Symbol descriptor based on shape context and vector model of information retrieval
Identifieur interne : 003158 ( Hal/Checkpoint ); précédent : 003157; suivant : 003159Symbol descriptor based on shape context and vector model of information retrieval
Auteurs : Thi Oanh Nguyen [France] ; Salvatore Tabbone [France] ; Oriol Ramos Terrades [France]Source :
English descriptors
Abstract
In this paper we present an adaptative method for graphic symbol representation based on shape contexts. The proposed descriptor is invariant under classical geometric transforms (rotation, scale) and based on interest points. To reduce the complexity of matching a symbol to a large set of candidates we use the popular vector model for information retrieval. In this way, on the set of shape descriptors we build a visual vocabulary where each symbol is retrieved on visual words. Experimental results on complex and occluded symbols show that the approach is very promising.
Url:
DOI: 10.1109/DAS.2008.58
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Hal:hal-00334432Le document en format XML
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type="university">Université Nancy 2</orgName><orgName type="institution" wicri:auto="newGroup">Université de Lorraine</orgName><placeName><settlement type="city">Nancy</settlement><region type="region" nuts="2">Grand Est</region><region type="old region" nuts="2">Lorraine (région)</region></placeName><orgName type="university">Institut national polytechnique de Lorraine</orgName><orgName type="institution" wicri:auto="newGroup">Université de Lorraine</orgName></affiliation></author></analytic><idno type="DOI">10.1109/DAS.2008.58</idno></biblStruct></sourceDesc></fileDesc><profileDesc><textClass><keywords scheme="mix" xml:lang="en"><term>graphic symbol</term><term>symbol descriptor</term><term>vector model</term><term>visual word</term></keywords></textClass></profileDesc></teiHeader><front><div type="abstract" xml:lang="en">In this paper we present an adaptative method for graphic symbol representation based on shape contexts. The proposed descriptor is invariant under classical geometric transforms (rotation, scale) and based on interest points. To reduce the complexity of matching a symbol to a large set of candidates we use the popular vector model for information retrieval. In this way, on the set of shape descriptors we build a visual vocabulary where each symbol is retrieved on visual words. Experimental results on complex and occluded symbols show that the approach is very promising.</div></front></TEI><hal api="V3"><titleStmt><title xml:lang="en">Symbol descriptor based on shape context and vector model of information retrieval</title><author role="aut"><persName><forename type="first">Thi Oanh</forename><surname>Nguyen</surname></persName><email>nguyenth@loria.fr</email><idno type="halauthor">363447</idno><affiliation ref="#struct-2361"></affiliation></author><author role="aut"><persName><forename type="first">Salvatore</forename><surname>Tabbone</surname></persName><email>tabbone@loria.fr</email><idno type="halauthor">59043</idno><affiliation ref="#struct-2361"></affiliation></author><author role="aut"><persName><forename type="first">Oriol</forename><surname>Ramos Terrades</surname></persName><email>ramos@loria.fr</email><idno type="halauthor">216378</idno><affiliation ref="#struct-2361"></affiliation></author><editor role="depositor"><persName><forename>Thi Oanh</forename><surname>Nguyen</surname></persName><email>nguyenth@loria.fr</email></editor></titleStmt><editionStmt><edition n="v1" type="current"><date type="whenSubmitted">2008-10-26 12:35:12</date><date type="whenWritten">2008-03-30</date><date type="whenModified">2016-05-19 01:09:17</date><date type="whenReleased">2008-10-27 12:10:38</date><date type="whenProduced">2008-09-16</date><date type="whenEndEmbargoed">2008-10-26</date><ref type="file" target="https://hal.archives-ouvertes.fr/hal-00334432/document"><date notBefore="2008-10-26"></date></ref><ref type="file" subtype="author" n="1" target="https://hal.archives-ouvertes.fr/hal-00334432/file/nguyen-symbolDescriptor-scip.pdf"><date notBefore="2008-10-26"></date></ref></edition><respStmt><resp>contributor</resp><name key="114960"><persName><forename>Thi Oanh</forename><surname>Nguyen</surname></persName><email>nguyenth@loria.fr</email></name></respStmt></editionStmt><publicationStmt><distributor>CCSD</distributor><idno type="halId">hal-00334432</idno><idno type="halUri">https://hal.archives-ouvertes.fr/hal-00334432</idno><idno type="halBibtex">nguyen:hal-00334432</idno><idno type="halRefHtml">The 8th IAPR International Workshop on Document Analysis Systems, Sep 2008, Nara, Japan. pp.191-197, 2008, <10.1109/DAS.2008.58></idno><idno type="halRef">The 8th IAPR International Workshop on Document Analysis Systems, Sep 2008, Nara, Japan. pp.191-197, 2008, <10.1109/DAS.2008.58></idno></publicationStmt><seriesStmt><idno type="stamp" n="CNRS">CNRS - Centre national de la recherche scientifique</idno><idno type="stamp" n="INRIA">INRIA - Institut National de Recherche en Informatique et en Automatique</idno><idno type="stamp" n="INPL">Institut National Polytechnique de Lorraine</idno><idno type="stamp" n="LORIA2">Publications du LORIA</idno><idno type="stamp" n="INRIA-NANCY-GRAND-EST">INRIA Nancy - Grand Est</idno><idno type="stamp" n="LORIA-TALC" p="LORIA">Traitement automatique des langues et des connaissances</idno><idno type="stamp" n="LORIA">LORIA - Laboratoire Lorrain de Recherche en Informatique et ses Applications</idno><idno type="stamp" n="UNIV-LORRAINE">Université de Lorraine</idno><idno type="stamp" n="INRIA-LORRAINE">INRIA Nancy - Grand Est</idno><idno type="stamp" n="LABO-LORIA-SET" p="LORIA">LABO-LORIA-SET</idno></seriesStmt><notesStmt><note type="audience" n="2">International</note><note type="invited" n="0">No</note><note type="popular" n="0">No</note><note type="peer" n="1">Yes</note><note type="proceedings" n="1">Yes</note></notesStmt><sourceDesc><biblStruct><analytic><title xml:lang="en">Symbol descriptor based on shape context and vector model of information retrieval</title><author role="aut"><persName><forename type="first">Thi Oanh</forename><surname>Nguyen</surname></persName><email>nguyenth@loria.fr</email><idno type="halAuthorId">363447</idno><affiliation ref="#struct-2361"></affiliation></author><author role="aut"><persName><forename type="first">Salvatore</forename><surname>Tabbone</surname></persName><email>tabbone@loria.fr</email><idno type="halAuthorId">59043</idno><affiliation ref="#struct-2361"></affiliation></author><author role="aut"><persName><forename type="first">Oriol</forename><surname>Ramos Terrades</surname></persName><email>ramos@loria.fr</email><idno type="halAuthorId">216378</idno><affiliation ref="#struct-2361"></affiliation></author></analytic><monogr><title level="m">DAS 2008 - Proceedings of the 8th IAPR International Workshop on Document Analysis Systems</title><meeting><title>The 8th IAPR International Workshop on Document Analysis Systems</title><date type="start">2008-09-16</date><date type="end">2008-09-19</date><settlement>Nara</settlement><country key="JP">Japan</country></meeting><imprint><biblScope unit="pp">191-197</biblScope><date type="datePub">2008-09-16</date></imprint></monogr><idno type="doi">10.1109/DAS.2008.58</idno></biblStruct></sourceDesc><profileDesc><langUsage><language ident="en">English</language></langUsage><textClass><keywords scheme="author"><term xml:lang="en">symbol descriptor</term><term xml:lang="en">graphic symbol</term><term xml:lang="en">visual word</term><term xml:lang="en">vector model</term></keywords><classCode scheme="halDomain" n="info.info-tt">Computer Science [cs]/Document and Text Processing</classCode><classCode scheme="halDomain" n="info.info-ti">Computer Science [cs]/Image Processing</classCode><classCode scheme="halDomain" n="info.info-cv">Computer Science [cs]/Computer Vision and Pattern Recognition [cs.CV]</classCode><classCode scheme="halTypology" n="COMM">Conference papers</classCode></textClass><abstract xml:lang="en">In this paper we present an adaptative method for graphic symbol representation based on shape contexts. The proposed descriptor is invariant under classical geometric transforms (rotation, scale) and based on interest points. To reduce the complexity of matching a symbol to a large set of candidates we use the popular vector model for information retrieval. In this way, on the set of shape descriptors we build a visual vocabulary where each symbol is retrieved on visual words. Experimental results on complex and occluded symbols show that the approach is very promising.</abstract></profileDesc></hal></record>Pour manipuler ce document sous Unix (Dilib)
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