Random projection tree and multiview embedding for large-scale image retrieval

Xie, B; Mu, Y; Song, M; Tao, D

Random projection tree and multiview embedding for large-scale image retrieval

Xie, B Mu, Y Song, M Tao, D

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Publication Type:: Conference Proceeding
Citation:: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2010, 6444 LNCS (PART 2), pp. 641 - 649
Issue Date:: 2010-12-21

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Field	Value	Language
dc.contributor.author	Xie, B	en_US
dc.contributor.author	Mu, Y	en_US
dc.contributor.author	Song, M	en_US
dc.contributor.author	Tao, D https://orcid.org/0000-0001-7225-5449	en_US
dc.date.issued	2010-12-21	en_US
dc.identifier.citation	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2010, 6444 LNCS (PART 2), pp. 641 - 649	en_US
dc.identifier.isbn	3642175333	en_US
dc.identifier.isbn	9783642175336	en_US
dc.identifier.issn	0302-9743	en_US
dc.identifier.uri	http://hdl.handle.net/10453/16239
dc.description.abstract	Image retrieval on large-scale datasets is challenging. Current indexing schemes, such as k-d tree, suffer from the "curse of dimensionality". In addition, there is no principled approach to integrate various features that measure multiple views of images, such as color histogram and edge directional histogram. We propose a novel retrieval system that tackles these two problems simultaneously. First, we use random projection trees to index data whose complexity only depends on the low intrinsic dimension of a dataset. Second, we apply a probabilistic multiview embedding algorithm to unify different features. Experiments on MSRA large-scale dataset demonstrate the efficiency and effectiveness of the proposed approach. © 2010 Springer-Verlag.	en_US
dc.relation.ispartof	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)	en_US
dc.relation.isbasedon	10.1007/978-3-642-17534-3_79	en_US
dc.subject.classification	Artificial Intelligence & Image Processing	en_US
dc.title	Random projection tree and multiview embedding for large-scale image retrieval	en_US
dc.type	Conference Proceeding
utslib.citation.volume	PART 2	en_US
utslib.citation.volume	6444 LNCS	en_US
utslib.for	080108 Neural, Evolutionary and Fuzzy Computation	en_US
dc.location.activity	Sydney, Australia	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
utslib.copyright.status	closed_access
pubs.issue	PART 2	en_US
pubs.publication-status	Published	en_US
pubs.volume	6444 LNCS	en_US

Abstract:

Image retrieval on large-scale datasets is challenging. Current indexing schemes, such as k-d tree, suffer from the "curse of dimensionality". In addition, there is no principled approach to integrate various features that measure multiple views of images, such as color histogram and edge directional histogram. We propose a novel retrieval system that tackles these two problems simultaneously. First, we use random projection trees to index data whose complexity only depends on the low intrinsic dimension of a dataset. Second, we apply a probabilistic multiview embedding algorithm to unify different features. Experiments on MSRA large-scale dataset demonstrate the efficiency and effectiveness of the proposed approach. © 2010 Springer-Verlag.

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http://hdl.handle.net/10453/16239