Towards Performance Evaluation of Graph-Based Representation
Identifieur interne : 002472 ( Istex/Corpus ); précédent : 002471; suivant : 002473Towards Performance Evaluation of Graph-Based Representation
Auteurs : Salim Jouili ; Salvatore TabboneSource :
- Lecture Notes in Computer Science [ 0302-9743 ]
Abstract
Abstract: Graphs give a universal and flexible framework to describe the structure and relationship between objects. They are useful in many different application domains like pattern recognition, computer vision and image analysis. In the image analysis context, images can be represented as graphs such that the nodes describe the features and the edges describe their relations. In this paper we, firstly, review the graph-based representations commonly used in the literature. Secondly, we discuss, empirically, the choice of a graph-based representation on three different image databases and show that the representation has a real impact on the method performances and experimental results in the literature on graph performance evaluation for similarity measures should be considered carefully.
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DOI: 10.1007/978-3-642-20844-7_8
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They are useful in many different application domains like pattern recognition, computer vision and image analysis. In the image analysis context, images can be represented as graphs such that the nodes describe the features and the edges describe their relations. In this paper we, firstly, review the graph-based representations commonly used in the literature. 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They are useful in many different application domains like pattern recognition, computer vision and image analysis. In the image analysis context, images can be represented as graphs such that the nodes describe the features and the edges describe their relations. In this paper we, firstly, review the graph-based representations commonly used in the literature. 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Science</title><editor><persName><forename type="first">David</forename><surname>Hutchison</surname></persName><affiliation><orgName type="institution">Lancaster University</orgName><address><settlement>Lancaster</settlement><country key="GB">UNITED KINGDOM</country></address></affiliation></editor><editor><persName><forename type="first">Takeo</forename><surname>Kanade</surname></persName><affiliation><orgName type="institution">Carnegie Mellon University</orgName><address><settlement>Pittsburgh</settlement><region>PA</region><country key="US">UNITED STATES</country></address></affiliation></editor><editor><persName><forename type="first">Josef</forename><surname>Kittler</surname></persName><affiliation><orgName type="institution">University of Surrey</orgName><address><settlement>Guildford</settlement><country key="GB">UNITED KINGDOM</country></address></affiliation></editor><editor><persName><forename type="first">Jon</forename><forename type="first">M.</forename><surname>Kleinberg</surname></persName><affiliation><orgName type="institution">Cornell University</orgName><address><settlement>Ithaca</settlement><region>NY</region><country key="US">UNITED STATES</country></address></affiliation></editor><editor><persName><forename type="first">Friedemann</forename><surname>Mattern</surname></persName><affiliation><orgName type="institution">ETH Zurich</orgName><address><settlement>Zurich</settlement><country key="CH">SWITZERLAND</country></address></affiliation></editor><editor><persName><forename type="first">John</forename><forename type="first">C.</forename><surname>Mitchell</surname></persName><affiliation><orgName type="institution">Stanford University</orgName><address><settlement>Stanford</settlement><region>CA</region><country key="US">UNITED STATES</country></address></affiliation></editor><editor><persName><forename type="first">Moni</forename><surname>Naor</surname></persName><affiliation><orgName 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key="DE">GERMANY</country></address></affiliation></editor><editor><persName><forename type="first">Madhu</forename><surname>Sudan</surname></persName><affiliation><orgName type="institution">Massachusetts Institute of Technology</orgName><address><region>MA</region><country key="US">UNITED STATES</country></address></affiliation></editor><editor><persName><forename type="first">Demetri</forename><surname>Terzopoulos</surname></persName><affiliation><orgName type="institution">University of California</orgName><address><settlement>Los Angeles</settlement><region>CA</region><country key="US">UNITED STATES</country></address></affiliation></editor><editor><persName><forename type="first">Doug</forename><surname>Tygar</surname></persName><affiliation><orgName type="institution">University of California</orgName><address><settlement>Berkeley</settlement><region>CA</region><country key="US">UNITED STATES</country></address></affiliation></editor><editor><persName><forename type="first">Moshe</forename><forename type="first">Y.</forename><surname>Vardi</surname></persName><affiliation><orgName type="institution">Rice University</orgName><address><settlement>Houston</settlement><region>TX</region><country key="US">UNITED STATES</country></address></affiliation></editor><editor><persName><forename type="first">Gerhard</forename><surname>Weikum</surname></persName><affiliation><orgName type="institution">Max-Planck Institute of Computer Science</orgName><address><settlement>Saarbrücken</settlement><country key="DE">GERMANY</country></address></affiliation></editor><idno type="pISSN">0302-9743</idno><idno type="eISSN">1611-3349</idno><idno type="seriesID">558</idno></series></biblStruct></sourceDesc></fileDesc><profileDesc><abstract xml:lang="en"><head>Abstract</head><p>Graphs give a universal and flexible framework to describe the structure and relationship between objects. They are useful in many different application domains like pattern recognition, computer vision and image analysis. In the image analysis context, images can be represented as graphs such that the nodes describe the features and the edges describe their relations. In this paper we, firstly, review the graph-based representations commonly used in the literature. Secondly, we discuss, empirically, the choice of a graph-based representation on three different image databases and show that the representation has a real impact on the method performances and experimental results in the literature on graph performance evaluation for similarity measures should be considered carefully.</p></abstract><textClass ana="subject"><keywords scheme="book-subject-collection"><list><label>SUCO11645</label><item><term>Computer Science</term></item></list></keywords></textClass><textClass ana="subject"><keywords scheme="book-subject"><list><label>I</label><item><term type="Primary">Computer Science</term></item><label>I2203X</label><item><term type="Secondary" subtype="priority-1">Pattern Recognition</term></item><label>I22021</label><item><term type="Secondary" subtype="priority-2">Image Processing and Computer Vision</term></item><label>I22005</label><item><term type="Secondary" subtype="priority-3">Computer Imaging, Vision, Pattern Recognition and Graphics</term></item><label>I22013</label><item><term type="Secondary" subtype="priority-4">Computer Graphics</term></item><label>I17028</label><item><term type="Secondary" subtype="priority-5">Discrete Mathematics in Computer Science</term></item><label>I21017</label><item><term type="Secondary" subtype="priority-6">Artificial Intelligence (incl. 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University</OrgName><OrgAddress><City>Pittsburgh</City><State>PA</State><Country>USA</Country></OrgAddress></Affiliation><Affiliation ID="Aff3"><OrgName>University of Surrey</OrgName><OrgAddress><City>Guildford</City><Country>UK</Country></OrgAddress></Affiliation><Affiliation ID="Aff4"><OrgName>Cornell University</OrgName><OrgAddress><City>Ithaca</City><State>NY</State><Country>USA</Country></OrgAddress></Affiliation><Affiliation ID="Aff5"><OrgName>ETH Zurich</OrgName><OrgAddress><City>Zurich</City><Country>Switzerland</Country></OrgAddress></Affiliation><Affiliation ID="Aff6"><OrgName>Stanford University</OrgName><OrgAddress><City>Stanford</City><State>CA</State><Country>USA</Country></OrgAddress></Affiliation><Affiliation ID="Aff7"><OrgName>Weizmann Institute of Science</OrgName><OrgAddress><City>Rehovot</City><Country>Israel</Country></OrgAddress></Affiliation><Affiliation ID="Aff8"><OrgName>University of Bern</OrgName><OrgAddress><City>Bern</City><Country>Switzerland</Country></OrgAddress></Affiliation><Affiliation ID="Aff9"><OrgName>Indian Institute of Technology</OrgName><OrgAddress><City>Madras</City><Country>India</Country></OrgAddress></Affiliation><Affiliation ID="Aff10"><OrgName>University of Dortmund</OrgName><OrgAddress><City>Dortmund</City><Country>Germany</Country></OrgAddress></Affiliation><Affiliation ID="Aff11"><OrgName>Massachusetts Institute of Technology</OrgName><OrgAddress><State>MA</State><Country>USA</Country></OrgAddress></Affiliation><Affiliation ID="Aff12"><OrgName>University of California</OrgName><OrgAddress><City>Los Angeles</City><State>CA</State><Country>USA</Country></OrgAddress></Affiliation><Affiliation ID="Aff13"><OrgName>University of California</OrgName><OrgAddress><City>Berkeley</City><State>CA</State><Country>USA</Country></OrgAddress></Affiliation><Affiliation ID="Aff14"><OrgName>Rice University</OrgName><OrgAddress><City>Houston</City><State>TX</State><Country>USA</Country></OrgAddress></Affiliation><Affiliation ID="Aff15"><OrgName>Max-Planck Institute of Computer Science</OrgName><OrgAddress><City>Saarbrücken</City><Country>Germany</Country></OrgAddress></Affiliation></EditorGroup></SeriesHeader><Book Language="En"><BookInfo BookProductType="Proceedings" ContainsESM="No" Language="En" MediaType="eBook" NumberingDepth="2" NumberingStyle="ContentOnly" OutputMedium="All" TocLevels="0"><BookID>978-3-642-20844-7</BookID><BookTitle>Graph-Based Representations in Pattern Recognition</BookTitle><BookSubTitle>8th IAPR-TC-15 International Workshop, GbRPR 2011, Münster, Germany, May 18-20, 2011. Proceedings</BookSubTitle><BookVolumeNumber>6658</BookVolumeNumber><BookSequenceNumber>6658</BookSequenceNumber><BookDOI>10.1007/978-3-642-20844-7</BookDOI><BookTitleID>270166</BookTitleID><BookPrintISBN>978-3-642-20843-0</BookPrintISBN><BookElectronicISBN>978-3-642-20844-7</BookElectronicISBN><BookChapterCount>34</BookChapterCount><BookCopyright><CopyrightHolderName>Springer Berlin Heidelberg</CopyrightHolderName><CopyrightYear>2011</CopyrightYear></BookCopyright><BookSubjectGroup><BookSubject Code="I" Type="Primary">Computer Science</BookSubject><BookSubject Code="I2203X" Priority="1" Type="Secondary">Pattern Recognition</BookSubject><BookSubject Code="I22021" Priority="2" Type="Secondary">Image Processing and Computer Vision</BookSubject><BookSubject Code="I22005" Priority="3" Type="Secondary">Computer Imaging, Vision, Pattern Recognition and Graphics</BookSubject><BookSubject Code="I22013" Priority="4" Type="Secondary">Computer Graphics</BookSubject><BookSubject Code="I17028" Priority="5" Type="Secondary">Discrete Mathematics in Computer Science</BookSubject><BookSubject Code="I21017" Priority="6" Type="Secondary">Artificial Intelligence (incl. Robotics)</BookSubject><SubjectCollection Code="SUCO11645">Computer Science</SubjectCollection></BookSubjectGroup><BookContext><SeriesID>558</SeriesID></BookContext></BookInfo><BookHeader><EditorGroup><Editor AffiliationIDS="Aff16"><EditorName DisplayOrder="Western"><GivenName>Xiaoyi</GivenName><FamilyName>Jiang</FamilyName></EditorName><Contact><Email>xjiang@uni-muenster.de</Email></Contact></Editor><Editor AffiliationIDS="Aff17"><EditorName DisplayOrder="Western"><GivenName>Miquel</GivenName><FamilyName>Ferrer</FamilyName></EditorName><Contact><Email>mferrer@iri.upc.edu</Email></Contact></Editor><Editor AffiliationIDS="Aff18"><EditorName DisplayOrder="Western"><GivenName>Andrea</GivenName><FamilyName>Torsello</FamilyName></EditorName><Contact><Email>torsello@dsi.unive.it</Email></Contact></Editor><Affiliation ID="Aff16"><OrgDivision>Department of Mathematics and Computer Science</OrgDivision><OrgName>University of Münster</OrgName><OrgAddress><Street>Einsteinstraße 62</Street><Postcode>48149</Postcode><City>Münster</City><Country>Germany</Country></OrgAddress></Affiliation><Affiliation ID="Aff17"><OrgDivision>Institut de Robòtica i Informàtica Industrial</OrgDivision><OrgName>Universitat Politècnica de Catalunya</OrgName><OrgAddress><Street>C. Llorens i Artigas 4-6</Street><Postcode>08028</Postcode><City>Barcelona</City><Country>Spain</Country></OrgAddress></Affiliation><Affiliation ID="Aff18"><OrgDivision>Dipartimento di Scienze Ambientali, Informatica, Statistica</OrgDivision><OrgName>Università Ca’ Foscari di Venezia</OrgName><OrgAddress><Street>Via Torino 155</Street><Postcode>30172</Postcode><City>Venezia Mestre</City><Country>Italy</Country></OrgAddress></Affiliation></EditorGroup></BookHeader><Part ID="Part1"><PartInfo TocLevels="0"><PartID>1</PartID><PartSequenceNumber>1</PartSequenceNumber><PartTitle>Graph Representation and Characterization</PartTitle><PartChapterCount>8</PartChapterCount><PartContext><SeriesID>558</SeriesID><BookTitle>Graph-Based Representations in Pattern Recognition</BookTitle></PartContext></PartInfo><Chapter ID="Chap8" Language="En"><ChapterInfo ChapterType="OriginalPaper" ContainsESM="No" NumberingDepth="2" NumberingStyle="ContentOnly" TocLevels="0"><ChapterID>8</ChapterID><ChapterDOI>10.1007/978-3-642-20844-7_8</ChapterDOI><ChapterSequenceNumber>8</ChapterSequenceNumber><ChapterTitle Language="En">Towards Performance Evaluation of Graph-Based Representation</ChapterTitle><ChapterFirstPage>72</ChapterFirstPage><ChapterLastPage>81</ChapterLastPage><ChapterCopyright><CopyrightHolderName>Springer-Verlag Berlin Heidelberg</CopyrightHolderName><CopyrightYear>2011</CopyrightYear></ChapterCopyright><ChapterGrants Type="Regular"><MetadataGrant Grant="OpenAccess"></MetadataGrant><AbstractGrant Grant="OpenAccess"></AbstractGrant><BodyPDFGrant Grant="Restricted"></BodyPDFGrant><BodyHTMLGrant Grant="Restricted"></BodyHTMLGrant><BibliographyGrant Grant="Restricted"></BibliographyGrant><ESMGrant Grant="Restricted"></ESMGrant></ChapterGrants><ChapterContext><SeriesID>558</SeriesID><PartID>1</PartID><BookID>978-3-642-20844-7</BookID><BookTitle>Graph-Based Representations in Pattern Recognition</BookTitle></ChapterContext></ChapterInfo><ChapterHeader><AuthorGroup><Author AffiliationIDS="Aff19"><AuthorName DisplayOrder="Western"><GivenName>Salim</GivenName><FamilyName>Jouili</FamilyName></AuthorName><Contact><Email>salim.jouili@loria.fr</Email></Contact></Author><Author AffiliationIDS="Aff19"><AuthorName DisplayOrder="Western"><GivenName>Salvatore</GivenName><FamilyName>Tabbone</FamilyName></AuthorName><Contact><Email>tabbone@loria.fr</Email></Contact></Author><Affiliation ID="Aff19"><OrgName>LORIA UMR 7503 - University of Nancy 2</OrgName><OrgAddress><Postbox>BP 239</Postbox><Postcode>54506</Postcode><City>Vandoeuvre-lès-Nancy Cedex</City><Country>France</Country></OrgAddress></Affiliation></AuthorGroup><Abstract ID="Abs1" Language="En"><Heading>Abstract</Heading><Para>Graphs give a universal and flexible framework to describe the structure and relationship between objects. They are useful in many different application domains like pattern recognition, computer vision and image analysis. In the image analysis context, images can be represented as graphs such that the nodes describe the features and the edges describe their relations. In this paper we, firstly, review the graph-based representations commonly used in the literature. Secondly, we discuss, empirically, the choice of a graph-based representation on three different image databases and show that the representation has a real impact on the method performances and experimental results in the literature on graph performance evaluation for similarity measures should be considered carefully.</Para></Abstract></ChapterHeader><NoBody></NoBody></Chapter></Part></Book></Series></Publisher></istex:document></istex:metadataXml><mods version="3.6"><titleInfo lang="en"><title>Towards Performance Evaluation of Graph-Based Representation</title></titleInfo><titleInfo type="alternative" contentType="CDATA"><title>Towards Performance Evaluation of Graph-Based Representation</title></titleInfo><name type="personal"><namePart type="given">Salim</namePart><namePart type="family">Jouili</namePart><affiliation>LORIA UMR 7503 - University of Nancy 2, BP 239, 54506, Vandoeuvre-lès-Nancy Cedex, France</affiliation><affiliation>E-mail: salim.jouili@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 Cedex, France</affiliation><affiliation>E-mail: tabbone@loria.fr</affiliation><role><roleTerm type="text">author</roleTerm></role></name><typeOfResource>text</typeOfResource><genre displayLabel="OriginalPaper" authority="ISTEX" authorityURI="https://content-type.data.istex.fr" type="conference" valueURI="https://content-type.data.istex.fr/ark:/67375/XTP-BFHXPBJJ-3">conference</genre><originInfo><publisher>Springer Berlin Heidelberg</publisher><place><placeTerm type="text">Berlin, Heidelberg</placeTerm></place><dateIssued encoding="w3cdtf">2011</dateIssued><copyrightDate encoding="w3cdtf">2011</copyrightDate></originInfo><language><languageTerm type="code" authority="rfc3066">en</languageTerm><languageTerm type="code" authority="iso639-2b">eng</languageTerm></language><abstract lang="en">Abstract: Graphs give a universal and flexible framework to describe the structure and relationship between objects. They are useful in many different application domains like pattern recognition, computer vision and image analysis. In the image analysis context, images can be represented as graphs such that the nodes describe the features and the edges describe their relations. In this paper we, firstly, review the graph-based representations commonly used in the literature. Secondly, we discuss, empirically, the choice of a graph-based representation on three different image databases and show that the representation has a real impact on the method performances and experimental results in the literature on graph performance evaluation for similarity measures should be considered carefully.</abstract><relatedItem type="host"><titleInfo><title>Graph-Based Representations in Pattern Recognition</title><subTitle>8th IAPR-TC-15 International Workshop, GbRPR 2011, Münster, Germany, May 18-20, 2011. Proceedings</subTitle></titleInfo><name type="personal"><namePart type="given">Xiaoyi</namePart><namePart type="family">Jiang</namePart><affiliation>Department of Mathematics and Computer Science, University of Münster, Einsteinstraße 62, 48149, Münster, Germany</affiliation><affiliation>E-mail: xjiang@uni-muenster.de</affiliation><role><roleTerm type="text">editor</roleTerm></role></name><name type="personal"><namePart type="given">Miquel</namePart><namePart type="family">Ferrer</namePart><affiliation>Institut de Robòtica i Informàtica Industrial, Universitat Politècnica de Catalunya, C. Llorens i Artigas 4-6, 08028, Barcelona, Spain</affiliation><affiliation>E-mail: mferrer@iri.upc.edu</affiliation><role><roleTerm type="text">editor</roleTerm></role></name><name type="personal"><namePart type="given">Andrea</namePart><namePart type="family">Torsello</namePart><affiliation>Dipartimento di Scienze Ambientali, Informatica, Statistica, Università Ca’ Foscari di Venezia, Via Torino 155, 30172, Venezia Mestre, Italy</affiliation><affiliation>E-mail: torsello@dsi.unive.it</affiliation><role><roleTerm type="text">editor</roleTerm></role></name><genre type="book-series" authority="ISTEX" authorityURI="https://publication-type.data.istex.fr" valueURI="https://publication-type.data.istex.fr/ark:/67375/JMC-0G6R5W5T-Z">book-series</genre><originInfo><publisher>Springer</publisher><copyrightDate encoding="w3cdtf">2011</copyrightDate><issuance>monographic</issuance></originInfo><subject><genre>Book-Subject-Collection</genre><topic authority="SpringerSubjectCodes" authorityURI="SUCO11645">Computer Science</topic></subject><subject><genre>Book-Subject-Group</genre><topic authority="SpringerSubjectCodes" authorityURI="I">Computer Science</topic><topic authority="SpringerSubjectCodes" authorityURI="I2203X">Pattern Recognition</topic><topic authority="SpringerSubjectCodes" authorityURI="I22021">Image Processing and Computer Vision</topic><topic authority="SpringerSubjectCodes" authorityURI="I22005">Computer Imaging, Vision, Pattern Recognition and Graphics</topic><topic authority="SpringerSubjectCodes" authorityURI="I22013">Computer Graphics</topic><topic authority="SpringerSubjectCodes" authorityURI="I17028">Discrete Mathematics in Computer Science</topic><topic authority="SpringerSubjectCodes" authorityURI="I21017">Artificial Intelligence (incl. 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type="series"><titleInfo><title>Lecture Notes in Computer Science</title></titleInfo><name type="personal"><namePart type="given">David</namePart><namePart type="family">Hutchison</namePart><affiliation>Lancaster University, Lancaster, UK</affiliation><role><roleTerm type="text">editor</roleTerm></role></name><name type="personal"><namePart type="given">Takeo</namePart><namePart type="family">Kanade</namePart><affiliation>Carnegie Mellon University, Pittsburgh, PA, USA</affiliation><role><roleTerm type="text">editor</roleTerm></role></name><name type="personal"><namePart type="given">Josef</namePart><namePart type="family">Kittler</namePart><affiliation>University of Surrey, Guildford, UK</affiliation><role><roleTerm type="text">editor</roleTerm></role></name><name type="personal"><namePart type="given">Jon</namePart><namePart type="given">M.</namePart><namePart type="family">Kleinberg</namePart><affiliation>Cornell University, Ithaca, NY, USA</affiliation><role><roleTerm type="text">editor</roleTerm></role></name><name type="personal"><namePart type="given">Friedemann</namePart><namePart type="family">Mattern</namePart><affiliation>ETH Zurich, Zurich, Switzerland</affiliation><role><roleTerm type="text">editor</roleTerm></role></name><name type="personal"><namePart type="given">John</namePart><namePart type="given">C.</namePart><namePart type="family">Mitchell</namePart><affiliation>Stanford University, Stanford, CA, USA</affiliation><role><roleTerm type="text">editor</roleTerm></role></name><name type="personal"><namePart type="given">Moni</namePart><namePart type="family">Naor</namePart><affiliation>Weizmann Institute of Science, Rehovot, Israel</affiliation><role><roleTerm type="text">editor</roleTerm></role></name><name type="personal"><namePart type="given">Oscar</namePart><namePart type="family">Nierstrasz</namePart><affiliation>University of Bern, Bern, Switzerland</affiliation><role><roleTerm type="text">editor</roleTerm></role></name><name 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