Each Part Matters: Local Patterns Facilitate Cross-view Geo-localization

Wang, T; Zheng, Z; Yan, C; Zhang, J; Sun, Y; Zhenga, B; Yang, Y

Each Part Matters: Local Patterns Facilitate Cross-view Geo-localization

Wang, T Zheng, Z Yan, C Zhang, J Sun, Y Zhenga, B Yang, Y

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Publisher:: Institute of Electrical and Electronics Engineers
Publication Type:: Journal Article
Citation:: IEEE Transactions on Circuits and Systems for Video Technology, 2022, 32, (2), pp. 867-879
Issue Date:: 2022-01-01

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Field	Value	Language
dc.contributor.author	Wang, T
dc.contributor.author	Zheng, Z
dc.contributor.author	Yan, C
dc.contributor.author	Zhang, J
dc.contributor.author	Sun, Y
dc.contributor.author	Zhenga, B
dc.contributor.author	Yang, Y https://orcid.org/0000-0002-0512-880X
dc.date.accessioned	2023-03-23T01:01:21Z
dc.date.available	2023-03-23T01:01:21Z
dc.date.issued	2022-01-01
dc.identifier.citation	IEEE Transactions on Circuits and Systems for Video Technology, 2022, 32, (2), pp. 867-879
dc.identifier.issn	1051-8215
dc.identifier.issn	1558-2205
dc.identifier.uri	http://hdl.handle.net/10453/168127
dc.description.abstract	Cross-view geo-localization is to spot images of the same geographic target from different platforms, e.g. , drone-view cameras and satellites. It is challenging in the large visual appearance changes caused by extreme viewpoint variations. Existing methods usually concentrate on mining the fine-grained feature of the geographic target in the image center, but underestimate the contextual information in neighbor areas. In this work, we argue that neighbor areas can be leveraged as auxiliary information, enriching discriminative clues for geo-localization. Specifically, we introduce a simple and effective deep neural network, called Local Pattern Network ( LPN ), to take advantage of contextual information in an end-to-end manner. Without using extra part estimators, LPN adopts a square-ring feature partition strategy, which provides the attention according to the distance to the image center. It eases the part matching and enables the part-wise representation learning. Owing to the square-ring partition design, the proposed LPN has good scalability to rotation variations and achieves competitive results on three prevailing benchmarks, i.e. , University-1652, CVUSA and CVACT. Besides, we also show the proposed LPN can be easily embedded into other frameworks to further boost performance.
dc.language	English
dc.publisher	Institute of Electrical and Electronics Engineers
dc.relation.ispartof	IEEE Transactions on Circuits and Systems for Video Technology
dc.relation.isbasedon	10.1109/tcsvt.2021.3061265
dc.rights	info:eu-repo/semantics/closedAccess
dc.subject	0801 Artificial Intelligence and Image Processing, 0906 Electrical and Electronic Engineering
dc.subject.classification	Artificial Intelligence & Image Processing
dc.title	Each Part Matters: Local Patterns Facilitate Cross-view Geo-localization
dc.type	Journal Article
utslib.citation.volume	32
utslib.for	0801 Artificial Intelligence and Image Processing
utslib.for	0906 Electrical and Electronic Engineering
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
utslib.copyright.status	closed_access	*
pubs.consider-herdc	false
dc.date.updated	2023-03-23T01:01:19Z
pubs.issue	2
pubs.publication-status	Published
pubs.volume	32
utslib.citation.issue	2

Abstract:

Cross-view geo-localization is to spot images of the same geographic target from different platforms, e.g. , drone-view cameras and satellites. It is challenging in the large visual appearance changes caused by extreme viewpoint variations. Existing methods usually concentrate on mining the fine-grained feature of the geographic target in the image center, but underestimate the contextual information in neighbor areas. In this work, we argue that neighbor areas can be leveraged as auxiliary information, enriching discriminative clues for geo-localization. Specifically, we introduce a simple and effective deep neural network, called Local Pattern Network ( LPN ), to take advantage of contextual information in an end-to-end manner. Without using extra part estimators, LPN adopts a square-ring feature partition strategy, which provides the attention according to the distance to the image center. It eases the part matching and enables the part-wise representation learning. Owing to the square-ring partition design, the proposed LPN has good scalability to rotation variations and achieves competitive results on three prevailing benchmarks, i.e. , University-1652, CVUSA and CVACT. Besides, we also show the proposed LPN can be easily embedded into other frameworks to further boost performance.

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http://hdl.handle.net/10453/168127