{"id":"https://openalex.org/W4232262564","doi":"https://doi.org/10.1109/cvpr.2010.5539870","title":"Visual event recognition in videos by learning from web data","display_name":"Visual event recognition in videos by learning from web data","publication_year":2010,"publication_date":"2010-06-01","ids":{"openalex":"https://openalex.org/W4232262564","doi":"https://doi.org/10.1109/cvpr.2010.5539870"},"language":"en","primary_location":{"id":"doi:10.1109/cvpr.2010.5539870","is_oa":false,"landing_page_url":"https://doi.org/10.1109/cvpr.2010.5539870","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition","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/A5080093489","display_name":"Lixin Duan","orcid":"https://orcid.org/0000-0002-0723-4016"},"institutions":[{"id":"https://openalex.org/I172675005","display_name":"Nanyang Technological University","ror":"https://ror.org/02e7b5302","country_code":"SG","type":"education","lineage":["https://openalex.org/I172675005"]}],"countries":["SG"],"is_corresponding":true,"raw_author_name":"Lixin Duan","raw_affiliation_strings":["School of Computer Engineering, Nanyang Technological University, Singapore"],"affiliations":[{"raw_affiliation_string":"School of Computer Engineering, Nanyang Technological University, Singapore","institution_ids":["https://openalex.org/I172675005"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5082181536","display_name":"Dong Xu","orcid":"https://orcid.org/0000-0003-2775-9730"},"institutions":[{"id":"https://openalex.org/I172675005","display_name":"Nanyang Technological University","ror":"https://ror.org/02e7b5302","country_code":"SG","type":"education","lineage":["https://openalex.org/I172675005"]}],"countries":["SG"],"is_corresponding":false,"raw_author_name":"Dong Xu","raw_affiliation_strings":["School of Computer Engineering, Nanyang Technological University, Singapore"],"affiliations":[{"raw_affiliation_string":"School of Computer Engineering, Nanyang Technological University, Singapore","institution_ids":["https://openalex.org/I172675005"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5021751767","display_name":"Ivor W. Tsang","orcid":"https://orcid.org/0000-0001-8095-4637"},"institutions":[{"id":"https://openalex.org/I172675005","display_name":"Nanyang Technological University","ror":"https://ror.org/02e7b5302","country_code":"SG","type":"education","lineage":["https://openalex.org/I172675005"]}],"countries":["SG"],"is_corresponding":false,"raw_author_name":"Ivor W. Tsang","raw_affiliation_strings":["School of Computer Engineering, Nanyang Technological University, Singapore"],"affiliations":[{"raw_affiliation_string":"School of Computer Engineering, Nanyang Technological University, Singapore","institution_ids":["https://openalex.org/I172675005"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5055469774","display_name":"Jiebo Luo","orcid":"https://orcid.org/0000-0002-4516-9729"},"institutions":[{"id":"https://openalex.org/I4210159451","display_name":"Kodak (United States)","ror":"https://ror.org/04rn3ph18","country_code":"US","type":"company","lineage":["https://openalex.org/I4210159451"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Jiebo Luo","raw_affiliation_strings":["Kodak Research Labs, Eastman Kodak (Japan) Limited, Rochester, NY, USA"],"affiliations":[{"raw_affiliation_string":"Kodak Research Labs, Eastman Kodak (Japan) Limited, Rochester, NY, USA","institution_ids":["https://openalex.org/I4210159451"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5080093489"],"corresponding_institution_ids":["https://openalex.org/I172675005"],"apc_list":null,"apc_paid":null,"fwci":16.2086,"has_fulltext":false,"cited_by_count":104,"citation_normalized_percentile":{"value":0.99385686,"is_in_top_1_percent":true,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":90,"max":100},"biblio":{"volume":null,"issue":null,"first_page":"1959","last_page":"1966"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11439","display_name":"Video Analysis and Summarization","score":0.9995999932289124,"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/T11439","display_name":"Video Analysis and Summarization","score":0.9995999932289124,"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/T10627","display_name":"Advanced Image and Video Retrieval Techniques","score":0.9991000294685364,"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/T10812","display_name":"Human Pose and Action Recognition","score":0.9983000159263611,"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/computer-science","display_name":"Computer science","score":0.7694640159606934},{"id":"https://openalex.org/keywords/multiple-kernel-learning","display_name":"Multiple kernel learning","score":0.7559211254119873},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6793158054351807},{"id":"https://openalex.org/keywords/scale-invariant-feature-transform","display_name":"Scale-invariant feature transform","score":0.5974615812301636},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.5594794750213623},{"id":"https://openalex.org/keywords/pyramid","display_name":"Pyramid (geometry)","score":0.54608154296875},{"id":"https://openalex.org/keywords/classifier","display_name":"Classifier (UML)","score":0.49864816665649414},{"id":"https://openalex.org/keywords/kernel","display_name":"Kernel (algebra)","score":0.4508098363876343},{"id":"https://openalex.org/keywords/support-vector-machine","display_name":"Support vector machine","score":0.45015662908554077},{"id":"https://openalex.org/keywords/feature-vector","display_name":"Feature vector","score":0.4458421766757965},{"id":"https://openalex.org/keywords/matching","display_name":"Matching (statistics)","score":0.4436952769756317},{"id":"https://openalex.org/keywords/feature-extraction","display_name":"Feature extraction","score":0.434267520904541},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.3542923331260681},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.33151084184646606},{"id":"https://openalex.org/keywords/kernel-method","display_name":"Kernel method","score":0.20651918649673462},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.14712050557136536}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7694640159606934},{"id":"https://openalex.org/C2776879701","wikidata":"https://www.wikidata.org/wiki/Q25048660","display_name":"Multiple kernel learning","level":4,"score":0.7559211254119873},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6793158054351807},{"id":"https://openalex.org/C61265191","wikidata":"https://www.wikidata.org/wiki/Q767770","display_name":"Scale-invariant feature transform","level":3,"score":0.5974615812301636},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.5594794750213623},{"id":"https://openalex.org/C142575187","wikidata":"https://www.wikidata.org/wiki/Q3358290","display_name":"Pyramid (geometry)","level":2,"score":0.54608154296875},{"id":"https://openalex.org/C95623464","wikidata":"https://www.wikidata.org/wiki/Q1096149","display_name":"Classifier (UML)","level":2,"score":0.49864816665649414},{"id":"https://openalex.org/C74193536","wikidata":"https://www.wikidata.org/wiki/Q574844","display_name":"Kernel (algebra)","level":2,"score":0.4508098363876343},{"id":"https://openalex.org/C12267149","wikidata":"https://www.wikidata.org/wiki/Q282453","display_name":"Support vector machine","level":2,"score":0.45015662908554077},{"id":"https://openalex.org/C83665646","wikidata":"https://www.wikidata.org/wiki/Q42139305","display_name":"Feature vector","level":2,"score":0.4458421766757965},{"id":"https://openalex.org/C165064840","wikidata":"https://www.wikidata.org/wiki/Q1321061","display_name":"Matching (statistics)","level":2,"score":0.4436952769756317},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.434267520904541},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.3542923331260681},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.33151084184646606},{"id":"https://openalex.org/C122280245","wikidata":"https://www.wikidata.org/wiki/Q620622","display_name":"Kernel method","level":3,"score":0.20651918649673462},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.14712050557136536},{"id":"https://openalex.org/C2524010","wikidata":"https://www.wikidata.org/wiki/Q8087","display_name":"Geometry","level":1,"score":0.0},{"id":"https://openalex.org/C114614502","wikidata":"https://www.wikidata.org/wiki/Q76592","display_name":"Combinatorics","level":1,"score":0.0},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/cvpr.2010.5539870","is_oa":false,"landing_page_url":"https://doi.org/10.1109/cvpr.2010.5539870","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":28,"referenced_works":["https://openalex.org/W2010399676","https://openalex.org/W2036931824","https://openalex.org/W2068576236","https://openalex.org/W2077072595","https://openalex.org/W2104311745","https://openalex.org/W2115213191","https://openalex.org/W2119799051","https://openalex.org/W2120601899","https://openalex.org/W2142194269","https://openalex.org/W2145295623","https://openalex.org/W2151103935","https://openalex.org/W2160039895","https://openalex.org/W2161612652","https://openalex.org/W2162915993","https://openalex.org/W2164943005","https://openalex.org/W2539525772","https://openalex.org/W2547599103","https://openalex.org/W3146885639","https://openalex.org/W4236055315","https://openalex.org/W4249279051","https://openalex.org/W6641446668","https://openalex.org/W6667730386","https://openalex.org/W6677137762","https://openalex.org/W6678296315","https://openalex.org/W6683390231","https://openalex.org/W6683492740","https://openalex.org/W6728706464","https://openalex.org/W6729213900"],"related_works":["https://openalex.org/W3013515612","https://openalex.org/W2786306966","https://openalex.org/W2336974148","https://openalex.org/W2345184372","https://openalex.org/W3080336085","https://openalex.org/W2738461075","https://openalex.org/W2889582799","https://openalex.org/W2187500075","https://openalex.org/W3212974055","https://openalex.org/W2801009187"],"abstract_inverted_index":{"We":[0,54],"propose":[1,26,84,180],"a":[2,13,27,85,143,181,236],"visual":[3],"event":[4],"recognition":[5],"framework":[6,232],"for":[7],"consumer":[8,129,241],"domain":[9,127],"videos":[10,20,121,242],"by":[11,208,243],"leveraging":[12,244],"large":[14],"amount":[15],"of":[16,138,145,161,229,239],"loosely":[17],"labeled":[18,240],"web":[19,126,245],"(e.g.,":[21],"from":[22,68,97,122,154,221],"YouTube).":[23],"First,":[24],"we":[25,83,141,179],"new":[28,86],"aligned":[29],"space-time":[30,49,104],"pyramid":[31,99,133],"matching":[32],"method":[33,89],"to":[34,77,92,173,186,193],"measure":[35],"the":[36,56,66,80,95,114,150,204,211,216,227],"distances":[37,58],"between":[38,59,120,218],"two":[39,61,123,155,222],"video":[40,44],"clips,":[41],"where":[42],"each":[43,132,136],"clip":[45],"is":[46],"divided":[47],"into":[48],"volumes":[50,62,70],"over":[51],"multiple":[52,98,158,200],"levels.":[53],"calculate":[55],"pair-wise":[57],"any":[60],"and":[63,101,106,110,128,135,165,203,215],"further":[64],"integrate":[65],"information":[67,96],"different":[69,162],"with":[71,113,170],"Integer-flow":[72],"Earth":[73],"Mover's":[74],"Distance":[75],"(EMD)":[76],"explicitly":[78],"align":[79],"volumes.":[81],"Second,":[82],"cross-domain":[87,182],"learning":[88,183],"in":[90,117],"order":[91],"1)":[93],"fuse":[94],"levels":[100],"features":[102],"(i.e.,":[103,125],"feature":[105,118],"static":[107],"SIFT":[108],"feature)":[109],"2)":[111],"cope":[112],"considerable":[115],"variation":[116],"distributions":[119,220],"domains":[124,156],"domain).":[130],"For":[131],"level":[134],"type":[137],"local":[139],"features,":[140],"train":[142],"set":[144,153],"SVM":[146],"classifiers":[147,207],"based":[148,198],"on":[149,199],"combined":[151],"training":[152],"using":[157],"base":[159,201],"kernels":[160,202],"kernel":[163],"types":[164],"parameters,":[166],"which":[167],"are":[168],"fused":[169],"equal":[171],"weights":[172],"obtain":[174],"an":[175,195],"average":[176,206],"classifier.":[177],"Finally,":[178],"method,":[184],"referred":[185],"as":[187],"Adaptive":[188],"Multiple":[189],"Kernel":[190],"Learning":[191],"(A-MKL),":[192],"learn":[194],"adapted":[196],"classifier":[197],"prelearned":[205],"minimizing":[209],"both":[210],"structural":[212],"risk":[213],"functional":[214],"mismatch":[217],"data":[219],"domains.":[223],"Extensive":[224],"experiments":[225],"demonstrate":[226],"effectiveness":[228],"our":[230],"proposed":[231],"that":[233],"requires":[234],"only":[235],"small":[237],"number":[238],"data.":[246]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2022,"cited_by_count":1},{"year":2021,"cited_by_count":2},{"year":2020,"cited_by_count":5},{"year":2019,"cited_by_count":8},{"year":2018,"cited_by_count":4},{"year":2017,"cited_by_count":4},{"year":2016,"cited_by_count":5},{"year":2015,"cited_by_count":11},{"year":2014,"cited_by_count":13},{"year":2013,"cited_by_count":9},{"year":2012,"cited_by_count":21}],"updated_date":"2026-04-03T22:45:19.894376","created_date":"2025-10-10T00:00:00"}