{"id":"https://openalex.org/W2017365379","doi":"https://doi.org/10.1109/cvpr.2011.5995378","title":"Locality-sensitive support vector machine by exploring local correlation and global regularization","display_name":"Locality-sensitive support vector machine by exploring local correlation and global regularization","publication_year":2011,"publication_date":"2011-06-01","ids":{"openalex":"https://openalex.org/W2017365379","doi":"https://doi.org/10.1109/cvpr.2011.5995378","mag":"2017365379"},"language":"en","primary_location":{"id":"doi:10.1109/cvpr.2011.5995378","is_oa":false,"landing_page_url":"https://doi.org/10.1109/cvpr.2011.5995378","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"CVPR 2011","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/A5100766907","display_name":"Guo-Jun Qi","orcid":"https://orcid.org/0000-0003-3508-1851"},"institutions":[{"id":"https://openalex.org/I157725225","display_name":"University of Illinois Urbana-Champaign","ror":"https://ror.org/047426m28","country_code":"US","type":"education","lineage":["https://openalex.org/I157725225"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Guo-Jun Qi","raw_affiliation_strings":["Beckman Institute, University of Illinois, Urbana-Champaign, USA","Beckman Institute, University of Illinois at, Urbana-Champaign,"],"affiliations":[{"raw_affiliation_string":"Beckman Institute, University of Illinois, Urbana-Champaign, USA","institution_ids":["https://openalex.org/I157725225"]},{"raw_affiliation_string":"Beckman Institute, University of Illinois at, Urbana-Champaign,","institution_ids":["https://openalex.org/I157725225"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100393506","display_name":"Qi Tian","orcid":"https://orcid.org/0000-0002-7252-5047"},"institutions":[{"id":"https://openalex.org/I45438204","display_name":"The University of Texas at San Antonio","ror":"https://ror.org/01kd65564","country_code":"US","type":"education","lineage":["https://openalex.org/I45438204"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Qi Tian","raw_affiliation_strings":["Department of Computer Science, University of Texas, San Antonio, USA","Department of Computer Science, University of Texas at San Antonio,#TAB#"],"affiliations":[{"raw_affiliation_string":"Department of Computer Science, University of Texas, San Antonio, USA","institution_ids":["https://openalex.org/I45438204"]},{"raw_affiliation_string":"Department of Computer Science, University of Texas at San Antonio,#TAB#","institution_ids":["https://openalex.org/I45438204"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5101457342","display_name":"Thomas S. Huang","orcid":"https://orcid.org/0000-0001-8474-5859"},"institutions":[{"id":"https://openalex.org/I157725225","display_name":"University of Illinois Urbana-Champaign","ror":"https://ror.org/047426m28","country_code":"US","type":"education","lineage":["https://openalex.org/I157725225"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Thomas Huang","raw_affiliation_strings":["Beckman Institute, University of Illinois, Urbana-Champaign, USA","Beckman Institute, University of Illinois at, Urbana-Champaign,"],"affiliations":[{"raw_affiliation_string":"Beckman Institute, University of Illinois, Urbana-Champaign, USA","institution_ids":["https://openalex.org/I157725225"]},{"raw_affiliation_string":"Beckman Institute, University of Illinois at, Urbana-Champaign,","institution_ids":["https://openalex.org/I157725225"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5100766907"],"corresponding_institution_ids":["https://openalex.org/I157725225"],"apc_list":null,"apc_paid":null,"fwci":1.5682,"has_fulltext":false,"cited_by_count":20,"citation_normalized_percentile":{"value":0.85451022,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":98},"biblio":{"volume":null,"issue":null,"first_page":"841","last_page":"848"},"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":0.9998999834060669,"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":0.9998999834060669,"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/T10824","display_name":"Image Retrieval and Classification Techniques","score":0.9886999726295471,"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":0.9592000246047974,"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/overfitting","display_name":"Overfitting","score":0.7802739143371582},{"id":"https://openalex.org/keywords/locality","display_name":"Locality","score":0.7585011720657349},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.6925280690193176},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6911839246749878},{"id":"https://openalex.org/keywords/local-structure","display_name":"Local structure","score":0.6337350606918335},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6250436902046204},{"id":"https://openalex.org/keywords/classifier","display_name":"Classifier (UML)","score":0.6196101903915405},{"id":"https://openalex.org/keywords/support-vector-machine","display_name":"Support vector machine","score":0.5173336863517761},{"id":"https://openalex.org/keywords/feature-vector","display_name":"Feature vector","score":0.5121533870697021},{"id":"https://openalex.org/keywords/contextual-image-classification","display_name":"Contextual image classification","score":0.4774789810180664},{"id":"https://openalex.org/keywords/correlation","display_name":"Correlation","score":0.42772001028060913},{"id":"https://openalex.org/keywords/regularization","display_name":"Regularization (linguistics)","score":0.41294971108436584},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.39179471135139465},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.2816608250141144},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.21479791402816772},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.20704978704452515}],"concepts":[{"id":"https://openalex.org/C22019652","wikidata":"https://www.wikidata.org/wiki/Q331309","display_name":"Overfitting","level":3,"score":0.7802739143371582},{"id":"https://openalex.org/C2779808786","wikidata":"https://www.wikidata.org/wiki/Q6664603","display_name":"Locality","level":2,"score":0.7585011720657349},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.6925280690193176},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6911839246749878},{"id":"https://openalex.org/C2986090443","wikidata":"https://www.wikidata.org/wiki/Q77870413","display_name":"Local structure","level":2,"score":0.6337350606918335},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6250436902046204},{"id":"https://openalex.org/C95623464","wikidata":"https://www.wikidata.org/wiki/Q1096149","display_name":"Classifier (UML)","level":2,"score":0.6196101903915405},{"id":"https://openalex.org/C12267149","wikidata":"https://www.wikidata.org/wiki/Q282453","display_name":"Support vector machine","level":2,"score":0.5173336863517761},{"id":"https://openalex.org/C83665646","wikidata":"https://www.wikidata.org/wiki/Q42139305","display_name":"Feature vector","level":2,"score":0.5121533870697021},{"id":"https://openalex.org/C75294576","wikidata":"https://www.wikidata.org/wiki/Q5165192","display_name":"Contextual image classification","level":3,"score":0.4774789810180664},{"id":"https://openalex.org/C117220453","wikidata":"https://www.wikidata.org/wiki/Q5172842","display_name":"Correlation","level":2,"score":0.42772001028060913},{"id":"https://openalex.org/C2776135515","wikidata":"https://www.wikidata.org/wiki/Q17143721","display_name":"Regularization (linguistics)","level":2,"score":0.41294971108436584},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.39179471135139465},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.2816608250141144},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.21479791402816772},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.20704978704452515},{"id":"https://openalex.org/C41895202","wikidata":"https://www.wikidata.org/wiki/Q8162","display_name":"Linguistics","level":1,"score":0.0},{"id":"https://openalex.org/C159467904","wikidata":"https://www.wikidata.org/wiki/Q2001702","display_name":"Chemical physics","level":1,"score":0.0},{"id":"https://openalex.org/C2524010","wikidata":"https://www.wikidata.org/wiki/Q8087","display_name":"Geometry","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/C138885662","wikidata":"https://www.wikidata.org/wiki/Q5891","display_name":"Philosophy","level":0,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1109/cvpr.2011.5995378","is_oa":false,"landing_page_url":"https://doi.org/10.1109/cvpr.2011.5995378","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"CVPR 2011","raw_type":"proceedings-article"},{"id":"pmh:oai:CiteSeerX.psu:10.1.1.701.6959","is_oa":false,"landing_page_url":"http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.701.6959","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"http://www.ifp.illinois.edu/%7Eqi4/papers/2011_CVPR_LSSVM-gjqi.pdf","raw_type":"text"}],"best_oa_location":null,"sustainable_development_goals":[{"display_name":"Reduced inequalities","id":"https://metadata.un.org/sdg/10","score":0.75}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":19,"referenced_works":["https://openalex.org/W1593641510","https://openalex.org/W2007972815","https://openalex.org/W2038276547","https://openalex.org/W2056763477","https://openalex.org/W2066440363","https://openalex.org/W2117959009","https://openalex.org/W2143104527","https://openalex.org/W2148603752","https://openalex.org/W2154952031","https://openalex.org/W2157825442","https://openalex.org/W2162006472","https://openalex.org/W2165828254","https://openalex.org/W2171448996","https://openalex.org/W2296319761","https://openalex.org/W4247915583","https://openalex.org/W4250589301","https://openalex.org/W6677640231","https://openalex.org/W6682717316","https://openalex.org/W6684893555"],"related_works":["https://openalex.org/W4362597605","https://openalex.org/W1574414179","https://openalex.org/W4297676672","https://openalex.org/W3009056573","https://openalex.org/W2922073769","https://openalex.org/W4281702477","https://openalex.org/W2490526372","https://openalex.org/W4376166922","https://openalex.org/W3128011703","https://openalex.org/W4390143830"],"abstract_inverted_index":{"Local":[0],"classifiers":[1,50,144],"have":[2,23],"obtained":[3,199],"great":[4],"success":[5],"in":[6,26,36,44,61,80],"classification":[7],"task":[8,194],"due":[9,116],"to":[10,34,41,93,117,149,159],"its":[11],"powerful":[12],"discriminating":[13,177],"ability":[14,178],"on":[15,105,121,200],"local":[16,30,37,45,49,54,63,103,110,126,139,143,155,171],"regions.":[17],"However,":[18],"most":[19],"of":[20,102,107,169,179],"them":[21,108],"still":[22],"restricted":[24],"generalization":[25],"twofold:":[27],"(1)":[28],"each":[29,62,106,170],"classifier":[31,88],"is":[32,157],"sensitive":[33],"noise":[35],"regions":[38,140],"which":[39,173],"leads":[40],"overfitting":[42,129],"phenomenon":[43],"classifiers;":[46],"(2)":[47],"the":[48,53,58,67,95,161,166,176,180,184,188,201],"also":[51],"ignore":[52],"correlation":[55,156],"determined":[56],"by":[57],"sample":[59,162],"distribution":[60,163],"region.":[64],"To":[65,123,182],"overcome":[66],"above":[68],"two":[69],"problems,":[70],"we":[71,186],"present":[72],"a":[73,100,109,135,151],"novel":[74],"locality-sensitive":[75,90,131],"support":[76],"vector":[77],"machine":[78],"(LSSVM)":[79],"this":[81],"paper":[82],"for":[83],"image":[84,192,204],"retrieval":[85,193],"problem.":[86],"This":[87],"applies":[89],"hashing":[91],"(LSH)":[92],"divide":[94],"whole":[96],"feature":[97],"space":[98],"into":[99,130,191],"number":[101],"regions,":[104],"model":[111],"can":[112,174],"be":[113],"better":[114],"constructed":[115],"smaller":[118],"within-class":[119],"variation":[120],"it.":[122],"avoid":[124],"these":[125],"models":[127],"from":[128],"structures,":[132],"it":[133],"imposes":[134],"global":[136],"regularizer":[137],"across":[138],"so":[141],"that":[142,164],"are":[145,198],"smoothly":[146],"glued":[147],"together":[148],"form":[150],"regularized":[152],"overall":[153],"classifier.":[154],"modeled":[158],"capture":[160],"determines":[165],"locality":[167],"structure":[168],"region,":[172],"increase":[175],"algorithm.":[181],"evaluate":[183],"performance,":[185],"apply":[187],"proposed":[189],"algorithm":[190],"and":[195],"competitive":[196],"results":[197],"real-world":[202],"web":[203],"data":[205],"set.":[206]},"counts_by_year":[{"year":2021,"cited_by_count":1},{"year":2020,"cited_by_count":2},{"year":2018,"cited_by_count":2},{"year":2017,"cited_by_count":3},{"year":2016,"cited_by_count":2},{"year":2015,"cited_by_count":4},{"year":2014,"cited_by_count":2},{"year":2013,"cited_by_count":3},{"year":2012,"cited_by_count":1}],"updated_date":"2026-04-04T16:13:02.066488","created_date":"2025-10-10T00:00:00"}
