Image Re-Ranking Acceleration on GPUs

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dc.contributor.authorGuimaraes Pedronette, Daniel Carlos [UNESP]
dc.contributor.authorTorres, Ricardo da S.
dc.contributor.authorBorin, Edson
dc.contributor.authorBreternitz, Mauricio
dc.contributor.institutionUniversidade Estadual Paulista (Unesp)
dc.date.accessioned2015-03-18T15:55:03Z
dc.date.available2015-03-18T15:55:03Z
dc.date.issued2013-01-01
dc.description.abstractHuge image collections are becoming available lately. In this scenario, the use of Content-Based Image Retrieval (CBIR) systems has emerged as a promising approach to support image searches. The objective of CBIR systems is to retrieve the most similar images in a collection, given a query image, by taking into account image visual properties such as texture, color, and shape. In these systems, the effectiveness of the retrieval process depends heavily on the accuracy of ranking approaches. Recently, re-ranking approaches have been proposed to improve the effectiveness of CBIR systems by taking into account the relationships among images. The re-ranking approaches consider the relationships among all images in a given dataset. These approaches typically demands a huge amount of computational power, which hampers its use in practical situations. On the other hand, these methods can be massively parallelized. In this paper, we propose to speedup the computation of the RL-Sim algorithm, a recently proposed image re-ranking approach, by using the computational power of Graphics Processing Units (GPU). GPUs are emerging as relatively inexpensive parallel processors that are becoming available on a wide range of computer systems. We address the image re-ranking performance challenges by proposing a parallel solution designed to fit the computational model of GPUs. We conducted an experimental evaluation considering different implementations and devices. Experimental results demonstrate that significant performance gains can be obtained. Our approach achieves speedups of 7x from serial implementation considering the overall algorithm and up to 36x on its core steps.en
dc.description.affiliationUniv Estadual Sao Paulo UNESP, Dept Stat Appl Math & Comp, Rio Claro, Brazil
dc.description.affiliationUnespUniv Estadual Sao Paulo UNESP, Dept Stat Appl Math & Comp, Rio Claro, Brazil
dc.format.extent176-183
dc.identifierhttp://dx.doi.org/10.1109/SBAC-PAD.2013.19
dc.identifier.citation2013 25th International Symposium On Computer Architecture And High Performance Computing (sbac-pad). New York: Ieee, p. 176-183, 2013.
dc.identifier.doi10.1109/SBAC-PAD.2013.19
dc.identifier.issn1550-6533
dc.identifier.urihttp://hdl.handle.net/11449/117072
dc.identifier.wosWOS:000345905800023
dc.language.isoeng
dc.publisherIeee
dc.relation.ispartof2013 25th International Symposium On Computer Architecture And High Performance Computing (sbac-pad)
dc.relation.ispartofsjr0,154
dc.rights.accessRightsAcesso aberto
dc.sourceWeb of Science
dc.subjectcontent-based image retrievalen
dc.subjectimage re-rankingen
dc.subjectparallel computingen
dc.subjectOpenCLen
dc.subjectGPUen
dc.titleImage Re-Ranking Acceleration on GPUsen
dc.typeTrabalho apresentado em evento
dcterms.licensehttp://www.ieee.org/publications_standards/publications/rights/rights_policies.html
dcterms.rightsHolderIeee
unesp.author.orcid0000-0002-2867-4838[1]
unesp.campusUniversidade Estadual Paulista (Unesp), Instituto de Geociências e Ciências Exatas, Rio Claropt

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