A Bayesian classifier for symbol recognition
Identifieur interne : 003857 ( Hal/Checkpoint ); précédent : 003856; suivant : 003858A Bayesian classifier for symbol recognition
Auteurs : Sabine Barrat [France] ; Salvatore Tabbone [France] ; Patrick Nourrissier [France]Source :
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Abstract
We present in this paper an original adaptation of Bayesian networks to symbol recognition problem. More precisely, a descriptor combination method, which enables to improve significantly the recognition rate compared to the recognition rates obtained by each descriptor, is presented. In this perspective, we use a simple Bayesian classifier, called naive Bayes. In fact, probabilistic graphical models, more specifically Bayesian networks, are a simple and intuitive way of probability distribution representation. In order to solve the dimensionality problem, we use a variable selection method. Experimental results, obtained in a supervised learning context and tested on GREC symbol database, are very promising.
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type="indirect"><org type="institution" xml:id="struct-300293" status="OLD"><orgName>Institut National Polytechnique de Lorraine</orgName><orgName type="acronym">INPL</orgName><date type="end">2011-12-31</date><desc><address><country key="FR"></country></address></desc></org></tutelle><tutelle active="#struct-2496" type="direct"><org type="laboratory" xml:id="struct-2496" status="OLD"><orgName>INRIA Lorraine</orgName><desc><address><addrLine>615 rue du Jardin Botanique 54600 Villers-lès-Nancy</addrLine><country key="FR"></country></address><ref type="url">http://www.inria.fr/centre-de-recherche-inria/nancy-grand-est</ref></desc><listRelation><relation active="#struct-300009" type="direct"></relation></listRelation></org></tutelle></tutelles></hal:affiliation><country>France</country><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">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></biblStruct></sourceDesc></fileDesc><profileDesc><textClass><keywords scheme="mix" xml:lang="en"><term>Bayesian networks</term><term>Symbol recognition</term><term>data analysis</term><term>probabilistic graphical models</term><term>variable selection</term></keywords></textClass></profileDesc></teiHeader><front><div type="abstract" xml:lang="en">We present in this paper an original adaptation of Bayesian networks to symbol recognition problem. More precisely, a descriptor combination method, which enables to improve significantly the recognition rate compared to the recognition rates obtained by each descriptor, is presented. In this perspective, we use a simple Bayesian classifier, called naive Bayes. In fact, probabilistic graphical models, more specifically Bayesian networks, are a simple and intuitive way of probability distribution representation. In order to solve the dimensionality problem, we use a variable selection method. Experimental results, obtained in a supervised learning context and tested on GREC symbol database, are very promising.</div></front></TEI><hal api="V3"><titleStmt><title xml:lang="en">A Bayesian classifier for symbol recognition</title><author role="aut"><persName><forename type="first">Sabine</forename><surname>Barrat</surname></persName><email>sabine.barrat@loria.fr</email><idno type="halauthor">150074</idno><orgName ref="#struct-300292"></orgName><affiliation ref="#struct-2361"></affiliation></author><author role="aut"><persName><forename type="first">Salvatore</forename><surname>Tabbone</surname></persName><email></email><idno type="halauthor">216741</idno><orgName ref="#struct-300292"></orgName><affiliation ref="#struct-2361"></affiliation></author><author role="aut"><persName><forename type="first">Patrick</forename><surname>Nourrissier</surname></persName><email></email><idno type="halauthor">129553</idno><affiliation ref="#struct-2361"></affiliation></author><editor role="depositor"><persName><forename>Sabine</forename><surname>Barrat</surname></persName><email>sabine.barrat@univ-tours.fr</email></editor></titleStmt><editionStmt><edition n="v1" type="current"><date type="whenSubmitted">2007-10-17 10:57:42</date><date type="whenModified">2016-05-19 01:09:22</date><date type="whenReleased">2007-10-17 15:34:41</date><date type="whenProduced">2007-09-20</date><date type="whenEndEmbargoed">2007-10-17</date><ref type="file" target="https://hal.inria.fr/inria-00179764/document"><date notBefore="2007-10-17"></date></ref><ref type="file" subtype="author" n="1" target="https://hal.inria.fr/inria-00179764/file/GREC07.pdf"><date notBefore="2007-10-17"></date></ref></edition><respStmt><resp>contributor</resp><name key="114970"><persName><forename>Sabine</forename><surname>Barrat</surname></persName><email>sabine.barrat@univ-tours.fr</email></name></respStmt></editionStmt><publicationStmt><distributor>CCSD</distributor><idno type="halId">inria-00179764</idno><idno type="halUri">https://hal.inria.fr/inria-00179764</idno><idno type="halBibtex">barrat:inria-00179764</idno><idno type="halRefHtml">Seventh International Workshop on Graphics Recognition - GREC'2007, Sep 2007, Curitiba, Brazil. 9 p., 2007, Session 2 : Symbol and shape description and recognition (1)</idno><idno type="halRef">Seventh International Workshop on Graphics Recognition - GREC'2007, Sep 2007, Curitiba, Brazil. 9 p., 2007, Session 2 : Symbol and shape description and recognition (1)</idno></publicationStmt><seriesStmt><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="CNRS">CNRS - Centre national de la recherche scientifique</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">LORIA - Laboratoire Lorrain de Recherche en Informatique et ses Applications</idno><idno type="stamp" n="LORIA-TALC" p="LORIA">Traitement automatique des langues et des connaissances</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="commentary">URL : http://www.buyans.com/POL/UploadedFile/134_9977.pdf</note><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="0">No</note></notesStmt><sourceDesc><biblStruct><analytic><title xml:lang="en">A Bayesian classifier for symbol recognition</title><author role="aut"><persName><forename type="first">Sabine</forename><surname>Barrat</surname></persName><email>sabine.barrat@loria.fr</email><idno type="halAuthorId">150074</idno><orgName ref="#struct-300292"></orgName><affiliation ref="#struct-2361"></affiliation></author><author role="aut"><persName><forename type="first">Salvatore</forename><surname>Tabbone</surname></persName><idno type="halAuthorId">216741</idno><orgName ref="#struct-300292"></orgName><affiliation ref="#struct-2361"></affiliation></author><author role="aut"><persName><forename type="first">Patrick</forename><surname>Nourrissier</surname></persName><idno type="halAuthorId">129553</idno><affiliation ref="#struct-2361"></affiliation></author></analytic><monogr><meeting><title>Seventh International Workshop on Graphics Recognition - GREC'2007</title><date type="start">2007-09-20</date><settlement>Curitiba</settlement><country key="BR">Brazil</country></meeting><respStmt><resp>conferenceOrganizer</resp><name>IAPR TC-10 (Technical Committee on Graphics Recognition)</name></respStmt><imprint><biblScope unit="serie">Session 2 : Symbol and shape description and recognition (1)</biblScope><biblScope unit="pp">9 pages</biblScope><date type="datePub">2007</date></imprint></monogr></biblStruct></sourceDesc><profileDesc><langUsage><language ident="en">English</language></langUsage><textClass><keywords scheme="author"><term xml:lang="en">Symbol recognition</term><term xml:lang="en">data analysis</term><term xml:lang="en">probabilistic graphical models</term><term xml:lang="en">Bayesian networks</term><term xml:lang="en">variable selection</term></keywords><classCode scheme="acm" n="I.5.3"></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">We present in this paper an original adaptation of Bayesian networks to symbol recognition problem. More precisely, a descriptor combination method, which enables to improve significantly the recognition rate compared to the recognition rates obtained by each descriptor, is presented. In this perspective, we use a simple Bayesian classifier, called naive Bayes. In fact, probabilistic graphical models, more specifically Bayesian networks, are a simple and intuitive way of probability distribution representation. In order to solve the dimensionality problem, we use a variable selection method. Experimental results, obtained in a supervised learning context and tested on GREC symbol database, are very promising.</abstract></profileDesc></hal></record>Pour manipuler ce document sous Unix (Dilib)
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