{"id":"https://openalex.org/W347936517","doi":"https://doi.org/10.1109/cvpr.2008.4587787","title":"Max Margin AND/OR Graph learning for parsing the human body","display_name":"Max Margin AND/OR Graph learning for parsing the human body","publication_year":2008,"publication_date":"2008-06-01","ids":{"openalex":"https://openalex.org/W347936517","doi":"https://doi.org/10.1109/cvpr.2008.4587787","mag":"347936517"},"language":"en","primary_location":{"id":"doi:10.1109/cvpr.2008.4587787","is_oa":false,"landing_page_url":"https://doi.org/10.1109/cvpr.2008.4587787","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2008 IEEE Conference on Computer Vision and Pattern Recognition","raw_type":"proceedings-article"},"type":"conference-paper","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://escholarship.org/content/qt01z2v05s/qt01z2v05s.pdf?t=lt7suv","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5111675675","display_name":"Long Zhu","orcid":null},"institutions":[{"id":"https://openalex.org/I161318765","display_name":"University of California, Los Angeles","ror":"https://ror.org/046rm7j60","country_code":"US","type":"education","lineage":["https://openalex.org/I161318765"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Long Zhu","raw_affiliation_strings":["Department of Statistics, University of California, Los Angeles, USA","Dept. of Stat., Univ. of California, Los Angeles, CA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Statistics, University of California, Los Angeles, USA","institution_ids":["https://openalex.org/I161318765"]},{"raw_affiliation_string":"Dept. of Stat., Univ. of California, Los Angeles, CA","institution_ids":["https://openalex.org/I161318765"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5083827460","display_name":"Yuanhao Chen","orcid":"https://orcid.org/0009-0002-0793-0940"},"institutions":[{"id":"https://openalex.org/I126520041","display_name":"University of Science and Technology of China","ror":"https://ror.org/04c4dkn09","country_code":"CN","type":"education","lineage":["https://openalex.org/I126520041","https://openalex.org/I19820366"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yuanhao Chen","raw_affiliation_strings":["University of Science and Technology, China","University of Science and Technology of China, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of Science and Technology, China","institution_ids":["https://openalex.org/I126520041"]},{"raw_affiliation_string":"University of Science and Technology of China, China","institution_ids":["https://openalex.org/I126520041"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101922168","display_name":"Yifei Lu","orcid":"https://orcid.org/0000-0001-9529-7780"},"institutions":[{"id":"https://openalex.org/I183067930","display_name":"Shanghai Jiao Tong University","ror":"https://ror.org/0220qvk04","country_code":"CN","type":"education","lineage":["https://openalex.org/I183067930"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yifei Lu","raw_affiliation_strings":["Shanghai Jiaotong University, China","[Shanghai Jiao Tong University, China]"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Shanghai Jiaotong University, China","institution_ids":["https://openalex.org/I183067930"]},{"raw_affiliation_string":"[Shanghai Jiao Tong University, China]","institution_ids":["https://openalex.org/I183067930"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5028846979","display_name":"Chenxi Lin","orcid":"https://orcid.org/0000-0003-4439-290X"},"institutions":[{"id":"https://openalex.org/I1290206253","display_name":"Microsoft (United States)","ror":"https://ror.org/00d0nc645","country_code":"US","type":"company","lineage":["https://openalex.org/I1290206253"]},{"id":"https://openalex.org/I4210113369","display_name":"Microsoft Research Asia (China)","ror":"https://ror.org/0300m5276","country_code":"CN","type":"company","lineage":["https://openalex.org/I1290206253","https://openalex.org/I4210113369"]}],"countries":["CN","US"],"is_corresponding":false,"raw_author_name":"Chenxi Lin","raw_affiliation_strings":["Microsoft Research Asia, China","[Microsoft Research Asia, CHINA]"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Microsoft Research Asia, China","institution_ids":["https://openalex.org/I4210113369"]},{"raw_affiliation_string":"[Microsoft Research Asia, CHINA]","institution_ids":["https://openalex.org/I1290206253"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5086706224","display_name":"Alan Yuille","orcid":"https://orcid.org/0000-0001-5207-9249"},"institutions":[{"id":"https://openalex.org/I161318765","display_name":"University of California, Los Angeles","ror":"https://ror.org/046rm7j60","country_code":"US","type":"education","lineage":["https://openalex.org/I161318765"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Alan Yuille","raw_affiliation_strings":["Department of Statistics, Psychology and Computer Science, University of California, Los Angeles, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Statistics, Psychology and Computer Science, University of California, Los Angeles, USA","institution_ids":["https://openalex.org/I161318765"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":5,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":true,"cited_by_count":2,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"8"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10992","display_name":"Forensic Anthropology and Bioarchaeology Studies","score":0.9790999889373779,"subfield":{"id":"https://openalex.org/subfields/1204","display_name":"Archeology"},"field":{"id":"https://openalex.org/fields/12","display_name":"Arts and Humanities"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}},"topics":[{"id":"https://openalex.org/T10992","display_name":"Forensic Anthropology and Bioarchaeology Studies","score":0.9790999889373779,"subfield":{"id":"https://openalex.org/subfields/1204","display_name":"Archeology"},"field":{"id":"https://openalex.org/fields/12","display_name":"Arts and Humanities"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}},{"id":"https://openalex.org/T10812","display_name":"Human Pose and Action Recognition","score":0.9779999852180481,"subfield":{"id":"https://openalex.org/subfields/1707","display_name":"Computer Vision and Pattern Recognition"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T11448","display_name":"Face recognition and analysis","score":0.9764999747276306,"subfield":{"id":"https://openalex.org/subfields/1707","display_name":"Computer Vision and Pattern Recognition"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/discriminative-model","display_name":"Discriminative model","score":0.7467440366744995},{"id":"https://openalex.org/keywords/margin","display_name":"Margin (machine learning)","score":0.6683506965637207},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6468446254730225},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.629970908164978},{"id":"https://openalex.org/keywords/graph","display_name":"Graph","score":0.5700545907020569},{"id":"https://openalex.org/keywords/support-vector-machine","display_name":"Support vector machine","score":0.5309844613075256},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.4947945475578308},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.482791543006897},{"id":"https://openalex.org/keywords/parsing","display_name":"Parsing","score":0.47903886437416077},{"id":"https://openalex.org/keywords/markov-chain","display_name":"Markov chain","score":0.4709875285625458},{"id":"https://openalex.org/keywords/graphical-model","display_name":"Graphical model","score":0.41230276226997375},{"id":"https://openalex.org/keywords/theoretical-computer-science","display_name":"Theoretical computer science","score":0.225603848695755}],"concepts":[{"id":"https://openalex.org/C97931131","wikidata":"https://www.wikidata.org/wiki/Q5282087","display_name":"Discriminative model","level":2,"score":0.7467440366744995},{"id":"https://openalex.org/C774472","wikidata":"https://www.wikidata.org/wiki/Q6760393","display_name":"Margin (machine learning)","level":2,"score":0.6683506965637207},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6468446254730225},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.629970908164978},{"id":"https://openalex.org/C132525143","wikidata":"https://www.wikidata.org/wiki/Q141488","display_name":"Graph","level":2,"score":0.5700545907020569},{"id":"https://openalex.org/C12267149","wikidata":"https://www.wikidata.org/wiki/Q282453","display_name":"Support vector machine","level":2,"score":0.5309844613075256},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.4947945475578308},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.482791543006897},{"id":"https://openalex.org/C186644900","wikidata":"https://www.wikidata.org/wiki/Q194152","display_name":"Parsing","level":2,"score":0.47903886437416077},{"id":"https://openalex.org/C98763669","wikidata":"https://www.wikidata.org/wiki/Q176645","display_name":"Markov chain","level":2,"score":0.4709875285625458},{"id":"https://openalex.org/C155846161","wikidata":"https://www.wikidata.org/wiki/Q1143367","display_name":"Graphical model","level":2,"score":0.41230276226997375},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.225603848695755}],"mesh":[],"locations_count":7,"locations":[{"id":"doi:10.1109/cvpr.2008.4587787","is_oa":false,"landing_page_url":"https://doi.org/10.1109/cvpr.2008.4587787","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2008 IEEE Conference on Computer Vision and Pattern Recognition","raw_type":"proceedings-article"},{"id":"pmh:oai:escholarship.org/ark:/13030/qt01z2v05s","is_oa":true,"landing_page_url":"https://escholarship.org/uc/item/01z2v05s","pdf_url":"https://escholarship.org/content/qt01z2v05s/qt01z2v05s.pdf?t=lt7suv","source":{"id":"https://openalex.org/S4306400115","display_name":"eScholarship (California Digital Library)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I2801248553","host_organization_name":"California Digital Library","host_organization_lineage":["https://openalex.org/I2801248553"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"","raw_type":"article"},{"id":"pmh:oai:CiteSeerX.psu:10.1.1.321.5977","is_oa":false,"landing_page_url":"http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.321.5977","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"http://www.stat.ucla.edu/~yuille/pubs/ucla/A226_lzhuCVPR2008a.pdf","raw_type":"text"},{"id":"pmh:oai:escholarship.org/ark:/13030/qt1656t4t9","is_oa":false,"landing_page_url":"https://escholarship.org/uc/item/1656t4t9","pdf_url":null,"source":{"id":"https://openalex.org/S4306400115","display_name":"eScholarship (California Digital Library)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I2801248553","host_organization_name":"California Digital Library","host_organization_lineage":["https://openalex.org/I2801248553"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"","raw_type":"article"},{"id":"pmh:qt01z2v05s","is_oa":false,"landing_page_url":"http://www.escholarship.org/uc/item/01z2v05s","pdf_url":null,"source":{"id":"https://openalex.org/S4306400115","display_name":"eScholarship (California Digital Library)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I2801248553","host_organization_name":"California Digital Library","host_organization_lineage":["https://openalex.org/I2801248553"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"Zhu, Long Leo; Chen, Yuanhao; Lu, Yifei; Lin, Chenxi; & Yuille, Alan. (2008). Max Margin AND/OR Graph Learning for Parsing the Human Body. UCLA: Department of Statistics, UCLA. Retrieved from: http://www.escholarship.org/uc/item/01z2v05s","raw_type":"article"},{"id":"pmh:qt1656t4t9","is_oa":false,"landing_page_url":"http://www.escholarship.org/uc/item/1656t4t9","pdf_url":null,"source":{"id":"https://openalex.org/S4306400115","display_name":"eScholarship (California Digital Library)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I2801248553","host_organization_name":"California Digital Library","host_organization_lineage":["https://openalex.org/I2801248553"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"Long (Leo) Zhu; Yuanhao Chen; Yifei Lu; Chenxi Lin; & Alan Yuille. (2011). Max Margin AND/OR Graph Learning for Parsing the Human Body. Department of Statistics, UCLA. UCLA: Department of Statistics, UCLA. Retrieved from: http://www.escholarship.org/uc/item/1656t4t9","raw_type":"article"},{"id":"mag:347936517","is_oa":false,"landing_page_url":"https://escholarship.org/uc/item/1656t4t9.pdf","pdf_url":null,"source":{"id":"https://openalex.org/S4306507726","display_name":"Department of Statistics, UCLA","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"journal"},"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":null,"raw_source_name":"Department of Statistics, UCLA","raw_type":null}],"best_oa_location":{"id":"pmh:oai:escholarship.org/ark:/13030/qt01z2v05s","is_oa":true,"landing_page_url":"https://escholarship.org/uc/item/01z2v05s","pdf_url":"https://escholarship.org/content/qt01z2v05s/qt01z2v05s.pdf?t=lt7suv","source":{"id":"https://openalex.org/S4306400115","display_name":"eScholarship (California Digital Library)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I2801248553","host_organization_name":"California Digital Library","host_organization_lineage":["https://openalex.org/I2801248553"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"","raw_type":"article"},"sustainable_development_goals":[{"display_name":"Reduced inequalities","id":"https://metadata.un.org/sdg/10","score":0.7300000190734863}],"awards":[{"id":"https://openalex.org/G6646900933","display_name":"Image Parsing: Integrating Generative and Discriminative Methods","funder_award_id":"0413214","funder_id":"https://openalex.org/F4320306076","funder_display_name":"National Science Foundation"}],"funders":[{"id":"https://openalex.org/F4320306076","display_name":"National Science Foundation","ror":"https://ror.org/021nxhr62"}],"has_content":{"grobid_xml":true,"pdf":true},"content_urls":{"pdf":"https://content.openalex.org/works/W347936517.pdf","grobid_xml":"https://content.openalex.org/works/W347936517.grobid-xml"},"referenced_works_count":24,"referenced_works":["https://openalex.org/W1563088657","https://openalex.org/W2018793343","https://openalex.org/W2110248298","https://openalex.org/W2124351082","https://openalex.org/W2124351162","https://openalex.org/W2143158307","https://openalex.org/W2156909104","https://openalex.org/W2429914308","https://openalex.org/W6604990878","https://openalex.org/W6605963652","https://openalex.org/W6655406981","https://openalex.org/W6675695589","https://openalex.org/W6675696094","https://openalex.org/W6675760969","https://openalex.org/W6675961540","https://openalex.org/W6676020639","https://openalex.org/W6678637433","https://openalex.org/W6680114966","https://openalex.org/W6680627905","https://openalex.org/W6680747436","https://openalex.org/W6682082992","https://openalex.org/W6682953061","https://openalex.org/W6683590607","https://openalex.org/W6684531988"],"related_works":["https://openalex.org/W2142571543","https://openalex.org/W3128327402","https://openalex.org/W1686335701","https://openalex.org/W60448380","https://openalex.org/W3184940953","https://openalex.org/W2513897596","https://openalex.org/W2971297210","https://openalex.org/W2007477772","https://openalex.org/W1991207565","https://openalex.org/W3089885888","https://openalex.org/W2132709984","https://openalex.org/W2801436699","https://openalex.org/W2105644991","https://openalex.org/W3000308450","https://openalex.org/W1975692209","https://openalex.org/W2810172011","https://openalex.org/W3214213376","https://openalex.org/W2961042725","https://openalex.org/W2947209427","https://openalex.org/W2080731696"],"abstract_inverted_index":{"We":[0,169],"present":[1],"a":[2,38,50,98],"novel":[3],"structure":[4],"learning":[5,104],"method,":[6],"Max":[7],"Margin":[8],"AND/OR":[9,34,69,81,88,120],"graph":[10,70,89,121],"(MM-AOG),":[11],"for":[12,56,134],"parsing":[13],"the":[14,26,53,65,68,87,110,116,119,128,131,138,150,174,184],"human":[15,27],"body":[16,28],"into":[17],"parts":[18,31],"and":[19,29,83,137,161],"recovering":[20],"their":[21],"poses.":[22],"Our":[23],"method":[24],"represents":[25],"its":[30],"by":[32],"an":[33],"graph,":[35],"which":[36,48],"is":[37,49,61],"multi-level":[39],"mixture":[40],"of":[41,52,67,80,118,130,141,167,183],"Markov":[42],"random":[43],"fields":[44],"(MRFs).":[45],"Max-margin":[46],"learning,":[47],"generalization":[51],"training":[54],"algorithm":[55],"support":[57],"vector":[58],"machines":[59],"(SVMs),":[60],"used":[62,155],"to":[63,92,156,162],"learn":[64],"parameters":[66,117],"model":[71,122,133],"discriminatively.":[72],"There":[73],"are":[74,123,146],"four":[75],"advantages":[76],"from":[77],"this":[78],"combination":[79],"graphs":[82],"max-margin":[84,103],"learning.":[85],"Firstly,":[86],"allows":[90],"us":[91],"handle":[93,157],"enormous":[94],"articulated":[95],"poses":[96],"with":[97],"compact":[99],"graphical":[100],"model.":[101],"Secondly,":[102],"has":[105],"more":[106],"discriminative":[107],"power":[108],"than":[109],"traditional":[111],"maximum":[112],"likelihood":[113],"approach.":[114],"Thirdly,":[115],"optimized":[124],"globally.":[125],"In":[126],"particular,":[127],"weights":[129],"appearance":[132],"individual":[135],"nodes":[136,145],"relative":[139],"importance":[140],"spatial":[142],"relationships":[143],"between":[144],"learnt":[147],"simultaneously.":[148],"Finally,":[149],"kernel":[151],"trick":[152],"can":[153],"be":[154],"high":[158],"dimensional":[159],"features":[160],"enable":[163],"complex":[164],"similarity":[165],"measure":[166],"shapes.":[168],"perform":[170],"comparison":[171],"experiments":[172],"on":[173],"base":[175],"ball":[176],"datasets,":[177],"showing":[178],"significant":[179],"improvements":[180],"over":[181],"state":[182],"art":[185],"methods.":[186]},"counts_by_year":[{"year":2022,"cited_by_count":1},{"year":2014,"cited_by_count":1}],"updated_date":"2026-07-14T23:27:15.235271","created_date":"2025-10-10T00:00:00"}