{"id":"https://openalex.org/W2165203396","doi":"https://doi.org/10.1109/cvpr.2008.4587345","title":"Unsupervised learning of probabilistic object models (POMs) for object classification, segmentation and recognition","display_name":"Unsupervised learning of probabilistic object models (POMs) for object classification, segmentation and recognition","publication_year":2008,"publication_date":"2008-06-01","ids":{"openalex":"https://openalex.org/W2165203396","doi":"https://doi.org/10.1109/cvpr.2008.4587345","mag":"2165203396"},"language":"en","primary_location":{"id":"doi:10.1109/cvpr.2008.4587345","is_oa":false,"landing_page_url":"https://doi.org/10.1109/cvpr.2008.4587345","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":"article","indexed_in":["crossref"],"open_access":{"is_oa":false,"oa_status":"closed","oa_url":null,"any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5083827460","display_name":"Yuanhao Chen","orcid":"https://orcid.org/0009-0002-0793-0940"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Yuanhao Chen","raw_affiliation_strings":["MOE-MS Key Laboratory of MCC, University of Science and Technology, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"MOE-MS Key Laboratory of MCC, University of Science and Technology, China","institution_ids":[]}]},{"author_position":"middle","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"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Statistics, University of California, Los Angeles, USA","institution_ids":["https://openalex.org/I161318765"]}]},{"author_position":"middle","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"]}]},{"author_position":"last","author":{"id":null,"display_name":"Hongjiang Zhang","orcid":null},"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"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Hongjiang Zhang","raw_affiliation_strings":["Microsoft Advanced Technology Center, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Microsoft Advanced Technology Center, USA","institution_ids":["https://openalex.org/I1290206253"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":4,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":1.42,"has_fulltext":false,"cited_by_count":11,"citation_normalized_percentile":{"value":0.84811646,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":97},"biblio":{"volume":"1","issue":null,"first_page":"1","last_page":"8"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10036","display_name":"Advanced Neural Network Applications","score":0.9988999962806702,"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"}},"topics":[{"id":"https://openalex.org/T10036","display_name":"Advanced Neural Network Applications","score":0.9988999962806702,"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/T10627","display_name":"Advanced Image and Video Retrieval Techniques","score":0.9988999962806702,"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/T11307","display_name":"Domain Adaptation and Few-Shot Learning","score":0.9973000288009644,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"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/inference","display_name":"Inference","score":0.7710668444633484},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.7178515195846558},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7042688131332397},{"id":"https://openalex.org/keywords/segmentation","display_name":"Segmentation","score":0.6522221565246582},{"id":"https://openalex.org/keywords/object","display_name":"Object (grammar)","score":0.586349606513977},{"id":"https://openalex.org/keywords/probabilistic-logic","display_name":"Probabilistic logic","score":0.5352635383605957},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.5280179381370544},{"id":"https://openalex.org/keywords/unsupervised-learning","display_name":"Unsupervised learning","score":0.5233772993087769},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.5136167407035828},{"id":"https://openalex.org/keywords/cognitive-neuroscience-of-visual-object-recognition","display_name":"Cognitive neuroscience of visual object recognition","score":0.5015664100646973},{"id":"https://openalex.org/keywords/image-segmentation","display_name":"Image segmentation","score":0.4451954960823059},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.35630297660827637}],"concepts":[{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.7710668444633484},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7178515195846558},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7042688131332397},{"id":"https://openalex.org/C89600930","wikidata":"https://www.wikidata.org/wiki/Q1423946","display_name":"Segmentation","level":2,"score":0.6522221565246582},{"id":"https://openalex.org/C2781238097","wikidata":"https://www.wikidata.org/wiki/Q175026","display_name":"Object (grammar)","level":2,"score":0.586349606513977},{"id":"https://openalex.org/C49937458","wikidata":"https://www.wikidata.org/wiki/Q2599292","display_name":"Probabilistic logic","level":2,"score":0.5352635383605957},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.5280179381370544},{"id":"https://openalex.org/C8038995","wikidata":"https://www.wikidata.org/wiki/Q1152135","display_name":"Unsupervised learning","level":2,"score":0.5233772993087769},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.5136167407035828},{"id":"https://openalex.org/C64876066","wikidata":"https://www.wikidata.org/wiki/Q5141226","display_name":"Cognitive neuroscience of visual object recognition","level":3,"score":0.5015664100646973},{"id":"https://openalex.org/C124504099","wikidata":"https://www.wikidata.org/wiki/Q56933","display_name":"Image segmentation","level":3,"score":0.4451954960823059},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.35630297660827637}],"mesh":[],"locations_count":4,"locations":[{"id":"doi:10.1109/cvpr.2008.4587345","is_oa":false,"landing_page_url":"https://doi.org/10.1109/cvpr.2008.4587345","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:CiteSeerX.psu:10.1.1.330.2733","is_oa":false,"landing_page_url":"http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.330.2733","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/courses/Stat238-Winter09/pom_pami_alan_f.pdf","raw_type":"text"},{"id":"pmh:oai:CiteSeerX.psu:10.1.1.332.2381","is_oa":false,"landing_page_url":"http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.332.2381","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"http://people.csail.mit.edu/leozhu/paper/pom_cvpr08_final2.pdf","raw_type":"text"},{"id":"pmh:oai:CiteSeerX.psu:10.1.1.408.9670","is_oa":false,"landing_page_url":"http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.408.9670","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"http://mplab.ucsd.edu/wp-content/uploads/cvpr2008/conference/data/papers/005.pdf","raw_type":"text"}],"best_oa_location":null,"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/4","score":0.5099999904632568,"display_name":"Quality Education"}],"awards":[],"funders":[{"id":"https://openalex.org/F4320306076","display_name":"National Science Foundation","ror":"https://ror.org/021nxhr62"}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":31,"referenced_works":["https://openalex.org/W1548595933","https://openalex.org/W1552060966","https://openalex.org/W1590899626","https://openalex.org/W1699734612","https://openalex.org/W1785730614","https://openalex.org/W1969735849","https://openalex.org/W2102324552","https://openalex.org/W2103658758","https://openalex.org/W2105488551","https://openalex.org/W2108538286","https://openalex.org/W2113137767","https://openalex.org/W2116773539","https://openalex.org/W2124351162","https://openalex.org/W2124722975","https://openalex.org/W2126833203","https://openalex.org/W2129004009","https://openalex.org/W2134529554","https://openalex.org/W2151103935","https://openalex.org/W2154422044","https://openalex.org/W2164877691","https://openalex.org/W2166049352","https://openalex.org/W2169551590","https://openalex.org/W2296770417","https://openalex.org/W6604990878","https://openalex.org/W6632941408","https://openalex.org/W6633230002","https://openalex.org/W6633236808","https://openalex.org/W6635212758","https://openalex.org/W6638212498","https://openalex.org/W6677254741","https://openalex.org/W6683978607"],"related_works":["https://openalex.org/W2055243143","https://openalex.org/W2943623134","https://openalex.org/W2494523064","https://openalex.org/W2215759665","https://openalex.org/W2960358116","https://openalex.org/W2030292806","https://openalex.org/W3041172967","https://openalex.org/W2749065928","https://openalex.org/W2147155098","https://openalex.org/W4287727129"],"abstract_inverted_index":{"We":[0,22,82,175,236],"present":[1],"a":[2,26,74,87,122,138,147,167],"new":[3],"unsupervised":[4],"method":[5],"to":[6,17,34,96,99,102,136,159,191,215],"learn":[7,35,121,216],"unified":[8],"probabilistic":[9],"object":[10,197,230,253],"models":[11,240],"(POMs)":[12],"which":[13,92,145,150,165,183],"can":[14,156,212,241],"be":[15,77,157],"applied":[16],"classification,":[18],"segmentation,":[19],"and":[20,30,36,53,65,69,105,133,193,200,202,225,250],"recognition.":[21,254],"formulate":[23],"this":[24,135],"as":[25],"structure":[27,59,89],"learning":[28,71,199],"task":[29],"our":[31],"strategy":[32],"is":[33,63,189,234],"combine":[37],"basic":[38],"POM's":[39,98,104,214],"that":[40,185,210,238],"make":[41],"use":[42,134],"of":[43,60,113,195,228],"complementary":[44],"image":[45,233],"cues.":[46],"Each":[47],"POM":[48,62,75,149,169,188],"has":[49],"algorithms":[50],"for":[51,73,116],"inference":[52,68,204],"parameter":[54,70],"learning,":[55],"but:":[56],"(i)":[57],"the":[58,67,111,186,196,226,229,247],"each":[61,232],"unknown,":[64],"(ii)":[66],"algorithm":[72],"may":[76],"impractical":[78],"without":[79],"additional":[80],"information.":[81],"address":[83],"these":[84,239],"problems":[85],"by":[86],"novel":[88],"induction":[90],"procedure":[91],"uses":[93],"knowledge":[94],"propagation":[95],"enable":[97,252],"provide":[100],"information":[101],"other":[103],"\"teach":[106],"them\"":[107],"(which":[108],"greatly":[109],"reduced":[110],"amount":[112],"supervision":[114,130],"required":[115],"training).":[117],"In":[118,206],"particular,":[119],"we":[120,208,211],"POM-IP":[123],"defined":[124,141,162],"on":[125,142,163,173,180],"interest":[126],"points":[127],"using":[128],"weak":[129],"[1,":[131],"2]":[132],"train":[137,160],"POM-":[139],"mask,":[140],"regional":[143],"features,":[144],"yields":[146],"combined":[148,154],"performs":[151,203],"segmentation/localization.":[152],"This":[153],"model":[155],"used":[158],"POM-edgelets,":[161],"edgelets,":[164],"gives":[166],"full":[168,187],"with":[170],"improved":[171],"performance":[172],"classification.":[174],"give":[176],"detailed":[177],"experimental":[178],"analysis":[179],"large":[181],"datasets":[182],"show":[184,209],"invariant":[190],"scale":[192],"rotation":[194],"(for":[198],"inference)":[201],"rapidly.":[205],"addition,":[207],"apply":[213],"objects":[217,224,245],"classes":[218],"(i.e.":[219],"when":[220],"there":[221],"are":[222],"several":[223],"identity":[227],"in":[231],"unknown).":[235],"emphasize":[237],"match":[242],"between":[243],"different":[244],"from":[246],"same":[248],"category":[249],"hence":[251]},"counts_by_year":[{"year":2015,"cited_by_count":1},{"year":2014,"cited_by_count":1},{"year":2013,"cited_by_count":3},{"year":2012,"cited_by_count":2}],"updated_date":"2026-06-11T09:08:48.828518","created_date":"2025-10-10T00:00:00"}
