Please use this identifier to cite or link to this item: https://hdl.handle.net/1959.11/13853
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dc.contributor.authorChatbri, Houssemen
dc.contributor.authorKameyama, Keisukeen
dc.contributor.authorKwan, Paul Hen
local.source.editorEditor(s): Paulo de Souza, Ulrich Engelke, Ashfaqur Rahmanen
dc.date.accessioned2014-01-10T14:02:00Z
dc.date.issued2013en
dc.identifier.citationProceedings of the 2013 International Conference on Digital Image Computing: Techniques and Applications (DICTA), p. 469-476en
dc.identifier.isbn9781479921263en
dc.identifier.urihttps://hdl.handle.net/1959.11/13853en
dc.description.abstractWe review available methods for Sketch-Based Image Retrieval (SBIR) and we discuss their limitations. Then, we present two SBIR algorithms: The first algorithm extracts shape features by using support regions calculated for each sketch point, and the second algorithm adapts the Shape Context descriptor [1] to make it scale invariant and enhances its performance in presence of noise. Both algorithms share the property of calculating the feature extraction window according to the sketch size. Experiments and comparative evaluation with state-of-the-art methods show that the proposed algorithms are competitive in distinctiveness capability and robust against noise.en
dc.languageenen
dc.publisherIEEE: Institute of Electrical and Electronics Engineersen
dc.relation.ispartofProceedings of the 2013 International Conference on Digital Image Computing: Techniques and Applications (DICTA)en
dc.titleSketch-Based Image Retrieval By Size-Adaptive and Noise-Robust Feature Descriptionen
dc.typeConference Publicationen
dc.relation.conferenceDICTA 2013: International Conference on Digital Image Computing: Techniques and Applications, Hobart, Australia, 26th - 28th November, 2013en
dc.identifier.doi10.1109/DICTA.2013.6691528en
dc.subject.keywordsImage Processingen
dc.subject.keywordsPattern Recognition and Data Miningen
dc.subject.keywordsComputer Visionen
local.contributor.firstnameHoussemen
local.contributor.firstnameKeisukeen
local.contributor.firstnamePaul Hen
local.subject.for2008080104 Computer Visionen
local.subject.for2008080109 Pattern Recognition and Data Miningen
local.subject.for2008080106 Image Processingen
local.subject.seo2008970110 Expanding Knowledge in Technologyen
local.subject.seo2008970108 Expanding Knowledge in the Information and Computing Sciencesen
local.subject.seo2008890201 Application Software Packages (excl. Computer Games)en
local.profile.schoolSchool of Science and Technologyen
local.profile.emailchatbri.houcem@gmail.comen
local.profile.emailkeisuke.kameyama@cs.tsukuba.ac.jpen
local.profile.emailwkwan2@une.edu.auen
local.output.categoryE1en
local.record.placeauen
local.record.institutionUniversity of New Englanden
local.identifier.epublicationsrecordune-20131203-103125en
local.publisher.placeNew York, United States of Americaen
local.format.startpage469en
local.format.endpage476en
local.peerreviewedYesen
local.contributor.lastnameChatbrien
local.contributor.lastnameKameyamaen
local.contributor.lastnameKwanen
dc.identifier.staffune-id:wkwan2en
local.profile.roleauthoren
local.profile.roleauthoren
local.profile.roleauthoren
local.identifier.unepublicationidune:14066en
dc.identifier.academiclevelAcademicen
dc.identifier.academiclevelAcademicen
local.title.maintitleSketch-Based Image Retrieval By Size-Adaptive and Noise-Robust Feature Descriptionen
local.output.categorydescriptionE1 Refereed Scholarly Conference Publicationen
local.conference.detailsDICTA 2013: International Conference on Digital Image Computing: Techniques and Applications, Hobart, Australia, 26th - 28th November, 2013en
local.description.statisticsepubsVisitors: 226<br />Views: 438<br />Downloads: 1en
local.search.authorChatbri, Houssemen
local.search.authorKameyama, Keisukeen
local.search.authorKwan, Paul Hen
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