Exploring consistent preferences: Discrete hashing with pair-exemplar for scalable landmark search

Zhu, L; Huang, Z; Chang, X; Song, J; Shen, HT

Exploring consistent preferences: Discrete hashing with pair-exemplar for scalable landmark search

Zhu, L Huang, Z Chang, X

Song, J Shen, HT

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Publication Type:: Conference Proceeding
Citation:: MM 2017 - Proceedings of the 2017 ACM Multimedia Conference, 2017, pp. 726 - 734
Issue Date:: 2017-10-23

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Field	Value	Language
dc.contributor.author	Zhu, L	en_US
dc.contributor.author	Huang, Z	en_US
dc.contributor.author	Chang, X https://orcid.org/0000-0002-7778-8807	en_US
dc.contributor.author	Song, J	en_US
dc.contributor.author	Shen, HT	en_US
dc.date.issued	2017-10-23	en_US
dc.identifier.citation	MM 2017 - Proceedings of the 2017 ACM Multimedia Conference, 2017, pp. 726 - 734	en_US
dc.identifier.isbn	9781450349062	en_US
dc.identifier.uri	http://hdl.handle.net/10453/126312
dc.description.abstract	© 2017 ACM. Content-based visual landmark search (CBVLS) enjoys great importance in many practical applications. In this paper, we propose a novel discrete hashing with pair-exemplar (DHPE) to support scalable and efficient large-scale CBVLS. Our approach mainly solves two essential problems in scalable landmark hashing: 1) Intralandmark visual diversity, and 2) Discrete optimization of hashing codes. Motivated by the characteristic of landmark, we explore the consistent preferences of tourists on landmark as pair-exemplars for scalable discrete hashing learning. In this paper, a pair-exemplar is comprised of a canonical view and the corresponding representative tags. Canonical view captures the key visual component of landmarks, and representative tags potentially involve landmark-specific semantics that can cope with the visual variations of intra-landmark. Based on pair-exemplars, a unified hashing learning framework is formulated to combine visual preserving with exemplar graph and the semantic guidance from representative tags. Further to guarantee direct semantic transfer for hashing codes and remove information redundancy, we design a novel optimization method based on augmented Lagrange multiplier to explicitly deal with the discrete constraint, the bit uncorrelated constraint and balance constraint. The whole learning process has linear computation complexity and enjoys desirable scalability. Experiments demonstrate the superior performance of DHPE compared with state-of-the-art methods.	en_US
dc.relation.ispartof	MM 2017 - Proceedings of the 2017 ACM Multimedia Conference	en_US
dc.relation.isbasedon	10.1145/3123266.3123301	en_US
dc.title	Exploring consistent preferences: Discrete hashing with pair-exemplar for scalable landmark search	en_US
dc.type	Conference Proceeding
utslib.for	0801 Artificial Intelligence and Image Processing	en_US
pubs.embargo.period	Not known	en_US
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Software
pubs.organisational-group	/University of Technology Sydney/Students
utslib.copyright.status	closed_access
pubs.publication-status	Published	en_US

Abstract:

© 2017 ACM. Content-based visual landmark search (CBVLS) enjoys great importance in many practical applications. In this paper, we propose a novel discrete hashing with pair-exemplar (DHPE) to support scalable and efficient large-scale CBVLS. Our approach mainly solves two essential problems in scalable landmark hashing: 1) Intralandmark visual diversity, and 2) Discrete optimization of hashing codes. Motivated by the characteristic of landmark, we explore the consistent preferences of tourists on landmark as pair-exemplars for scalable discrete hashing learning. In this paper, a pair-exemplar is comprised of a canonical view and the corresponding representative tags. Canonical view captures the key visual component of landmarks, and representative tags potentially involve landmark-specific semantics that can cope with the visual variations of intra-landmark. Based on pair-exemplars, a unified hashing learning framework is formulated to combine visual preserving with exemplar graph and the semantic guidance from representative tags. Further to guarantee direct semantic transfer for hashing codes and remove information redundancy, we design a novel optimization method based on augmented Lagrange multiplier to explicitly deal with the discrete constraint, the bit uncorrelated constraint and balance constraint. The whole learning process has linear computation complexity and enjoys desirable scalability. Experiments demonstrate the superior performance of DHPE compared with state-of-the-art methods.

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http://hdl.handle.net/10453/126312