Learning Visual Shape Lexicon for Document Image Content Recognition
Identifieur interne : 000C96 ( Istex/Corpus ); précédent : 000C95; suivant : 000C97Learning Visual Shape Lexicon for Document Image Content Recognition
Auteurs : Guangyu Zhu ; Xiaodong Yu ; Yi Li ; David DoermannSource :
- Lecture Notes in Computer Science [ 0302-9743 ] ; 2008.
Abstract
Abstract: Developing effective content recognition methods for diverse imagery continues to challenge computer vision researchers. We present a new approach for document image content categorization using a lexicon of shape features. Each lexical word corresponds to a scale and rotation invariant shape feature that is generic enough to be detected repeatably and segmentation free. We learn a concise, structurally indexed shape lexicon from training by clustering and partitioning feature types through graph cuts. We demonstrate our approach on two challenging document image content recognition problems: 1) The classification of 4,500 Web images crawled from Google Image Search into three content categories — pure image, image with text, and document image, and 2) Language identification of 8 languages (Arabic, Chinese, English, Hindi, Japanese, Korean, Russian, and Thai) on a 1,512 complex document image database composed of mixed machine printed text and handwriting. Our approach is capable to handle high intra-class variability and shows results that exceed other state-of-the-art approaches, allowing it to be used as a content recognizer in image indexing and retrieval systems.
Url:
DOI: 10.1007/978-3-540-88688-4_55
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ISTEX:000EA72B875137D2E35868AFB5C5FCB5D7A54937Le document en format XML
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We present a new approach for document image content categorization using a lexicon of shape features. Each lexical word corresponds to a scale and rotation invariant shape feature that is generic enough to be detected repeatably and segmentation free. We learn a concise, structurally indexed shape lexicon from training by clustering and partitioning feature types through graph cuts. We demonstrate our approach on two challenging document image content recognition problems: 1) The classification of 4,500 Web images crawled from Google Image Search into three content categories — pure image, image with text, and document image, and 2) Language identification of 8 languages (Arabic, Chinese, English, Hindi, Japanese, Korean, Russian, and Thai) on a 1,512 complex document image database composed of mixed machine printed text and handwriting. Our approach is capable to handle high intra-class variability and shows results that exceed other state-of-the-art approaches, allowing it to be used as a content recognizer in image indexing and retrieval systems.</div></front></TEI><istex><corpusName>springer</corpusName><author><json:item><name>Guangyu Zhu</name><affiliations><json:string>University of Maryland, MD 20742, College Park, USA</json:string></affiliations></json:item><json:item><name>Xiaodong Yu</name><affiliations><json:string>University of Maryland, MD 20742, College Park, USA</json:string></affiliations></json:item><json:item><name>Yi Li</name><affiliations><json:string>University of Maryland, MD 20742, College Park, USA</json:string></affiliations></json:item><json:item><name>David Doermann</name><affiliations><json:string>University of Maryland, MD 20742, College Park, USA</json:string></affiliations></json:item></author><language><json:string>eng</json:string></language><abstract>Abstract: Developing effective content recognition methods for diverse imagery continues to challenge computer vision researchers. We present a new approach for document image content categorization using a lexicon of shape features. Each lexical word corresponds to a scale and rotation invariant shape feature that is generic enough to be detected repeatably and segmentation free. We learn a concise, structurally indexed shape lexicon from training by clustering and partitioning feature types through graph cuts. We demonstrate our approach on two challenging document image content recognition problems: 1) The classification of 4,500 Web images crawled from Google Image Search into three content categories — pure image, image with text, and document image, and 2) Language identification of 8 languages (Arabic, Chinese, English, Hindi, Japanese, Korean, Russian, and Thai) on a 1,512 complex document image database composed of mixed machine printed text and handwriting. 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We present a new approach for document image content categorization using a lexicon of shape features. Each lexical word corresponds to a scale and rotation invariant shape feature that is generic enough to be detected repeatably and segmentation free. We learn a concise, structurally indexed shape lexicon from training by clustering and partitioning feature types through graph cuts. We demonstrate our approach on two challenging document image content recognition problems: 1) The classification of 4,500 Web images crawled from Google Image Search into three content categories — pure image, image with text, and document image, and 2) Language identification of 8 languages (Arabic, Chinese, English, Hindi, Japanese, Korean, Russian, and Thai) on a 1,512 complex document image database composed of mixed machine printed text and handwriting. 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2008</BookTitle></PartContext></PartInfo><Chapter ID="Chap55" Language="En"><ChapterInfo ChapterType="OriginalPaper" ContainsESM="No" NumberingStyle="Unnumbered" TocLevels="0"><ChapterID>55</ChapterID><ChapterDOI>10.1007/978-3-540-88688-4_55</ChapterDOI><ChapterSequenceNumber>55</ChapterSequenceNumber><ChapterTitle Language="En">Learning Visual Shape Lexicon for Document Image Content Recognition</ChapterTitle><ChapterFirstPage>745</ChapterFirstPage><ChapterLastPage>758</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>3</PartID><BookID>978-3-540-88688-4</BookID><BookTitle>Computer Vision – ECCV 2008</BookTitle></ChapterContext></ChapterInfo><ChapterHeader><AuthorGroup><Author AffiliationIDS="Aff4"><AuthorName DisplayOrder="Western"><GivenName>Guangyu</GivenName><FamilyName>Zhu</FamilyName></AuthorName></Author><Author AffiliationIDS="Aff4"><AuthorName DisplayOrder="Western"><GivenName>Xiaodong</GivenName><FamilyName>Yu</FamilyName></AuthorName></Author><Author AffiliationIDS="Aff4"><AuthorName DisplayOrder="Western"><GivenName>Yi</GivenName><FamilyName>Li</FamilyName></AuthorName></Author><Author AffiliationIDS="Aff4"><AuthorName DisplayOrder="Western"><GivenName>David</GivenName><FamilyName>Doermann</FamilyName></AuthorName></Author><Affiliation ID="Aff4"><OrgName>University of Maryland</OrgName><OrgAddress><City>College Park</City><Postcode>MD 20742</Postcode><Country>USA</Country></OrgAddress></Affiliation></AuthorGroup><Abstract ID="Abs1" Language="En"><Heading>Abstract</Heading><Para>Developing effective content recognition methods for diverse imagery continues to challenge computer vision researchers. We present a new approach for document image content categorization using a lexicon of shape features. Each lexical word corresponds to a scale and rotation invariant shape feature that is generic enough to be detected repeatably and segmentation free. We learn a concise, structurally indexed shape lexicon from training by clustering and partitioning feature types through graph cuts. We demonstrate our approach on two challenging document image content recognition problems: 1) The classification of 4,500 Web images crawled from Google Image Search into three content categories — pure image, image with text, and document image, and 2) Language identification of 8 languages (Arabic, Chinese, English, Hindi, Japanese, Korean, Russian, and Thai) on a 1,512 complex document image database composed of mixed machine printed text and handwriting. Our approach is capable to handle high intra-class variability and shows results that exceed other state-of-the-art approaches, allowing it to be used as a content recognizer in image indexing and retrieval systems.</Para></Abstract></ChapterHeader><NoBody></NoBody></Chapter></Part></Book></Series></Publisher></istex:document></istex:metadataXml><mods version="3.6"><titleInfo lang="en"><title>Learning Visual Shape Lexicon for Document Image Content Recognition</title></titleInfo><titleInfo type="alternative" contentType="CDATA" lang="en"><title>Learning Visual Shape Lexicon for Document Image Content Recognition</title></titleInfo><name type="personal"><namePart type="given">Guangyu</namePart><namePart type="family">Zhu</namePart><affiliation>University of Maryland, MD 20742, College Park, USA</affiliation><role><roleTerm type="text">author</roleTerm></role></name><name type="personal"><namePart type="given">Xiaodong</namePart><namePart type="family">Yu</namePart><affiliation>University of Maryland, MD 20742, College Park, USA</affiliation><role><roleTerm type="text">author</roleTerm></role></name><name type="personal"><namePart type="given">Yi</namePart><namePart type="family">Li</namePart><affiliation>University of Maryland, MD 20742, College Park, USA</affiliation><role><roleTerm type="text">author</roleTerm></role></name><name type="personal"><namePart type="given">David</namePart><namePart type="family">Doermann</namePart><affiliation>University of Maryland, MD 20742, College Park, USA</affiliation><role><roleTerm type="text">author</roleTerm></role></name><typeOfResource>text</typeOfResource><genre type="conference [eBooks]" 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: Developing effective content recognition methods for diverse imagery continues to challenge computer vision researchers. We present a new approach for document image content categorization using a lexicon of shape features. Each lexical word corresponds to a scale and rotation invariant shape feature that is generic enough to be detected repeatably and segmentation free. We learn a concise, structurally indexed shape lexicon from training by clustering and partitioning feature types through graph cuts. We demonstrate our approach on two challenging document image content recognition problems: 1) The classification of 4,500 Web images crawled from Google Image Search into three content categories — pure image, image with text, and document image, and 2) Language identification of 8 languages (Arabic, Chinese, English, Hindi, Japanese, Korean, Russian, and Thai) on a 1,512 complex document image database composed of mixed machine printed text and handwriting. Our approach is capable to handle high intra-class variability and shows results that exceed other state-of-the-art approaches, allowing it to be used as a content recognizer in image indexing and retrieval systems.</abstract><relatedItem type="host"><titleInfo><title>Computer Vision – ECCV 2008</title><subTitle>10th European Conference on Computer Vision, Marseille, France, October 12-18, 2008, Proceedings, Part II</subTitle></titleInfo><name type="personal"><namePart type="given">David</namePart><namePart type="family">Forsyth</namePart><affiliation>Computer Science Department, University of Illinois at Urbana Champaign, 3310 Siebel Hall, IL 61801, Urbana, USA</affiliation><affiliation>E-mail: daf@cs.uiuc.edu</affiliation><role><roleTerm type="text">editor</roleTerm></role></name><name type="personal"><namePart type="given">Philip</namePart><namePart type="family">Torr</namePart><affiliation>Department of Computing, Oxford Brookes University, OX33 1HX, Wheatley, Oxford, UK</affiliation><affiliation>E-mail: philiptorr@brookes.ac.uk</affiliation><role><roleTerm type="text">editor</roleTerm></role></name><name type="personal"><namePart type="given">Andrew</namePart><namePart type="family">Zisserman</namePart><affiliation>Department of Engineering Science, University of Oxford, Parks Road, OX1 3PJ, Oxford, UK</affiliation><affiliation>E-mail: az@robots.ox.ac.uk</affiliation><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="I22021">Image Processing and Computer 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