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Conference Paper: Out-of-core tensor approximation of multi-dimensional matrices of visual data

TitleOut-of-core tensor approximation of multi-dimensional matrices of visual data
Authors
KeywordsBidirectional Texture Functions
Block-Based Partitioning
Multilinear Models
Spatial Coherence
Volume Simulations
Issue Date2005
Citation
Acm Transactions On Graphics, 2005, v. 24 n. 3, p. 527-535 How to Cite?
AbstractTensor approximation is necessary to obtain compact multilinear models for multi-dimensional visual datasets. Traditionally, each multi-dimensional data item is represented as a vector. Such a scheme flattens the data and partially destroys the internal structures established throughout the multiple dimensions. In this paper, we retain the original dimensionality of the data items to more effectively exploit existing spatial redundancy and allow more efficient computation. Since the size of visual datasets can easily exceed the memory capacity of a single machine, we also present an out-of-core algorithm for higher-order tensor approximation. The basic idea is to partition a tensor into smaller blocks and perform tensor-related operations blockwise. We have successfully applied our techniques to three graphics-related data-driven models, including 6D bidirectional texture functions, 7D dynamic BTFs and 4D volume simulation sequences. Experimental results indicate that our techniques can not only process out-of-core data, but also achieve higher compression ratios and quality than previous methods. Copyright © 2005 by the Association for Computing Machinery, Inc.
Persistent Identifierhttp://hdl.handle.net/10722/151880
ISSN
2015 Impact Factor: 4.218
2015 SCImago Journal Rankings: 2.552
ISI Accession Number ID
References

 

DC FieldValueLanguage
dc.contributor.authorWang, Hen_US
dc.contributor.authorWu, Qen_US
dc.contributor.authorShi, Len_US
dc.contributor.authorYu, Yen_US
dc.contributor.authorAhuja, Nen_US
dc.date.accessioned2012-06-26T06:30:19Z-
dc.date.available2012-06-26T06:30:19Z-
dc.date.issued2005en_US
dc.identifier.citationAcm Transactions On Graphics, 2005, v. 24 n. 3, p. 527-535en_US
dc.identifier.issn0730-0301en_US
dc.identifier.urihttp://hdl.handle.net/10722/151880-
dc.description.abstractTensor approximation is necessary to obtain compact multilinear models for multi-dimensional visual datasets. Traditionally, each multi-dimensional data item is represented as a vector. Such a scheme flattens the data and partially destroys the internal structures established throughout the multiple dimensions. In this paper, we retain the original dimensionality of the data items to more effectively exploit existing spatial redundancy and allow more efficient computation. Since the size of visual datasets can easily exceed the memory capacity of a single machine, we also present an out-of-core algorithm for higher-order tensor approximation. The basic idea is to partition a tensor into smaller blocks and perform tensor-related operations blockwise. We have successfully applied our techniques to three graphics-related data-driven models, including 6D bidirectional texture functions, 7D dynamic BTFs and 4D volume simulation sequences. Experimental results indicate that our techniques can not only process out-of-core data, but also achieve higher compression ratios and quality than previous methods. Copyright © 2005 by the Association for Computing Machinery, Inc.en_US
dc.languageengen_US
dc.relation.ispartofACM Transactions on Graphicsen_US
dc.subjectBidirectional Texture Functionsen_US
dc.subjectBlock-Based Partitioningen_US
dc.subjectMultilinear Modelsen_US
dc.subjectSpatial Coherenceen_US
dc.subjectVolume Simulationsen_US
dc.titleOut-of-core tensor approximation of multi-dimensional matrices of visual dataen_US
dc.typeConference_Paperen_US
dc.identifier.emailYu, Y:yzyu@cs.hku.hken_US
dc.identifier.authorityYu, Y=rp01415en_US
dc.description.naturelink_to_subscribed_fulltexten_US
dc.identifier.doi10.1145/1073204.1073224en_US
dc.identifier.scopuseid_2-s2.0-33646061690en_US
dc.relation.referenceshttp://www.scopus.com/mlt/select.url?eid=2-s2.0-33646061690&selection=ref&src=s&origin=recordpageen_US
dc.identifier.volume24en_US
dc.identifier.issue3en_US
dc.identifier.spage527en_US
dc.identifier.epage535en_US
dc.identifier.isiWOS:000231223700019-
dc.publisher.placeUnited Statesen_US
dc.identifier.scopusauthoridWang, H=8732047300en_US
dc.identifier.scopusauthoridWu, Q=51964899100en_US
dc.identifier.scopusauthoridShi, L=36168655800en_US
dc.identifier.scopusauthoridYu, Y=8554163500en_US
dc.identifier.scopusauthoridAhuja, N=35515078200en_US

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