An Evolutionary Approach for Learning Motion Class Patterns
Identifieur interne : 000E57 ( Istex/Corpus ); précédent : 000E56; suivant : 000E58An Evolutionary Approach for Learning Motion Class Patterns
Auteurs : Meinard Müller ; Bastian Demuth ; Bodo RosenhahnSource :
- Lecture Notes in Computer Science [ 0302-9743 ] ; 2008.
English descriptors
- Teeft :
- Additional element, Algorithm, Alternative feature vectors, Col1, Col1 col2, Computer animation, Computer vision, Correct motions, Current population, Early convergence, Evolutionary approach, Evolutionary process, Expectation value, Feat, Feat feat1, Feat1, Feat1 feat2, Feat2, Feature function, Feature sequence, Feature vectors, Fourth column, Fuzzy queries, Fuzzy query, Fuzzy sets, Genetic algorithm, Human motion, Initial population, Matrix, Mocap, Mocap data, Mocap data stream, Motion class, Motion class pattern, Motion class patterns, Motion classes, Motion retrieval, Mutation, Negative training motions, Next generation, Parent individuals, Positive training motions, Precision values, Query, Recombination, Recombination step, Reference motion, Relational feature, Relational features, Reproduction process, Retrieval, Retrieval performance, Retrieval quality, Retrieval results, Right knee, Rosenhahn, Temporal variations, Tness, Tness function, Tness value, Tness values.
Abstract
Abstract: This article presents a genetic learning algorithm to derive discrete patterns that can be used for classification and retrieval of 3D motion capture data. Based on boolean motion features, the idea is to learn motion class patterns in an evolutionary process with the objective to discriminate a given set of positive from a given set of negative training motions. Here, the fitness of a pattern is measured with respect to precision and recall in a retrieval scenario, where the pattern is used as a motion query. Our experiments show that motion class patterns can automate query specification without loss of retrieval quality.
Url:
DOI: 10.1007/978-3-540-69321-5_37
Links to Exploration step
ISTEX:8DC325B1F4752CCCC684E1A308B8A1487A1A3E2BLe document en format XML
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Based on boolean motion features, the idea is to learn motion class patterns in an evolutionary process with the objective to discriminate a given set of positive from a given set of negative training motions. Here, the fitness of a pattern is measured with respect to precision and recall in a retrieval scenario, where the pattern is used as a motion query. Our experiments show that motion class patterns can automate query specification without loss of retrieval quality.</p></abstract><textClass><keywords scheme="Book-Subject-Collection"><list><label>SUCO11645</label><item><term>Computer Science</term></item></list></keywords></textClass><textClass><keywords scheme="Book-Subject-Group"><list><label>I</label><item><term>Computer Science</term></item><label>I2203X</label><item><term>Pattern Recognition</term></item><label>I22021</label><item><term>Image Processing and Computer Vision</term></item><label>I21017</label><item><term>Artificial Intelligence (incl. 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Robotics)</BookSubject><BookSubject Code="I22013" Priority="4" Type="Secondary">Computer Graphics</BookSubject><BookSubject Code="I16021" Priority="5" Type="Secondary">Algorithm Analysis and Problem Complexity</BookSubject><SubjectCollection Code="SUCO11645">Computer Science</SubjectCollection></BookSubjectGroup></BookInfo><BookHeader><EditorGroup><Editor><EditorName DisplayOrder="Western"><GivenName>Gerhard</GivenName><FamilyName>Rigoll</FamilyName></EditorName><Contact><Email>rigoll@tum.de</Email></Contact></Editor></EditorGroup></BookHeader><Part ID="Part6"><PartInfo TocLevels="0"><PartID>6</PartID><PartSequenceNumber>6</PartSequenceNumber><PartTitle>Motion and Matching</PartTitle><PartChapterCount>10</PartChapterCount><PartContext><SeriesID>558</SeriesID><BookTitle>Pattern Recognition</BookTitle></PartContext></PartInfo><Chapter ID="Chap37" Language="En"><ChapterInfo ChapterType="OriginalPaper" ContainsESM="No" NumberingStyle="Unnumbered" TocLevels="0"><ChapterID>37</ChapterID><ChapterDOI>10.1007/978-3-540-69321-5_37</ChapterDOI><ChapterSequenceNumber>37</ChapterSequenceNumber><ChapterTitle Language="En">An Evolutionary Approach for Learning Motion Class Patterns</ChapterTitle><ChapterFirstPage>365</ChapterFirstPage><ChapterLastPage>374</ChapterLastPage><ChapterCopyright><CopyrightHolderName>Springer-Verlag Berlin Heidelberg</CopyrightHolderName><CopyrightYear>2008</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>6</PartID><BookID>978-3-540-69321-5</BookID><BookTitle>Pattern Recognition</BookTitle></ChapterContext></ChapterInfo><ChapterHeader><AuthorGroup><Author AffiliationIDS="Aff1"><AuthorName DisplayOrder="Western"><GivenName>Meinard</GivenName><FamilyName>Müller</FamilyName></AuthorName><Contact><Email>meinard@mpi-inf.mpg.de</Email></Contact></Author><Author AffiliationIDS="Aff2"><AuthorName DisplayOrder="Western"><GivenName>Bastian</GivenName><FamilyName>Demuth</FamilyName></AuthorName></Author><Author AffiliationIDS="Aff1"><AuthorName DisplayOrder="Western"><GivenName>Bodo</GivenName><FamilyName>Rosenhahn</FamilyName></AuthorName></Author><Affiliation ID="Aff1"><OrgName>Max-Planck-Institut für Informatik</OrgName><OrgAddress><Street>Campus E1-4</Street><Postcode>66123</Postcode><City>Saarbrücken</City><Country>Germany</Country></OrgAddress></Affiliation><Affiliation ID="Aff2"><OrgDivision>Institut für Informatik III</OrgDivision><OrgName>Universität Bonn</OrgName><OrgAddress><Street>Römerstr. 164</Street><Postcode>53117</Postcode><City>Bonn</City><Country>Germany</Country></OrgAddress></Affiliation></AuthorGroup><Abstract ID="Abs1" Language="En"><Heading>Abstract</Heading><Para>This article presents a genetic learning algorithm to derive discrete patterns that can be used for classification and retrieval of 3D motion capture data. Based on boolean motion features, the idea is to learn motion class patterns in an evolutionary process with the objective to discriminate a given set of positive from a given set of negative training motions. Here, the fitness of a pattern is measured with respect to precision and recall in a retrieval scenario, where the pattern is used as a motion query. Our experiments show that motion class patterns can automate query specification without loss of retrieval quality.</Para></Abstract></ChapterHeader><NoBody></NoBody></Chapter></Part></Book></Series></Publisher></istex:document></istex:metadataXml><mods version="3.6"><titleInfo lang="en"><title>An Evolutionary Approach for Learning Motion Class Patterns</title></titleInfo><titleInfo type="alternative" contentType="CDATA" lang="en"><title>An Evolutionary Approach for Learning Motion Class Patterns</title></titleInfo><name type="personal"><namePart type="given">Meinard</namePart><namePart type="family">Müller</namePart><affiliation>Max-Planck-Institut für Informatik, Campus E1-4, 66123, Saarbrücken, Germany</affiliation><affiliation>E-mail: meinard@mpi-inf.mpg.de</affiliation><role><roleTerm type="text">author</roleTerm></role></name><name type="personal"><namePart type="given">Bastian</namePart><namePart type="family">Demuth</namePart><affiliation>Institut für Informatik III, Universität Bonn, Römerstr. 164, 53117, Bonn, Germany</affiliation><role><roleTerm type="text">author</roleTerm></role></name><name type="personal"><namePart type="given">Bodo</namePart><namePart type="family">Rosenhahn</namePart><affiliation>Max-Planck-Institut für Informatik, Campus E1-4, 66123, Saarbrücken, Germany</affiliation><role><roleTerm type="text">author</roleTerm></role></name><typeOfResource>text</typeOfResource><genre type="conference" displayLabel="OriginalPaper"></genre><originInfo><publisher>Springer Berlin Heidelberg</publisher><place><placeTerm type="text">Berlin, Heidelberg</placeTerm></place><dateIssued encoding="w3cdtf">2008</dateIssued><copyrightDate encoding="w3cdtf">2008</copyrightDate></originInfo><language><languageTerm type="code" authority="rfc3066">en</languageTerm><languageTerm type="code" authority="iso639-2b">eng</languageTerm></language><physicalDescription><internetMediaType>text/html</internetMediaType></physicalDescription><abstract lang="en">Abstract: This article presents a genetic learning algorithm to derive discrete patterns that can be used for classification and retrieval of 3D motion capture data. Based on boolean motion features, the idea is to learn motion class patterns in an evolutionary process with the objective to discriminate a given set of positive from a given set of negative training motions. Here, the fitness of a pattern is measured with respect to precision and recall in a retrieval scenario, where the pattern is used as a motion query. Our experiments show that motion class patterns can automate query specification without loss of retrieval quality.</abstract><relatedItem type="host"><titleInfo><title>Pattern Recognition</title><subTitle>30th DAGM Symposium Munich, Germany, June 10-13, 2008 Proceedings</subTitle></titleInfo><name type="personal"><namePart type="given">Gerhard</namePart><namePart type="family">Rigoll</namePart><role><roleTerm type="text">editor</roleTerm></role></name><genre type="book-series" displayLabel="Proceedings"></genre><originInfo><copyrightDate encoding="w3cdtf">2008</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="I21017">Artificial Intelligence (incl. Robotics)</topic><topic authority="SpringerSubjectCodes" authorityURI="I22013">Computer Graphics</topic><topic authority="SpringerSubjectCodes" authorityURI="I16021">Algorithm Analysis and Problem Complexity</topic></subject><identifier type="DOI">10.1007/978-3-540-69321-5</identifier><identifier type="ISBN">978-3-540-69320-8</identifier><identifier type="eISBN">978-3-540-69321-5</identifier><identifier type="ISSN">0302-9743</identifier><identifier type="eISSN">1611-3349</identifier><identifier type="BookTitleID">162442</identifier><identifier type="BookID">978-3-540-69321-5</identifier><identifier type="BookChapterCount">53</identifier><identifier type="BookVolumeNumber">5096</identifier><identifier type="BookSequenceNumber">5096</identifier><identifier type="PartChapterCount">10</identifier><part><date>2008</date><detail type="part"><title>Motion and Matching</title></detail><detail type="volume"><number>5096</number><caption>vol.</caption></detail><extent unit="pages"><start>365</start><end>374</end></extent></part><recordInfo><recordOrigin>Springer-Verlag Berlin Heidelberg, 2008</recordOrigin></recordInfo></relatedItem><relatedItem type="series"><titleInfo><title>Lecture Notes in Computer Science</title></titleInfo><name type="personal"><namePart type="given">David</namePart><namePart type="family">Hutchison</namePart><role><roleTerm type="text">editor</roleTerm></role></name><name type="personal"><namePart type="given">Takeo</namePart><namePart type="family">Kanade</namePart><role><roleTerm type="text">editor</roleTerm></role></name><name type="personal"><namePart type="given">Josef</namePart><namePart type="family">Kittler</namePart><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><role><roleTerm type="text">editor</roleTerm></role></name><name type="personal"><namePart type="given">Friedemann</namePart><namePart type="family">Mattern</namePart><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><role><roleTerm type="text">editor</roleTerm></role></name><name type="personal"><namePart type="given">Moni</namePart><namePart type="family">Naor</namePart><role><roleTerm type="text">editor</roleTerm></role></name><name type="personal"><namePart type="given">Oscar</namePart><namePart type="family">Nierstrasz</namePart><role><roleTerm type="text">editor</roleTerm></role></name><name type="personal"><namePart type="given">C.</namePart><namePart type="family">Pandu Rangan</namePart><role><roleTerm type="text">editor</roleTerm></role></name><name type="personal"><namePart type="given">Bernhard</namePart><namePart type="family">Steffen</namePart><role><roleTerm type="text">editor</roleTerm></role></name><name type="personal"><namePart type="given">Madhu</namePart><namePart type="family">Sudan</namePart><role><roleTerm type="text">editor</roleTerm></role></name><name type="personal"><namePart type="given">Demetri</namePart><namePart type="family">Terzopoulos</namePart><role><roleTerm type="text">editor</roleTerm></role></name><name type="personal"><namePart type="given">Doug</namePart><namePart type="family">Tygar</namePart><role><roleTerm type="text">editor</roleTerm></role></name><name type="personal"><namePart type="given">Moshe</namePart><namePart type="given">Y.</namePart><namePart type="family">Vardi</namePart><role><roleTerm type="text">editor</roleTerm></role></name><name type="personal"><namePart type="given">Gerhard</namePart><namePart type="family">Weikum</namePart><role><roleTerm type="text">editor</roleTerm></role></name><originInfo><copyrightDate encoding="w3cdtf">2008</copyrightDate><issuance>serial</issuance></originInfo><identifier type="ISSN">0302-9743</identifier><identifier type="eISSN">1611-3349</identifier><identifier type="SeriesID">558</identifier><recordInfo><recordOrigin>Springer-Verlag Berlin Heidelberg, 2008</recordOrigin></recordInfo></relatedItem><identifier type="istex">8DC325B1F4752CCCC684E1A308B8A1487A1A3E2B</identifier><identifier type="DOI">10.1007/978-3-540-69321-5_37</identifier><identifier type="ChapterID">37</identifier><identifier type="ChapterID">Chap37</identifier><accessCondition type="use and reproduction" contentType="copyright">Springer-Verlag Berlin Heidelberg, 2008</accessCondition><recordInfo><recordContentSource>SPRINGER</recordContentSource><recordOrigin>Springer-Verlag Berlin Heidelberg, 2008</recordOrigin></recordInfo></mods></metadata></istex></record>Pour manipuler ce document sous Unix (Dilib)
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