{"id":"https://openalex.org/W2148696156","doi":"https://doi.org/10.1109/cvpr.2008.4587801","title":"Taylor expansion based classifier adaptation: Application to person detection","display_name":"Taylor expansion based classifier adaptation: Application to person detection","publication_year":2008,"publication_date":"2008-06-01","ids":{"openalex":"https://openalex.org/W2148696156","doi":"https://doi.org/10.1109/cvpr.2008.4587801","mag":"2148696156"},"language":"en","primary_location":{"id":"doi:10.1109/cvpr.2008.4587801","is_oa":false,"landing_page_url":"https://doi.org/10.1109/cvpr.2008.4587801","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":false,"oa_status":"closed","oa_url":null,"any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5103551927","display_name":"Cha 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":"Cha Zhang","raw_affiliation_strings":["Microsoft Research, Redmond, WA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Microsoft Research, Redmond, WA","institution_ids":["https://openalex.org/I1290206253"]}]},{"author_position":"middle","author":{"id":null,"display_name":"Raffay Hamid","orcid":null},"institutions":[{"id":"https://openalex.org/I130701444","display_name":"Georgia Institute of Technology","ror":"https://ror.org/01zkghx44","country_code":"US","type":"education","lineage":["https://openalex.org/I130701444"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Raffay Hamid","raw_affiliation_strings":["Georgia Institute of Technology, Atlanta, GA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Georgia Institute of Technology, Atlanta, GA","institution_ids":["https://openalex.org/I130701444"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5113678278","display_name":"Zhengyou Zhang","orcid":"https://orcid.org/0009-0004-6952-6905"},"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":"Zhengyou Zhang","raw_affiliation_strings":["Microsoft Research, Redmond, WA, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Microsoft Research, Redmond, WA, USA","institution_ids":["https://openalex.org/I1290206253"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":26,"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/T10331","display_name":"Video Surveillance and Tracking Methods","score":0.9997000098228455,"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/T10331","display_name":"Video Surveillance and Tracking Methods","score":0.9997000098228455,"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/T11512","display_name":"Anomaly Detection Techniques and Applications","score":0.9994999766349792,"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"}},{"id":"https://openalex.org/T10057","display_name":"Face and Expression Recognition","score":0.9970999956130981,"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/classifier","display_name":"Classifier (UML)","score":0.811631441116333},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7484747171401978},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.685693621635437},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.6190264821052551},{"id":"https://openalex.org/keywords/margin-classifier","display_name":"Margin classifier","score":0.5054638385772705},{"id":"https://openalex.org/keywords/quadratic-classifier","display_name":"Quadratic classifier","score":0.4833037853240967},{"id":"https://openalex.org/keywords/boosting","display_name":"Boosting (machine learning)","score":0.4490714967250824},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.41540154814720154},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.37524402141571045}],"concepts":[{"id":"https://openalex.org/C95623464","wikidata":"https://www.wikidata.org/wiki/Q1096149","display_name":"Classifier (UML)","level":2,"score":0.811631441116333},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7484747171401978},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.685693621635437},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.6190264821052551},{"id":"https://openalex.org/C173102733","wikidata":"https://www.wikidata.org/wiki/Q6760396","display_name":"Margin classifier","level":3,"score":0.5054638385772705},{"id":"https://openalex.org/C52620605","wikidata":"https://www.wikidata.org/wiki/Q7268357","display_name":"Quadratic classifier","level":3,"score":0.4833037853240967},{"id":"https://openalex.org/C46686674","wikidata":"https://www.wikidata.org/wiki/Q466303","display_name":"Boosting (machine learning)","level":2,"score":0.4490714967250824},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.41540154814720154},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.37524402141571045}],"mesh":[],"locations_count":3,"locations":[{"id":"doi:10.1109/cvpr.2008.4587801","is_oa":false,"landing_page_url":"https://doi.org/10.1109/cvpr.2008.4587801","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:10.1.1.187.6271","is_oa":false,"landing_page_url":"http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.187.6271","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"http://research.microsoft.com/en-us/people/chazhang/publications/cvpr08_ChaZhang.pdf","raw_type":"text"},{"id":"pmh:oai:CiteSeerX.psu:10.1.1.330.6837","is_oa":false,"landing_page_url":"http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.330.6837","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/461.pdf","raw_type":"text"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":30,"referenced_works":["https://openalex.org/W291003596","https://openalex.org/W1546961578","https://openalex.org/W1553458173","https://openalex.org/W1601795611","https://openalex.org/W1663973292","https://openalex.org/W1988790447","https://openalex.org/W1995055819","https://openalex.org/W2024046085","https://openalex.org/W2053181242","https://openalex.org/W2060451775","https://openalex.org/W2116683971","https://openalex.org/W2121680631","https://openalex.org/W2129035221","https://openalex.org/W2132103241","https://openalex.org/W2136504847","https://openalex.org/W2146159678","https://openalex.org/W2146820038","https://openalex.org/W2150000644","https://openalex.org/W2153927146","https://openalex.org/W2155714768","https://openalex.org/W2164202775","https://openalex.org/W2164598857","https://openalex.org/W2166010828","https://openalex.org/W2170865122","https://openalex.org/W2295262250","https://openalex.org/W3173138228","https://openalex.org/W4285719527","https://openalex.org/W6629510986","https://openalex.org/W6633113729","https://openalex.org/W6680140577"],"related_works":["https://openalex.org/W1483596504","https://openalex.org/W2010370304","https://openalex.org/W2162083125","https://openalex.org/W2009506202","https://openalex.org/W47559851","https://openalex.org/W2297694731","https://openalex.org/W204488290","https://openalex.org/W2071988253","https://openalex.org/W2096969571","https://openalex.org/W2040550925"],"abstract_inverted_index":{"Because":[0],"of":[1,94,123,144,158],"the":[2,41,59,63,67,71,75,92,156],"large":[3],"variation":[4],"across":[5],"different":[6],"environments,":[7],"a":[8,19,28,49,111],"generic":[9,38],"classifier":[10,32,39,46,55],"trained":[11],"on":[12,62,70,132],"extensive":[13],"data-sets":[14,65],"may":[15],"perform":[16,54],"sub-optimally":[17],"in":[18,40,161],"particular":[20],"test":[21,43],"environment.":[22,44,77],"In":[23],"this":[24,130],"paper,":[25],"we":[26,53],"present":[27],"general":[29],"framework":[30],"for":[31,101,110,155],"adaptation,":[33],"which":[34],"improves":[35],"an":[36],"existing":[37],"new":[42,76],"Viewing":[45],"learning":[47],"as":[48],"cost":[50,60,68],"minimization":[51],"problem,":[52],"adaptation":[56],"by":[57],"combining":[58],"function":[61,69],"old":[64],"with":[66,84,172],"data-set":[72],"collected":[73],"from":[74],"The":[78],"former":[79],"term":[80],"is":[81,118],"further":[82],"approximated":[83],"its":[85],"second":[86],"order":[87],"Taylor":[88],"expansion":[89],"to":[90,98,120],"reduce":[91],"amount":[93],"information":[95],"that":[96,106,165],"needs":[97],"be":[99,170],"saved":[100],"adaptation.":[102],"Unlike":[103],"traditional":[104],"approaches":[105],"are":[107],"often":[108],"designed":[109],"specific":[112],"application":[113],"or":[114],"classifier,":[115],"our":[116,173],"scheme":[117],"applicable":[119],"various":[121],"types":[122,143],"classifiers":[124,135],"and":[125,138,149],"user":[126,145],"labels.":[127],"We":[128],"demonstrate":[129],"property":[131],"two":[133,142],"popular":[134],"(logistic":[136],"regression":[137],"boosting),":[139],"while":[140],"using":[141],"labels":[146,148],"(direct":[147],"similarity":[150],"labels).":[151],"Extensive":[152],"experiments":[153],"conducted":[154],"task":[157],"person":[159],"detection":[160],"conference-room":[162],"environments":[163],"show":[164],"significant":[166],"performance":[167],"improvement":[168],"can":[169],"achieved":[171],"proposed":[174],"method.":[175]},"counts_by_year":[{"year":2016,"cited_by_count":2},{"year":2015,"cited_by_count":4},{"year":2014,"cited_by_count":3},{"year":2013,"cited_by_count":3},{"year":2012,"cited_by_count":5}],"updated_date":"2026-07-14T23:27:15.235271","created_date":"2025-10-10T00:00:00"}
