A Bayesian network for combining descriptors: application to symbol recognition
Identifieur interne : 003781 ( Istex/Corpus ); précédent : 003780; suivant : 003782A Bayesian network for combining descriptors: application to symbol recognition
Auteurs : Sabine Barrat ; Salvatore TabboneSource :
- International Journal on Document Analysis and Recognition (IJDAR) [ 1433-2833 ] ; 2010-03-01.
English descriptors
- KwdEn :
Abstract
Abstract: In this paper, we propose a descriptor combination method, which enables to improve significantly the recognition rate compared to the recognition rates obtained by each descriptor. This approach is based on a probabilistic graphical model. This model also enables to handle both discrete and continuous-valued variables. In fact, in order to improve the recognition rate, we have combined two kinds of features: discrete features (corresponding to shape measures) and continuous features (corresponding to shape descriptors). In order to solve the dimensionality problem due to the large dimension of visual features, we have adapted a variable selection method. Experimental results, obtained in a supervised learning context, on noisy and occluded symbols, show the feasibility of the approach.
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DOI: 10.1007/s10032-009-0103-y
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ISTEX:E92AD1CBF5B5B6BB6DFEBD0FE2D408B699768419Le document en format XML
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Grant="Restricted"></ESMGrant></ArticleGrants></ArticleInfo><ArticleHeader><AuthorGroup><Author AffiliationIDS="Aff1" CorrespondingAffiliationID="Aff1"><AuthorName DisplayOrder="Western"><GivenName>Sabine</GivenName><FamilyName>Barrat</FamilyName></AuthorName><Contact><Email>barrat@loria.fr</Email></Contact></Author><Author AffiliationIDS="Aff1"><AuthorName DisplayOrder="Western"><GivenName>Salvatore</GivenName><FamilyName>Tabbone</FamilyName></AuthorName><Contact><Email>tabbone@loria.fr</Email></Contact></Author><Affiliation ID="Aff1"><OrgDivision>LORIA-UMR 7503</OrgDivision><OrgName>University of Nancy 2</OrgName><OrgAddress><Postbox>BP 239</Postbox><Postcode>54506</Postcode><City>Vandoeuvre-lès-Nancy</City><Country Code="FR">France</Country></OrgAddress></Affiliation></AuthorGroup><Abstract ID="Abs1" Language="En"><Heading>Abstract</Heading><Para>In this paper, we propose a descriptor combination method, which enables to improve significantly the recognition rate compared to the recognition rates obtained by each descriptor. This approach is based on a probabilistic graphical model. This model also enables to handle both discrete and continuous-valued variables. In fact, in order to improve the recognition rate, we have combined two kinds of features: discrete features (corresponding to shape measures) and continuous features (corresponding to shape descriptors). In order to solve the dimensionality problem due to the large dimension of visual features, we have adapted a variable selection method. Experimental results, obtained in a supervised learning context, on noisy and occluded symbols, show the feasibility of the approach.</Para></Abstract><KeywordGroup Language="En"><Heading>Keywords</Heading><Keyword>Symbol recognition</Keyword><Keyword>Descriptor combination</Keyword><Keyword>Variable selection</Keyword><Keyword>Probabilistic graphical models</Keyword><Keyword>Bayesian networks</Keyword></KeywordGroup></ArticleHeader><NoBody></NoBody></Article></Issue></Volume></Journal></Publisher></istex:document></istex:metadataXml><mods version="3.6"><titleInfo lang="en"><title>A Bayesian network for combining descriptors: application to symbol recognition</title></titleInfo><titleInfo type="alternative" contentType="CDATA"><title>A Bayesian network for combining descriptors: application to symbol recognition</title></titleInfo><name type="personal" displayLabel="corresp"><namePart type="given">Sabine</namePart><namePart type="family">Barrat</namePart><affiliation>LORIA-UMR 7503, University of Nancy 2, BP 239, 54506, Vandoeuvre-lès-Nancy, France</affiliation><affiliation>E-mail: barrat@loria.fr</affiliation><role><roleTerm type="text">author</roleTerm></role></name><name type="personal"><namePart type="given">Salvatore</namePart><namePart type="family">Tabbone</namePart><affiliation>LORIA-UMR 7503, University of Nancy 2, BP 239, 54506, Vandoeuvre-lès-Nancy, France</affiliation><affiliation>E-mail: tabbone@loria.fr</affiliation><role><roleTerm type="text">author</roleTerm></role></name><typeOfResource>text</typeOfResource><genre type="research-article" displayLabel="OriginalPaper" authority="ISTEX" authorityURI="https://content-type.data.istex.fr" valueURI="https://content-type.data.istex.fr/ark:/67375/XTP-1JC4F85T-7">research-article</genre><originInfo><publisher>Springer-Verlag</publisher><place><placeTerm type="text">Berlin/Heidelberg</placeTerm></place><dateCreated encoding="w3cdtf">2009-03-25</dateCreated><dateIssued encoding="w3cdtf">2010-03-01</dateIssued><copyrightDate encoding="w3cdtf">2009</copyrightDate></originInfo><language><languageTerm type="code" authority="rfc3066">en</languageTerm><languageTerm type="code" authority="iso639-2b">eng</languageTerm></language><abstract lang="en">Abstract: In this paper, we propose a descriptor combination method, which enables to improve significantly the recognition rate compared to the recognition rates obtained by each descriptor. This approach is based on a probabilistic graphical model. This model also enables to handle both discrete and continuous-valued variables. In fact, in order to improve the recognition rate, we have combined two kinds of features: discrete features (corresponding to shape measures) and continuous features (corresponding to shape descriptors). In order to solve the dimensionality problem due to the large dimension of visual features, we have adapted a variable selection method. Experimental results, obtained in a supervised learning context, on noisy and occluded symbols, show the feasibility of the approach.</abstract><note>Full Paper</note><subject lang="en"><genre>Keywords</genre><topic>Symbol recognition</topic><topic>Descriptor combination</topic><topic>Variable selection</topic><topic>Probabilistic graphical models</topic><topic>Bayesian networks</topic></subject><relatedItem type="host"><titleInfo><title>International Journal on Document Analysis and Recognition (IJDAR)</title></titleInfo><titleInfo type="abbreviated"><title>IJDAR</title></titleInfo><genre type="journal" authority="ISTEX" authorityURI="https://publication-type.data.istex.fr" valueURI="https://publication-type.data.istex.fr/ark:/67375/JMC-0GLKJH51-B">journal</genre><originInfo><publisher>Springer</publisher><dateIssued encoding="w3cdtf">2010-03-12</dateIssued><copyrightDate encoding="w3cdtf">2010</copyrightDate></originInfo><subject><genre>Computer Science</genre><topic>Pattern Recognition</topic><topic>Image Processing and Computer Vision</topic></subject><identifier type="ISSN">1433-2833</identifier><identifier type="eISSN">1433-2825</identifier><identifier type="JournalID">10032</identifier><identifier type="IssueArticleCount">5</identifier><identifier type="VolumeIssueCount">4</identifier><part><date>2010</date><detail type="volume"><number>13</number><caption>vol.</caption></detail><detail type="issue"><number>1</number><caption>no.</caption></detail><extent unit="pages"><start>65</start><end>75</end></extent></part><recordInfo><recordOrigin>Springer-Verlag, 2010</recordOrigin></recordInfo></relatedItem><identifier type="istex">E92AD1CBF5B5B6BB6DFEBD0FE2D408B699768419</identifier><identifier type="ark">ark:/67375/VQC-D4TLZX59-7</identifier><identifier type="DOI">10.1007/s10032-009-0103-y</identifier><identifier type="ArticleID">103</identifier><identifier type="ArticleID">s10032-009-0103-y</identifier><accessCondition type="use and reproduction" contentType="copyright">Springer-Verlag, 2009</accessCondition><recordInfo><recordContentSource authority="ISTEX" authorityURI="https://loaded-corpus.data.istex.fr" valueURI="https://loaded-corpus.data.istex.fr/ark:/67375/XBH-3XSW68JL-F">springer</recordContentSource><recordOrigin>Springer-Verlag, 2009</recordOrigin></recordInfo></mods><json:item><extension>json</extension><original>false</original><mimetype>application/json</mimetype><uri>https://api.istex.fr/ark:/67375/VQC-D4TLZX59-7/record.json</uri></json:item></metadata><serie></serie></istex></record>Pour manipuler ce document sous Unix (Dilib)
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