{"id":"https://openalex.org/W1516619939","doi":null,"title":"Semi-supervised training of models for appearance-based statistical object detection methods","display_name":"Semi-supervised training of models for appearance-based statistical object detection methods","publication_year":2004,"publication_date":"2004-01-01","ids":{"openalex":"https://openalex.org/W1516619939","mag":"1516619939"},"language":"en","primary_location":{"id":"pmh:oai:CiteSeerX.psu:10.1.1.1.9975","doi":null,"is_oa":false,"landing_page_url":"http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.1.9975","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"http://www.cs.cmu.edu/~chuck/thesis/thesis-rosenberg04.ps.gz","raw_type":"text"},"volume":null,"type":"article","is_traditional":true,"indexed_in":[],"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/A5042034610","display_name":"Charles J. Rosenberg","orcid":"https://orcid.org/0009-0003-9664-8644","observed_orcids":["https://orcid.org/0009-0003-9664-8644"]},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Charles Rosenberg","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5075246991","display_name":"Martial Hebert","orcid":"https://orcid.org/0000-0003-4566-5930","observed_orcids":["https://orcid.org/0000-0003-4566-5930"]},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Martial Hebert","raw_affiliation_strings":[],"raw_orcid":"https://orcid.org/0000-0003-4566-5930","affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5066710774","display_name":"Sebastian Thrun","orcid":null,"observed_orcids":[]},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Sebastian Thrun","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":0,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.9046,"has_fulltext":false,"cited_by_count":4,"citation_normalized_percentile":{"value":0.80123648,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"118","last_page":"118"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10627","display_name":"Advanced Image and Video Retrieval Techniques","score":0.7476000189781189,"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/T10627","display_name":"Advanced Image and Video Retrieval Techniques","score":0.7476000189781189,"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/T10036","display_name":"Advanced Neural Network Applications","score":0.14820000529289246,"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/T10331","display_name":"Video Surveillance and Tracking Methods","score":0.011900000274181366,"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/semi-supervised-learning","display_name":"semi-supervised learning","score":0.9660000205039978},{"id":"https://openalex.org/keywords/weakly-labeled-data","display_name":"weakly labeled data","score":0.9039999842643738},{"id":"https://openalex.org/keywords/self-training","display_name":"self-training","score":0.7789999842643738},{"id":"https://openalex.org/keywords/expectation-maximization-algorithm","display_name":"expectation-maximization algorithm","score":0.9430000185966492},{"id":"https://openalex.org/keywords/landmark-detection","display_name":"landmark detection","score":0.5260000228881836}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7159058451652527},{"id":"https://openalex.org/C132094186","wikidata":"https://www.wikidata.org/wiki/Q641585","display_name":"Clutter","level":3,"score":0.7131993174552917},{"id":"https://openalex.org/C2781238097","wikidata":"https://www.wikidata.org/wiki/Q175026","display_name":"Object (grammar)","level":2,"score":0.6755695939064026},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6675840020179749},{"id":"https://openalex.org/C177264268","wikidata":"https://www.wikidata.org/wiki/Q1514741","display_name":"Set (abstract data type)","level":2,"score":0.5864333510398865},{"id":"https://openalex.org/C2776151529","wikidata":"https://www.wikidata.org/wiki/Q3045304","display_name":"Object detection","level":3,"score":0.579602837562561},{"id":"https://openalex.org/C136197465","wikidata":"https://www.wikidata.org/wiki/Q1729295","display_name":"Variety (cybernetics)","level":2,"score":0.559914767742157},{"id":"https://openalex.org/C51632099","wikidata":"https://www.wikidata.org/wiki/Q3985153","display_name":"Training set","level":2,"score":0.49993252754211426},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.49076923727989197},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.47225064039230347},{"id":"https://openalex.org/C2778755073","wikidata":"https://www.wikidata.org/wiki/Q10858537","display_name":"Scale (ratio)","level":2,"score":0.46421411633491516},{"id":"https://openalex.org/C2776330181","wikidata":"https://www.wikidata.org/wiki/Q18358244","display_name":"Maximization","level":2,"score":0.44553306698799133},{"id":"https://openalex.org/C98045186","wikidata":"https://www.wikidata.org/wiki/Q205663","display_name":"Process (computing)","level":2,"score":0.4413444995880127},{"id":"https://openalex.org/C26517878","wikidata":"https://www.wikidata.org/wiki/Q228039","display_name":"Key (lock)","level":2,"score":0.4368838667869568},{"id":"https://openalex.org/C2777211547","wikidata":"https://www.wikidata.org/wiki/Q17141490","display_name":"Training (meteorology)","level":2,"score":0.43327271938323975},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.4287392497062683},{"id":"https://openalex.org/C116834253","wikidata":"https://www.wikidata.org/wiki/Q2039217","display_name":"Identification (biology)","level":2,"score":0.4205954074859619},{"id":"https://openalex.org/C114289077","wikidata":"https://www.wikidata.org/wiki/Q3284399","display_name":"Statistical model","level":2,"score":0.41829076409339905},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.17293012142181396},{"id":"https://openalex.org/C38652104","wikidata":"https://www.wikidata.org/wiki/Q3510521","display_name":"Computer security","level":1,"score":0.0},{"id":"https://openalex.org/C554190296","wikidata":"https://www.wikidata.org/wiki/Q47528","display_name":"Radar","level":2,"score":0.0},{"id":"https://openalex.org/C62520636","wikidata":"https://www.wikidata.org/wiki/Q944","display_name":"Quantum mechanics","level":1,"score":0.0},{"id":"https://openalex.org/C76155785","wikidata":"https://www.wikidata.org/wiki/Q418","display_name":"Telecommunications","level":1,"score":0.0},{"id":"https://openalex.org/C199360897","wikidata":"https://www.wikidata.org/wiki/Q9143","display_name":"Programming language","level":1,"score":0.0},{"id":"https://openalex.org/C126255220","wikidata":"https://www.wikidata.org/wiki/Q141495","display_name":"Mathematical optimization","level":1,"score":0.0},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0},{"id":"https://openalex.org/C111919701","wikidata":"https://www.wikidata.org/wiki/Q9135","display_name":"Operating system","level":1,"score":0.0},{"id":"https://openalex.org/C153294291","wikidata":"https://www.wikidata.org/wiki/Q25261","display_name":"Meteorology","level":1,"score":0.0},{"id":"https://openalex.org/C59822182","wikidata":"https://www.wikidata.org/wiki/Q441","display_name":"Botany","level":1,"score":0.0},{"id":"https://openalex.org/C86803240","wikidata":"https://www.wikidata.org/wiki/Q420","display_name":"Biology","level":0,"score":0.0}],"mesh":[],"locations_count":3,"locations":[{"id":"pmh:oai:CiteSeerX.psu:10.1.1.1.9975","doi":null,"is_oa":false,"landing_page_url":"http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.1.9975","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"http://www.cs.cmu.edu/~chuck/thesis/thesis-rosenberg04.ps.gz","raw_type":"text"},{"id":"pmh:oai:CiteSeerX.psu:10.1.1.67.7925","doi":null,"is_oa":false,"landing_page_url":"http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.67.7925","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"http://reports-archive.adm.cs.cmu.edu/anon/2004/CMU-CS-04-150.pdf","raw_type":"text"},{"id":"mag:1516619939","doi":null,"is_oa":false,"landing_page_url":"http://dl.acm.org/citation.cfm?id=1037542","pdf_url":null,"source":null,"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":null}],"best_oa_location":null,"sustainable_development_goals":[],"study_designs":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":92,"referenced_works":["https://openalex.org/W8853611","https://openalex.org/W130979836","https://openalex.org/W138923984","https://openalex.org/W1514907448","https://openalex.org/W1527429500","https://openalex.org/W1538131130","https://openalex.org/W1540386283","https://openalex.org/W1547172162","https://openalex.org/W1550821944","https://openalex.org/W1553187123","https://openalex.org/W1554039773","https://openalex.org/W1554663460","https://openalex.org/W1558805185","https://openalex.org/W1574877594","https://openalex.org/W1780731312","https://openalex.org/W1861993554","https://openalex.org/W1866403196","https://openalex.org/W1934863104","https://openalex.org/W1949116567","https://openalex.org/W1958762911","https://openalex.org/W1965292650","https://openalex.org/W1991605728","https://openalex.org/W2036216970","https://openalex.org/W2037603696","https://openalex.org/W2044523229","https://openalex.org/W2048679005","https://openalex.org/W2049633694","https://openalex.org/W2051228319","https://openalex.org/W2056695679","https://openalex.org/W2083515729","https://openalex.org/W2085207288","https://openalex.org/W2097089247","https://openalex.org/W2100921332","https://openalex.org/W2102524069","https://openalex.org/W2103369961","https://openalex.org/W2103504567","https://openalex.org/W2107008379","https://openalex.org/W2108077184","https://openalex.org/W2108745803","https://openalex.org/W2108966602","https://openalex.org/W2110119381","https://openalex.org/W2110948693","https://openalex.org/W2111557120","https://openalex.org/W2112751623","https://openalex.org/W2114446215","https://openalex.org/W2117030594","https://openalex.org/W2117138270","https://openalex.org/W2122111042","https://openalex.org/W2122520221","https://openalex.org/W2122837498","https://openalex.org/W2124386111","https://openalex.org/W2127816222","https://openalex.org/W2128221272","https://openalex.org/W2128554449","https://openalex.org/W2128716185","https://openalex.org/W2129957127","https://openalex.org/W2131780901","https://openalex.org/W2133774033","https://openalex.org/W2134789565","https://openalex.org/W2137054688","https://openalex.org/W2137471889","https://openalex.org/W2139578439","https://openalex.org/W2139823104","https://openalex.org/W2141522145","https://openalex.org/W2149875083","https://openalex.org/W2149950545","https://openalex.org/W2152942156","https://openalex.org/W2153168223","https://openalex.org/W2153904051","https://openalex.org/W2153927146","https://openalex.org/W2154318594","https://openalex.org/W2154422044","https://openalex.org/W2154794763","https://openalex.org/W2155511848","https://openalex.org/W2158485347","https://openalex.org/W2162708558","https://openalex.org/W2163030924","https://openalex.org/W2163474322","https://openalex.org/W2164302083","https://openalex.org/W2166713160","https://openalex.org/W2166761907","https://openalex.org/W2166770390","https://openalex.org/W2167828171","https://openalex.org/W2171140863","https://openalex.org/W2217896605","https://openalex.org/W2612120747","https://openalex.org/W2963056604","https://openalex.org/W3017143921","https://openalex.org/W3097096317","https://openalex.org/W3145546055","https://openalex.org/W3171280080","https://openalex.org/W6959560793"],"related_works":["https://openalex.org/W3040887816","https://openalex.org/W1965292650","https://openalex.org/W7100845235","https://openalex.org/W7096910422","https://openalex.org/W4312380174","https://openalex.org/W2893351918","https://openalex.org/W3180842119","https://openalex.org/W2070350843","https://openalex.org/W2592384071","https://openalex.org/W3021542222"],"abstract_inverted_index":{"Appearance-based":[0],"object":[1,56,70,190,263],"detection":[2,238,264],"systems":[3,32],"using":[4],"statistical":[5],"models":[6,113],"have":[7],"proven":[8],"quite":[9],"successful.":[10],"They":[11],"can":[12],"reliably":[13,230],"detect":[14],"textured,":[15],"rigid":[16],"objects":[17],"in":[18,50,55,114,139,151,168,226,258],"a":[19,38,115,131,142,176,208,216,235],"variety":[20,236],"of":[21,30,41,64,92,105,134,144,203,237,245,262],"poses,":[22],"lighting":[23],"conditions":[24],"and":[25,35,47,74,98,127,192,248],"scales.":[26],"However,":[27],"the":[28,65,69,88,103,163,185,189,201,204,227,243,259],"construction":[29],"these":[31,112],"is":[33,95,149,166,242],"time-consuming":[34],"difficult":[36],"because":[37],"large":[39],"number":[40,133],"training":[42,111,137,186,220,228,261],"examples":[43,108,138],"must":[44],"be":[45,72,181],"collected":[46],"manually":[48],"labeled":[49,107,136,154,178,182,219,224],"order":[51],"to":[52,68,71,77,96,160,254],"capture":[53],"variations":[54],"appearance.":[57],"Typically,":[58],"this":[59,93,119,197],"requires":[60],"indicating":[61],"which":[62,75,101],"regions":[63],"image":[66,179],"correspond":[67],"detected,":[73],"belong":[76],"background":[78],"clutter,":[79],"as":[80,82,183],"well":[81],"marking":[83],"key":[84,249],"landmark":[85],"locations":[86],"on":[87,125],"object.":[89],"The":[90,240],"goal":[91],"work":[94,198],"pursue":[97],"evaluate":[99],"approaches":[100,123,247],"reduce":[102],"amount":[104],"fully":[106,135,218],"needed,":[109],"by":[110],"semi-supervised":[116,260],"manner.":[117],"To":[118],"end,":[120],"we":[121,199],"develop":[122],"based":[124],"ExpectationMaximization":[126],"self-training":[128],"that":[129,152,214,251],"utilize":[130],"small":[132,217],"combination":[140],"with":[141,188,222],"set":[143,221],"\"weakly":[145],"labeled\"":[146],"examples.":[147],"This":[148],"advantageous":[150],"weakly":[153,177,223],"data":[155,225],"are":[156,252],"inherently":[157],"less":[158],"costly":[159],"generate,":[161],"since":[162],"label":[164],"information":[165],"specified":[167],"an":[169],"uncertain":[170],"or":[171],"incomplete":[172],"fashion.":[173],"For":[174],"example,":[175],"might":[180],"containing":[184],"object,":[187],"location":[191],"scale":[193],"left":[194],"unspecified.":[195],"In":[196],"analyze":[200],"performance":[202,233,257],"techniques":[205],"developed":[206],"through":[207],"comprehensive":[209],"empirical":[210],"investigation.":[211],"We":[212],"find":[213],"supplementing":[215],"process":[229],"improves":[231],"detector":[232],"for":[234],"approaches.":[239],"outcome":[241],"identification":[244],"successful":[246],"issues":[250],"central":[253],"achieving":[255],"good":[256],"systems.":[265]},"counts_by_year":[],"updated_date":"2026-10-13T10:08:12.033832","created_date":"2016-06-24T00:00:00"}