{"id":"https://openalex.org/W2127997884","doi":"https://doi.org/10.1109/iccv.2009.5459182","title":"Efficient discriminative local learning for object recognition","display_name":"Efficient discriminative local learning for object recognition","publication_year":2009,"publication_date":"2009-09-01","ids":{"openalex":"https://openalex.org/W2127997884","doi":"https://doi.org/10.1109/iccv.2009.5459182","mag":"2127997884"},"language":"en","primary_location":{"id":"doi:10.1109/iccv.2009.5459182","is_oa":false,"landing_page_url":"https://doi.org/10.1109/iccv.2009.5459182","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2009 IEEE 12th International Conference on Computer Vision","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/A5002217153","display_name":"Yen\u2010Yu Lin","orcid":"https://orcid.org/0000-0002-7183-6070"},"institutions":[{"id":"https://openalex.org/I16733864","display_name":"National Taiwan University","ror":"https://ror.org/05bqach95","country_code":"TW","type":"education","lineage":["https://openalex.org/I16733864"]},{"id":"https://openalex.org/I4210098366","display_name":"Institute of Information Science, Academia Sinica","ror":"https://ror.org/00z83z196","country_code":"TW","type":"facility","lineage":["https://openalex.org/I4210098366","https://openalex.org/I84653119"]}],"countries":["TW"],"is_corresponding":true,"raw_author_name":"Yen-Yu Lin","raw_affiliation_strings":["Department of CSIE, National Taiwan University, Taipei, Taiwan","Institute of Information Science, Academia Sinica, Taipei, Taiwan"],"affiliations":[{"raw_affiliation_string":"Department of CSIE, National Taiwan University, Taipei, Taiwan","institution_ids":["https://openalex.org/I16733864"]},{"raw_affiliation_string":"Institute of Information Science, Academia Sinica, Taipei, Taiwan","institution_ids":["https://openalex.org/I4210098366"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5045765783","display_name":"Jyun-Fan Tsai","orcid":"https://orcid.org/0009-0007-0444-0221"},"institutions":[{"id":"https://openalex.org/I4210098366","display_name":"Institute of Information Science, Academia Sinica","ror":"https://ror.org/00z83z196","country_code":"TW","type":"facility","lineage":["https://openalex.org/I4210098366","https://openalex.org/I84653119"]}],"countries":["TW"],"is_corresponding":false,"raw_author_name":"Jyun-Fan Tsai","raw_affiliation_strings":["Institute of Information Science, Academia Sinica, Taipei, Taiwan"],"affiliations":[{"raw_affiliation_string":"Institute of Information Science, Academia Sinica, Taipei, Taiwan","institution_ids":["https://openalex.org/I4210098366"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5043386280","display_name":"Tyng-Luh Liu","orcid":"https://orcid.org/0000-0002-8366-5213"},"institutions":[{"id":"https://openalex.org/I4210098366","display_name":"Institute of Information Science, Academia Sinica","ror":"https://ror.org/00z83z196","country_code":"TW","type":"facility","lineage":["https://openalex.org/I4210098366","https://openalex.org/I84653119"]}],"countries":["TW"],"is_corresponding":false,"raw_author_name":"Tyng-Luh Liu","raw_affiliation_strings":["Institute of Information Science, Academia Sinica, Taipei, Taiwan"],"affiliations":[{"raw_affiliation_string":"Institute of Information Science, Academia Sinica, Taipei, Taiwan","institution_ids":["https://openalex.org/I4210098366"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5002217153"],"corresponding_institution_ids":["https://openalex.org/I16733864","https://openalex.org/I4210098366"],"apc_list":null,"apc_paid":null,"fwci":3.8828,"has_fulltext":false,"cited_by_count":24,"citation_normalized_percentile":{"value":0.94201874,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":89,"max":99},"biblio":{"volume":null,"issue":null,"first_page":"598","last_page":"605"},"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/T11307","display_name":"Domain Adaptation and Few-Shot Learning","score":0.9997000098228455,"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/T10036","display_name":"Advanced Neural Network Applications","score":0.9987999796867371,"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/boosting","display_name":"Boosting (machine learning)","score":0.8736150860786438},{"id":"https://openalex.org/keywords/overfitting","display_name":"Overfitting","score":0.7170760631561279},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.698094367980957},{"id":"https://openalex.org/keywords/discriminative-model","display_name":"Discriminative model","score":0.6845128536224365},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.6757819652557373},{"id":"https://openalex.org/keywords/embedding","display_name":"Embedding","score":0.6678080558776855},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6630771160125732},{"id":"https://openalex.org/keywords/nonlinear-dimensionality-reduction","display_name":"Nonlinear dimensionality reduction","score":0.4614831805229187},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.4300311803817749},{"id":"https://openalex.org/keywords/parametric-statistics","display_name":"Parametric statistics","score":0.4193606674671173},{"id":"https://openalex.org/keywords/benchmark","display_name":"Benchmark (surveying)","score":0.41301029920578003},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.2234935760498047},{"id":"https://openalex.org/keywords/dimensionality-reduction","display_name":"Dimensionality reduction","score":0.19686463475227356},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.1500016748905182}],"concepts":[{"id":"https://openalex.org/C46686674","wikidata":"https://www.wikidata.org/wiki/Q466303","display_name":"Boosting (machine learning)","level":2,"score":0.8736150860786438},{"id":"https://openalex.org/C22019652","wikidata":"https://www.wikidata.org/wiki/Q331309","display_name":"Overfitting","level":3,"score":0.7170760631561279},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.698094367980957},{"id":"https://openalex.org/C97931131","wikidata":"https://www.wikidata.org/wiki/Q5282087","display_name":"Discriminative model","level":2,"score":0.6845128536224365},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.6757819652557373},{"id":"https://openalex.org/C41608201","wikidata":"https://www.wikidata.org/wiki/Q980509","display_name":"Embedding","level":2,"score":0.6678080558776855},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6630771160125732},{"id":"https://openalex.org/C151876577","wikidata":"https://www.wikidata.org/wiki/Q7049464","display_name":"Nonlinear dimensionality reduction","level":3,"score":0.4614831805229187},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.4300311803817749},{"id":"https://openalex.org/C117251300","wikidata":"https://www.wikidata.org/wiki/Q1849855","display_name":"Parametric statistics","level":2,"score":0.4193606674671173},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.41301029920578003},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.2234935760498047},{"id":"https://openalex.org/C70518039","wikidata":"https://www.wikidata.org/wiki/Q16000077","display_name":"Dimensionality reduction","level":2,"score":0.19686463475227356},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.1500016748905182},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.0},{"id":"https://openalex.org/C13280743","wikidata":"https://www.wikidata.org/wiki/Q131089","display_name":"Geodesy","level":1,"score":0.0},{"id":"https://openalex.org/C205649164","wikidata":"https://www.wikidata.org/wiki/Q1071","display_name":"Geography","level":0,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/iccv.2009.5459182","is_oa":false,"landing_page_url":"https://doi.org/10.1109/iccv.2009.5459182","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2009 IEEE 12th International Conference on Computer Vision","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/10","display_name":"Reduced inequalities","score":0.7599999904632568}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":44,"referenced_works":["https://openalex.org/W1566135517","https://openalex.org/W1580520684","https://openalex.org/W1624854622","https://openalex.org/W1689445748","https://openalex.org/W1988790447","https://openalex.org/W1999693420","https://openalex.org/W2031839470","https://openalex.org/W2066690519","https://openalex.org/W2101098151","https://openalex.org/W2104978738","https://openalex.org/W2105464770","https://openalex.org/W2110036324","https://openalex.org/W2122808326","https://openalex.org/W2129156852","https://openalex.org/W2130903752","https://openalex.org/W2131743987","https://openalex.org/W2144502914","https://openalex.org/W2145295623","https://openalex.org/W2150470820","https://openalex.org/W2150772522","https://openalex.org/W2151103935","https://openalex.org/W2154054404","https://openalex.org/W2154683974","https://openalex.org/W2155490028","https://openalex.org/W2155511848","https://openalex.org/W2155904486","https://openalex.org/W2159680539","https://openalex.org/W2162915993","https://openalex.org/W2163999590","https://openalex.org/W2165828254","https://openalex.org/W2166049352","https://openalex.org/W2166742463","https://openalex.org/W2168002178","https://openalex.org/W2186094539","https://openalex.org/W2912522929","https://openalex.org/W2914746235","https://openalex.org/W4210969899","https://openalex.org/W4248437541","https://openalex.org/W6641446668","https://openalex.org/W6674896937","https://openalex.org/W6676298309","https://openalex.org/W6679734692","https://openalex.org/W6682131137","https://openalex.org/W6759193872"],"related_works":["https://openalex.org/W4362597605","https://openalex.org/W1574414179","https://openalex.org/W4297676672","https://openalex.org/W3009056573","https://openalex.org/W2393964553","https://openalex.org/W3040691452","https://openalex.org/W2116862786","https://openalex.org/W2970256865","https://openalex.org/W2047382418","https://openalex.org/W2954481250"],"abstract_inverted_index":{"Although":[0],"object":[1],"recognition":[2,157],"methods":[3],"based":[4],"on":[5,141],"local":[6,37,61,86,100,118,168],"learning":[7,67,115],"can":[8,120],"reasonably":[9],"resolve":[10],"the":[11,15,21,24,30,55,105,110,114,125,132],"difficulties":[12],"caused":[13],"by":[14,103,124],"large":[16],"variations":[17],"in":[18,34,166],"images":[19],"from":[20,128],"same":[22],"category,":[23],"high":[25],"risk":[26],"of":[27,60,116],"overfitting":[28],"and":[29,69,89,95,146],"heavy":[31],"computational":[32],"cost":[33],"training":[35,58,102,133],"numerous":[36],"models":[38,62,87],"(classifiers":[39],"or":[40],"distance":[41],"functions)":[42],"often":[43],"limit":[44],"their":[45],"applicability.":[46],"To":[47],"address":[48],"these":[49,85],"two":[50,142,163],"unpleasant":[51],"issues,":[52],"we":[53,79],"cast":[54],"multiple,":[56],"independent":[57],"processes":[59],"as":[63,91],"a":[64,71,81,92,162],"correlative":[65],"multi-task":[66],"problem,":[68],"design":[70],"new":[72],"boosting":[73,97],"algorithm":[74],"to":[75,98],"accomplish":[76],"it.":[77],"Specifically,":[78],"establish":[80],"parametric":[82],"space":[83],"where":[84],"lie":[88],"spread":[90],"manifold-like":[93],"structure,":[94],"use":[96],"perform":[99],"model":[101,119],"completing":[104],"manifold":[106],"embedding.":[107],"Via":[108],"sharing":[109],"common":[111],"embedding":[112],"space,":[113],"each":[117],"be":[121],"properly":[122],"regularized":[123],"extra":[126],"knowledge":[127],"other":[129],"models,":[130],"while":[131],"time":[134],"is":[135],"also":[136,160],"significantly":[137],"reduced.":[138],"Experimental":[139],"results":[140],"benchmark":[143],"datasets,":[144],"Caltech-101":[145],"VOC":[147],"2007,":[148],"support":[149],"that":[150],"our":[151],"approach":[152],"not":[153],"only":[154],"achieves":[155],"promising":[156],"rates":[158],"but":[159],"gives":[161],"order":[164],"speed-up":[165],"realizing":[167],"learning.":[169]},"counts_by_year":[{"year":2022,"cited_by_count":1},{"year":2018,"cited_by_count":1},{"year":2017,"cited_by_count":2},{"year":2016,"cited_by_count":2},{"year":2014,"cited_by_count":2},{"year":2013,"cited_by_count":4},{"year":2012,"cited_by_count":6}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
