts the complete features, and the local branch uses the Batch DropBlock method to erase a portion of the features to achieve feature diversity. Finally, extensive experiments validate the superiority of the proposed model for person re-ID over a wide variety of state-of-the-art methods on three large-scale benchmarks, including DukeMTMC-ReID, Market-1501 and CUHK03.
abstract_unstemmed	In existing person re-identification methods based on deep learning, the extraction of good features is still a key step. Some efforts divide the image of a person into multiple parts to extract more detailed information from semantically coherent parts but ignore their correlation with each other. Others adopt self attention to reallocate weights of pixels for learning the association between different regions. This association can improve the accuracy of the person re-identification task, but the features obtained by this type of algorithm have high redundancy, which is not conducive to the expression of feature information. In order to address the above challenges, we propose a feature extraction method based on a novel attention mechanism which combines the expectation maximization (EM) algorithm and non-local operation. We embed the attention module into the ResNet50 backbone network. The attention module captures the correlation between different regional features through non-local operation and then reconstructs these features through the EM algorithm. In addition, we divide the network into a global branch and a local branch, where the global branch extracts the complete features, and the local branch uses the Batch DropBlock method to erase a portion of the features to achieve feature diversity. Finally, extensive experiments validate the superiority of the proposed model for person re-ID over a wide variety of state-of-the-art methods on three large-scale benchmarks, including DukeMTMC-ReID, Market-1501 and CUHK03.
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title_short	Deep Learning Research With an Expectation-Maximization Model for Person Re-Identification
url	https://doi.org/10.1109/ACCESS.2020.3019100 https://doaj.org/article/8a596c4e6b9a49f294945b821f18650b https://ieeexplore.ieee.org/document/9174970/ https://doaj.org/toc/2169-3536
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