乐胖代购免代理版

{"id":"https://openalex.org/W1989655867","doi":"https://doi.org/10.1145/1631272.1631349","title":"Coboost learning of visual categories with 1st and 2nd order features from Google images","display_name":"Coboost learning of visual categories with 1st and 2nd order features from Google images","publication_year":2009,"publication_date":"2009-10-19","ids":{"openalex":"https://openalex.org/W1989655867","doi":"https://doi.org/10.1145/1631272.1631349","mag":"1989655867"},"language":"en","primary_location":{"is_oa":false,"landing_page_url":"https://doi.org/10.1145/1631272.1631349","pdf_url":null,"source":{"id":"https://openalex.org/S4363608757","display_name":"Proceedings of the 30th ACM International Conference on Multimedia","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"conference"},"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":false},"type":"article","type_crossref":"proceedings-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/A5100731599","display_name":"Xi Liu","orcid":"https://orcid.org/0000-0003-2336-8417"},"institutions":[{"id":"https://openalex.org/I19820366","display_name":"Chinese Academy of Sciences","ror":"https://ror.org/034t30j35","country_code":"CN","type":"government","lineage":["https://openalex.org/I19820366"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xi Liu","raw_affiliation_strings":["[Institute Of Computing Technology, Chinese Academy of Sciences, Beijing, China]"],"affiliations":[{"raw_affiliation_string":"[Institute Of Computing Technology, Chinese Academy of Sciences, Beijing, China]","institution_ids":["https://openalex.org/I19820366"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5072412248","display_name":"Zhiping Shi","orcid":"https://orcid.org/0000-0002-3562-8602"},"institutions":[{"id":"https://openalex.org/I19820366","display_name":"Chinese Academy of Sciences","ror":"https://ror.org/034t30j35","country_code":"CN","type":"government","lineage":["https://openalex.org/I19820366"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Zhiping Shi","raw_affiliation_strings":["[Institute Of Computing Technology, Chinese Academy of Sciences, Beijing, China]"],"affiliations":[{"raw_affiliation_string":"[Institute Of Computing Technology, Chinese Academy of Sciences, Beijing, China]","institution_ids":["https://openalex.org/I19820366"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100701695","display_name":"Zhixin Li","orcid":"https://orcid.org/0000-0002-5313-6134"},"institutions":[{"id":"https://openalex.org/I19820366","display_name":"Chinese Academy of Sciences","ror":"https://ror.org/034t30j35","country_code":"CN","type":"government","lineage":["https://openalex.org/I19820366"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Zhixin Li","raw_affiliation_strings":["[Institute Of Computing Technology, Chinese Academy of Sciences, Beijing, China]"],"affiliations":[{"raw_affiliation_string":"[Institute Of Computing Technology, Chinese Academy of Sciences, Beijing, China]","institution_ids":["https://openalex.org/I19820366"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5075925664","display_name":"Zhongzhi Shi","orcid":"https://orcid.org/0000-0002-3280-1676"},"institutions":[{"id":"https://openalex.org/I19820366","display_name":"Chinese Academy of Sciences","ror":"https://ror.org/034t30j35","country_code":"CN","type":"government","lineage":["https://openalex.org/I19820366"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Zhongzhi Shi","raw_affiliation_strings":["[Institute Of Computing Technology, Chinese Academy of Sciences, Beijing, China]"],"affiliations":[{"raw_affiliation_string":"[Institute Of Computing Technology, Chinese Academy of Sciences, Beijing, China]","institution_ids":["https://openalex.org/I19820366"]}]}],"institution_assertions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.231,"has_fulltext":true,"fulltext_origin":"ngrams","cited_by_count":1,"citation_normalized_percentile":{"value":0.203014,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":64,"max":71},"biblio":{"volume":null,"issue":null,"first_page":"533","last_page":"536"},"is_retracted":false,"is_paratext":false,"primary_topic":{"id":"https://openalex.org/T10627","display_name":"Advanced Image and Video Retrieval Techniques","score":0.9995,"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":0.9995,"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":0.9972,"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/T10824","display_name":"Image Retrieval and Classification Techniques","score":0.9947,"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","score":0.7423413},{"id":"https://openalex.org/keywords/benchmark","display_name":"Benchmark (surveying)","score":0.48878166},{"id":"https://openalex.org/keywords/supervised-learning","display_name":"Supervised Learning","score":0.4684197},{"id":"https://openalex.org/keywords/feature-learning","display_name":"Feature Learning","score":0.45374522},{"id":"https://openalex.org/keywords/bag-of-words-model","display_name":"Bag-of-words model","score":0.43236554}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8207388},{"id":"https://openalex.org/C46686674","wikidata":"https://www.wikidata.org/wiki/Q466303","display_name":"Boosting (machine learning)","level":2,"score":0.7423413},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7388456},{"id":"https://openalex.org/C95623464","wikidata":"https://www.wikidata.org/wiki/Q1096149","display_name":"Classifier (UML)","level":2,"score":0.6246594},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.56727844},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.53104025},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.48878166},{"id":"https://openalex.org/C136389625","wikidata":"https://www.wikidata.org/wiki/Q334384","display_name":"Supervised learning","level":3,"score":0.4684197},{"id":"https://openalex.org/C59404180","wikidata":"https://www.wikidata.org/wiki/Q17013334","display_name":"Feature learning","level":2,"score":0.45374522},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.44341683},{"id":"https://openalex.org/C45374587","wikidata":"https://www.wikidata.org/wiki/Q12525525","display_name":"Computation","level":2,"score":0.43255723},{"id":"https://openalex.org/C13672336","wikidata":"https://www.wikidata.org/wiki/Q3460803","display_name":"Bag-of-words model","level":2,"score":0.43236554},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.12975252},{"id":"https://openalex.org/C13280743","wikidata":"https://www.wikidata.org/wiki/Q131089","display_name":"Geodesy","level":1,"score":0.0},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","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":[{"is_oa":false,"landing_page_url":"https://doi.org/10.1145/1631272.1631349","pdf_url":null,"source":{"id":"https://openalex.org/S4363608757","display_name":"Proceedings of the 30th ACM International Conference on Multimedia","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"conference"},"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":false}],"best_oa_location":null,"sustainable_development_goals":[],"grants":[],"datasets":[],"versions":[],"referenced_works_count":10,"referenced_works":["https://openalex.org/W1496659369","https://openalex.org/W2080289064","https://openalex.org/W2099930951","https://openalex.org/W2112020727","https://openalex.org/W2123053055","https://openalex.org/W2134135198","https://openalex.org/W2168133252","https://openalex.org/W2171706135","https://openalex.org/W2172191903","https://openalex.org/W2785349534"],"related_works":["https://openalex.org/W4367598285","https://openalex.org/W4220945658","https://openalex.org/W2995914718","https://openalex.org/W2944309180","https://openalex.org/W2905846897","https://openalex.org/W2807311372","https://openalex.org/W2784352036","https://openalex.org/W2188464267","https://openalex.org/W2126920443","https://openalex.org/W2022996092"],"abstract_inverted_index":{"Conventional":[0],"object":[1],"recognition":[2],"techniques":[3],"rely":[4],"heavily":[5],"on":[6,82,134],"manually":[7],"annotated":[8],"image":[9],"datasets":[10,19],"to":[11,114,125],"achieve":[12],"good":[13],"performances.":[14],"However,":[15],"collecting":[16],"high":[17,116],"quality":[18],"is":[20,122],"really":[21],"laborious.":[22],"In":[23],"this":[24],"paper,":[25],"we":[26],"propose":[27],"a":[28,68],"semi-supervised":[29],"framework":[30],"for":[31,99],"learning":[32,120],"visual":[33],"categories":[34],"from":[35],"Google":[36],"Images.":[37],"The":[38],"1st":[39,84],"and":[40,50,61,85,146],"2nd":[41,86,104],"order":[42,87,105],"features,":[43],"which":[44,94],"define":[45],"bag":[46],"of":[47],"words":[48],"representation":[49],"spatial":[51],"relationship":[52],"between":[53],"local":[54],"features":[55,88,106],"respectively,":[56],"make":[57],"up":[58],"an":[59],"independent":[60],"redundant":[62],"feature":[63],"split.":[64],"We":[65,76,130],"then":[66],"integrate":[67],"cotraining":[69],"algorithm":[70],"CoBoost":[71],"with":[72,141],"these":[73],"two":[74,78],"features.":[75],"create":[77],"boosting":[79],"classifiers":[80],"based":[81],"the":[83,91,100,103,128,135,142],"respectively":[89],"in":[90],"training,":[92],"during":[93],"one":[95],"classifier":[96],"provides":[97],"labels":[98],"other.":[101],"Besides,":[102],"are":[107],"generated":[108],"dynamically":[109],"rather":[110],"than":[111],"extracted":[112],"exhaustively":[113],"avoid":[115],"computation.":[117],"An":[118],"active":[119],"technique":[121],"also":[123],"introduced":[124],"further":[126],"improve":[127],"performance.":[129],"evaluate":[131],"our":[132],"method":[133],"benchmark":[136],"datasets,":[137],"showing":[138],"results":[139],"competitive":[140],"state-of-the-art":[143],"unsupervised":[144],"approaches":[145],"some":[147],"supervised":[148],"techniques.":[149]},"cited_by_api_url":"https://api.openalex.org/works?filter=cites:W1989655867","counts_by_year":[{"year":2012,"cited_by_count":1}],"updated_date":"2025-01-16T03:54:12.170512","created_date":"2016-06-24"}