{"id":"https://openalex.org/W2026355154","doi":"https://doi.org/10.1145/1873951.1874181","title":"Fast feature selection and training for AdaBoost-based concept detection with large scale datasets","display_name":"Fast feature selection and training for AdaBoost-based concept detection with large scale datasets","publication_year":2010,"publication_date":"2010-10-25","ids":{"openalex":"https://openalex.org/W2026355154","doi":"https://doi.org/10.1145/1873951.1874181","mag":"2026355154"},"language":"en","primary_location":{"id":"doi:10.1145/1873951.1874181","is_oa":false,"landing_page_url":"https://doi.org/10.1145/1873951.1874181","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 18th ACM international conference on Multimedia","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/A5100362202","display_name":"Shi Chen","orcid":"https://orcid.org/0000-0002-3749-4767"},"institutions":[{"id":"https://openalex.org/I4210094879","display_name":"Shandong Institute of Automation","ror":"https://ror.org/00qdtba35","country_code":"CN","type":"facility","lineage":["https://openalex.org/I4210094879","https://openalex.org/I4210142748"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Shi Chen","raw_affiliation_strings":["National Lab of Pattern Recognition, Institute of Automation, CAS, Beijing, China"],"affiliations":[{"raw_affiliation_string":"National Lab of Pattern Recognition, Institute of Automation, CAS, Beijing, China","institution_ids":["https://openalex.org/I4210094879"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5058420913","display_name":"Jinqiao Wang","orcid":"https://orcid.org/0000-0002-9118-2780"},"institutions":[{"id":"https://openalex.org/I4210094879","display_name":"Shandong Institute of Automation","ror":"https://ror.org/00qdtba35","country_code":"CN","type":"facility","lineage":["https://openalex.org/I4210094879","https://openalex.org/I4210142748"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Jinqiao Wang","raw_affiliation_strings":["National Lab of Pattern Recognition, Institute of Automation, CAS, Beijing, China"],"affiliations":[{"raw_affiliation_string":"National Lab of Pattern Recognition, Institute of Automation, CAS, Beijing, China","institution_ids":["https://openalex.org/I4210094879"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5009384556","display_name":"Liu Yang","orcid":"https://orcid.org/0000-0002-2179-7026"},"institutions":[{"id":"https://openalex.org/I4210094879","display_name":"Shandong Institute of Automation","ror":"https://ror.org/00qdtba35","country_code":"CN","type":"facility","lineage":["https://openalex.org/I4210094879","https://openalex.org/I4210142748"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yang Liu","raw_affiliation_strings":["National Lab of Pattern Recognition, Institute of Automation, CAS, Beijing, China"],"affiliations":[{"raw_affiliation_string":"National Lab of Pattern Recognition, Institute of Automation, CAS, Beijing, China","institution_ids":["https://openalex.org/I4210094879"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5022636178","display_name":"Changsheng Xu","orcid":"https://orcid.org/0000-0001-8343-9665"},"institutions":[{"id":"https://openalex.org/I4210094879","display_name":"Shandong Institute of Automation","ror":"https://ror.org/00qdtba35","country_code":"CN","type":"facility","lineage":["https://openalex.org/I4210094879","https://openalex.org/I4210142748"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Changsheng Xu","raw_affiliation_strings":["National Lab of Pattern Recognition, Institute of Automation, CAS, Beijing, China"],"affiliations":[{"raw_affiliation_string":"National Lab of Pattern Recognition, Institute of Automation, CAS, Beijing, China","institution_ids":["https://openalex.org/I4210094879"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5100511737","display_name":"Hanqing Lu","orcid":"https://orcid.org/0000-0001-9506-3407"},"institutions":[{"id":"https://openalex.org/I4210094879","display_name":"Shandong Institute of Automation","ror":"https://ror.org/00qdtba35","country_code":"CN","type":"facility","lineage":["https://openalex.org/I4210094879","https://openalex.org/I4210142748"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Hanqing Lu","raw_affiliation_strings":["National Lab of Pattern Recognition, Institute of Automation, CAS, Beijing, China"],"affiliations":[{"raw_affiliation_string":"National Lab of Pattern Recognition, Institute of Automation, CAS, Beijing, China","institution_ids":["https://openalex.org/I4210094879"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":5,"corresponding_author_ids":["https://openalex.org/A5100362202"],"corresponding_institution_ids":["https://openalex.org/I4210094879"],"apc_list":null,"apc_paid":null,"fwci":0.6374,"has_fulltext":false,"cited_by_count":7,"citation_normalized_percentile":{"value":0.69501387,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":96},"biblio":{"volume":null,"issue":null,"first_page":"1179","last_page":"1182"},"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":0.9871000051498413,"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":0.9764000177383423,"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/computer-science","display_name":"Computer science","score":0.7375971078872681},{"id":"https://openalex.org/keywords/adaboost","display_name":"AdaBoost","score":0.6359096169471741},{"id":"https://openalex.org/keywords/locality-sensitive-hashing","display_name":"Locality-sensitive hashing","score":0.611426591873169},{"id":"https://openalex.org/keywords/bottleneck","display_name":"Bottleneck","score":0.610663652420044},{"id":"https://openalex.org/keywords/boosting","display_name":"Boosting (machine learning)","score":0.5867078304290771},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5550433397293091},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.5483303070068359},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.4820595681667328},{"id":"https://openalex.org/keywords/random-projection","display_name":"Random projection","score":0.47474604845046997},{"id":"https://openalex.org/keywords/classifier","display_name":"Classifier (UML)","score":0.47001293301582336},{"id":"https://openalex.org/keywords/feature-selection","display_name":"Feature selection","score":0.4285171627998352},{"id":"https://openalex.org/keywords/k-nearest-neighbors-algorithm","display_name":"k-nearest neighbors algorithm","score":0.41885197162628174},{"id":"https://openalex.org/keywords/hash-function","display_name":"Hash function","score":0.41660749912261963},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.3635272979736328},{"id":"https://openalex.org/keywords/hash-table","display_name":"Hash table","score":0.19633835554122925}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7375971078872681},{"id":"https://openalex.org/C141404830","wikidata":"https://www.wikidata.org/wiki/Q2823869","display_name":"AdaBoost","level":3,"score":0.6359096169471741},{"id":"https://openalex.org/C74270461","wikidata":"https://www.wikidata.org/wiki/Q1625299","display_name":"Locality-sensitive hashing","level":4,"score":0.611426591873169},{"id":"https://openalex.org/C2780513914","wikidata":"https://www.wikidata.org/wiki/Q18210350","display_name":"Bottleneck","level":2,"score":0.610663652420044},{"id":"https://openalex.org/C46686674","wikidata":"https://www.wikidata.org/wiki/Q466303","display_name":"Boosting (machine learning)","level":2,"score":0.5867078304290771},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5550433397293091},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.5483303070068359},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.4820595681667328},{"id":"https://openalex.org/C2777036070","wikidata":"https://www.wikidata.org/wiki/Q18393452","display_name":"Random projection","level":2,"score":0.47474604845046997},{"id":"https://openalex.org/C95623464","wikidata":"https://www.wikidata.org/wiki/Q1096149","display_name":"Classifier (UML)","level":2,"score":0.47001293301582336},{"id":"https://openalex.org/C148483581","wikidata":"https://www.wikidata.org/wiki/Q446488","display_name":"Feature selection","level":2,"score":0.4285171627998352},{"id":"https://openalex.org/C113238511","wikidata":"https://www.wikidata.org/wiki/Q1071612","display_name":"k-nearest neighbors algorithm","level":2,"score":0.41885197162628174},{"id":"https://openalex.org/C99138194","wikidata":"https://www.wikidata.org/wiki/Q183427","display_name":"Hash function","level":2,"score":0.41660749912261963},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.3635272979736328},{"id":"https://openalex.org/C67388219","wikidata":"https://www.wikidata.org/wiki/Q207440","display_name":"Hash table","level":3,"score":0.19633835554122925},{"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/C149635348","wikidata":"https://www.wikidata.org/wiki/Q193040","display_name":"Embedded system","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/1873951.1874181","is_oa":false,"landing_page_url":"https://doi.org/10.1145/1873951.1874181","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 18th ACM international conference on Multimedia","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"score":0.5299999713897705,"id":"https://metadata.un.org/sdg/10","display_name":"Reduced inequalities"}],"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":16,"referenced_works":["https://openalex.org/W2023240376","https://openalex.org/W2032210760","https://openalex.org/W2103556603","https://openalex.org/W2104577112","https://openalex.org/W2112020727","https://openalex.org/W2118373237","https://openalex.org/W2144921994","https://openalex.org/W2147717514","https://openalex.org/W2148809503","https://openalex.org/W2161449253","https://openalex.org/W2293597654","https://openalex.org/W2603497684","https://openalex.org/W2916826692","https://openalex.org/W4285719527","https://openalex.org/W6675321306","https://openalex.org/W7066667914"],"related_works":["https://openalex.org/W2327035729","https://openalex.org/W2348748958","https://openalex.org/W3039673966","https://openalex.org/W1538046993","https://openalex.org/W2884325279","https://openalex.org/W1570592793","https://openalex.org/W1525436954","https://openalex.org/W2385662756","https://openalex.org/W2585372724","https://openalex.org/W2241444561"],"abstract_inverted_index":{"AdaBoost":[0],"has":[1],"been":[2],"proved":[3],"a":[4,26,54,77],"successful":[5],"statistical":[6],"learning":[7],"method":[8,153,197,210],"for":[9,52],"concept":[10,27],"detection":[11],"with":[12,61,123,185,213],"high":[13],"performance":[14,207],"of":[15,38,87,93,111,118,142,166,208],"discrimination":[16],"and":[17,64,145,162,182],"generalization.":[18],"However,":[19],"it":[20],"is":[21,41,115,130,211],"computationally":[22],"expensive":[23],"to":[24,42,154,156],"train":[25],"detector":[28],"using":[29],"boosting,":[30],"especially":[31],"on":[32],"large":[33],"scale":[34],"datasets.":[35],"The":[36],"bottleneck":[37],"training":[39,202],"phase":[40],"select":[43],"the":[44,72,83,91,99,109,116,124,127,140,158,163,186,201,206,214],"best":[45,73,100],"learner":[46,74,101,160],"among":[47],"massive":[48],"learners.":[49,66],"Traditional":[50],"approaches":[51],"selecting":[53],"weak":[55,143,159],"classifier":[56],"usually":[57],"run":[58],"in":[59,82,108,120,133,179,189],"O(NT),":[60],"N":[62],"examples":[63],"T":[65,125],"In":[67,136],"this":[68],"paper,":[69],"we":[70,149],"treat":[71],"selection":[75],"as":[76,170,172],"Nearest":[78],"Neighbor":[79],"Search":[80],"problem":[81],"function":[84],"space":[85],"instead":[86],"feature":[88],"space.":[89],"With":[90],"help":[92],"Locality":[94],"Sensitive":[95],"Hashing":[96],"(LSH)":[97],"algorithm,":[98],"searching":[102],"procedure":[103],"can":[104,175,198],"be":[105],"speeded":[106],"up":[107],"time":[110],"O(NL),":[112],"where":[113],"L":[114,128],"number":[117],"buckets":[119],"LSH.":[121],"Compared":[122],"(~500,000),":[126],"(~600)":[129],"much":[131,171],"smaller":[132],"our":[134,196,209],"experiments.":[135],"addition,":[137],"through":[138],"studying":[139],"distribution":[141],"learners":[144],"candidate":[146],"query":[147,167],"points,":[148],"present":[150],"an":[151],"efficient":[152],"try":[155],"partition":[157],"points":[161,168],"feasible":[164],"region":[165],"uniformly":[169],"possible,":[173],"which":[174],"achieve":[176],"significant":[177],"improvement":[178],"both":[180],"recall":[181],"precision":[183],"compared":[184],"random":[187],"projection":[188],"traditional":[190],"LSH":[191],"algorithm.":[192],"Experimental":[193],"results":[194],"reveal":[195],"significantly":[199],"reduce":[200],"time.":[203],"And":[204],"still":[205],"comparable":[212],"state-of-art":[215],"methods.":[216]},"counts_by_year":[{"year":2020,"cited_by_count":1},{"year":2016,"cited_by_count":1},{"year":2015,"cited_by_count":2},{"year":2014,"cited_by_count":1},{"year":2012,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}