{"id":"https://openalex.org/W2537929378","doi":"https://doi.org/10.1109/iccv.2009.5459179","title":"Incremental Multiple Kernel Learning for object recognition","display_name":"Incremental Multiple Kernel Learning for object recognition","publication_year":2009,"publication_date":"2009-09-01","ids":{"openalex":"https://openalex.org/W2537929378","doi":"https://doi.org/10.1109/iccv.2009.5459179","mag":"2537929378"},"language":"en","primary_location":{"id":"doi:10.1109/iccv.2009.5459179","is_oa":false,"landing_page_url":"https://doi.org/10.1109/iccv.2009.5459179","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/A5018421415","display_name":"Aniruddha Kembhavi","orcid":"https://orcid.org/0000-0002-7608-7443"},"institutions":[{"id":"https://openalex.org/I66946132","display_name":"University of Maryland, College Park","ror":"https://ror.org/047s2c258","country_code":"US","type":"education","lineage":["https://openalex.org/I66946132"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Aniruddha Kembhavi","raw_affiliation_strings":["University of Maryland, College Park, USA"],"affiliations":[{"raw_affiliation_string":"University of Maryland, College Park, USA","institution_ids":["https://openalex.org/I66946132"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5049953837","display_name":"Behjat Siddiquie","orcid":null},"institutions":[{"id":"https://openalex.org/I66946132","display_name":"University of Maryland, College Park","ror":"https://ror.org/047s2c258","country_code":"US","type":"education","lineage":["https://openalex.org/I66946132"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Behjat Siddiquie","raw_affiliation_strings":["University of Maryland, College Park, USA"],"affiliations":[{"raw_affiliation_string":"University of Maryland, College Park, USA","institution_ids":["https://openalex.org/I66946132"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5088044746","display_name":"Roland Miezianko","orcid":null},"institutions":[{"id":"https://openalex.org/I4210101534","display_name":"Honeywell (India)","ror":"https://ror.org/017eb5121","country_code":"IN","type":"company","lineage":["https://openalex.org/I4210101534","https://openalex.org/I82514191"]}],"countries":["IN"],"is_corresponding":false,"raw_author_name":"Roland Miezianko","raw_affiliation_strings":["Honeywell Laboratories, India"],"affiliations":[{"raw_affiliation_string":"Honeywell Laboratories, India","institution_ids":["https://openalex.org/I4210101534"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5031665162","display_name":"Scott McCloskey","orcid":null},"institutions":[{"id":"https://openalex.org/I4210101534","display_name":"Honeywell (India)","ror":"https://ror.org/017eb5121","country_code":"IN","type":"company","lineage":["https://openalex.org/I4210101534","https://openalex.org/I82514191"]}],"countries":["IN"],"is_corresponding":false,"raw_author_name":"Scott McCloskey","raw_affiliation_strings":["Honeywell Laboratories, India"],"affiliations":[{"raw_affiliation_string":"Honeywell Laboratories, India","institution_ids":["https://openalex.org/I4210101534"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5111454036","display_name":"Larry S. Davis","orcid":null},"institutions":[{"id":"https://openalex.org/I66946132","display_name":"University of Maryland, College Park","ror":"https://ror.org/047s2c258","country_code":"US","type":"education","lineage":["https://openalex.org/I66946132"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Larry S. Davis","raw_affiliation_strings":["University of Maryland, College Park, USA"],"affiliations":[{"raw_affiliation_string":"University of Maryland, College Park, USA","institution_ids":["https://openalex.org/I66946132"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":5,"corresponding_author_ids":["https://openalex.org/A5018421415"],"corresponding_institution_ids":["https://openalex.org/I66946132"],"apc_list":null,"apc_paid":null,"fwci":7.6586,"has_fulltext":false,"cited_by_count":44,"citation_normalized_percentile":{"value":0.97350206,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":90,"max":99},"biblio":{"volume":null,"issue":null,"first_page":"638","last_page":"645"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"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"}},"topics":[{"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/T10627","display_name":"Advanced Image and Video Retrieval Techniques","score":0.9988999962806702,"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/T10331","display_name":"Video Surveillance and Tracking Methods","score":0.9966999888420105,"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":0.8366077542304993},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6979407072067261},{"id":"https://openalex.org/keywords/kernel","display_name":"Kernel (algebra)","score":0.6729152798652649},{"id":"https://openalex.org/keywords/task","display_name":"Task (project management)","score":0.619293212890625},{"id":"https://openalex.org/keywords/categorization","display_name":"Categorization","score":0.601542592048645},{"id":"https://openalex.org/keywords/cognitive-neuroscience-of-visual-object-recognition","display_name":"Cognitive neuroscience of visual object recognition","score":0.5467109680175781},{"id":"https://openalex.org/keywords/object","display_name":"Object (grammar)","score":0.5102741718292236},{"id":"https://openalex.org/keywords/intersection","display_name":"Intersection (aeronautics)","score":0.505129873752594},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.47037023305892944},{"id":"https://openalex.org/keywords/object-detection","display_name":"Object detection","score":0.4699801504611969},{"id":"https://openalex.org/keywords/multiple-kernel-learning","display_name":"Multiple kernel learning","score":0.4469508230686188},{"id":"https://openalex.org/keywords/support-vector-machine","display_name":"Support vector machine","score":0.4400706887245178},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.4381709396839142},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.41579994559288025},{"id":"https://openalex.org/keywords/kernel-method","display_name":"Kernel method","score":0.23841893672943115}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8366077542304993},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6979407072067261},{"id":"https://openalex.org/C74193536","wikidata":"https://www.wikidata.org/wiki/Q574844","display_name":"Kernel (algebra)","level":2,"score":0.6729152798652649},{"id":"https://openalex.org/C2780451532","wikidata":"https://www.wikidata.org/wiki/Q759676","display_name":"Task (project management)","level":2,"score":0.619293212890625},{"id":"https://openalex.org/C94124525","wikidata":"https://www.wikidata.org/wiki/Q912550","display_name":"Categorization","level":2,"score":0.601542592048645},{"id":"https://openalex.org/C64876066","wikidata":"https://www.wikidata.org/wiki/Q5141226","display_name":"Cognitive neuroscience of visual object recognition","level":3,"score":0.5467109680175781},{"id":"https://openalex.org/C2781238097","wikidata":"https://www.wikidata.org/wiki/Q175026","display_name":"Object (grammar)","level":2,"score":0.5102741718292236},{"id":"https://openalex.org/C64543145","wikidata":"https://www.wikidata.org/wiki/Q162942","display_name":"Intersection (aeronautics)","level":2,"score":0.505129873752594},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.47037023305892944},{"id":"https://openalex.org/C2776151529","wikidata":"https://www.wikidata.org/wiki/Q3045304","display_name":"Object detection","level":3,"score":0.4699801504611969},{"id":"https://openalex.org/C2776879701","wikidata":"https://www.wikidata.org/wiki/Q25048660","display_name":"Multiple kernel learning","level":4,"score":0.4469508230686188},{"id":"https://openalex.org/C12267149","wikidata":"https://www.wikidata.org/wiki/Q282453","display_name":"Support vector machine","level":2,"score":0.4400706887245178},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.4381709396839142},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.41579994559288025},{"id":"https://openalex.org/C122280245","wikidata":"https://www.wikidata.org/wiki/Q620622","display_name":"Kernel method","level":3,"score":0.23841893672943115},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.0},{"id":"https://openalex.org/C187736073","wikidata":"https://www.wikidata.org/wiki/Q2920921","display_name":"Management","level":1,"score":0.0},{"id":"https://openalex.org/C162324750","wikidata":"https://www.wikidata.org/wiki/Q8134","display_name":"Economics","level":0,"score":0.0},{"id":"https://openalex.org/C146978453","wikidata":"https://www.wikidata.org/wiki/Q3798668","display_name":"Aerospace engineering","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/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1109/iccv.2009.5459179","is_oa":false,"landing_page_url":"https://doi.org/10.1109/iccv.2009.5459179","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"},{"id":"pmh:oai:CiteSeerX.psu:10.1.1.210.9692","is_oa":false,"landing_page_url":"http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.210.9692","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"http://www.cs.umd.edu/~behjat/papers/ICCV09.pdf","raw_type":"text"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":28,"referenced_works":["https://openalex.org/W2012330712","https://openalex.org/W2031823405","https://openalex.org/W2048679005","https://openalex.org/W2096112371","https://openalex.org/W2098054142","https://openalex.org/W2103658758","https://openalex.org/W2108807072","https://openalex.org/W2112020727","https://openalex.org/W2120219904","https://openalex.org/W2120515362","https://openalex.org/W2121680631","https://openalex.org/W2122808326","https://openalex.org/W2127597232","https://openalex.org/W2132641846","https://openalex.org/W2134135198","https://openalex.org/W2145295623","https://openalex.org/W2148596671","https://openalex.org/W2150772522","https://openalex.org/W2154422044","https://openalex.org/W2154462399","https://openalex.org/W2154683974","https://openalex.org/W2161969291","https://openalex.org/W2162915993","https://openalex.org/W2165828254","https://openalex.org/W6641446668","https://openalex.org/W6674636832","https://openalex.org/W6676485797","https://openalex.org/W6683181193"],"related_works":["https://openalex.org/W2165912799","https://openalex.org/W2735662278","https://openalex.org/W2382615723","https://openalex.org/W4311804456","https://openalex.org/W1987484445","https://openalex.org/W2623658258","https://openalex.org/W2143413548","https://openalex.org/W2573606541","https://openalex.org/W2912559722","https://openalex.org/W4387272257"],"abstract_inverted_index":{"A":[0,141],"good":[1,18],"training":[2,63,80,157],"dataset,":[3],"representative":[4],"of":[5,20,29,42,116,132],"the":[6,43,70,79,85,124,133,145,152,170,174],"test":[7],"images":[8,115],"expected":[9],"in":[10,102,118,123,139,144,173],"a":[11,21,39,61,98,103,107],"given":[12],"application,":[13],"is":[14,149,163],"critical":[15],"for":[16],"ensuring":[17],"performance":[19],"visual":[22,30],"categorization":[23],"system.":[24],"Obtaining":[25],"task":[26,72],"specific":[27,156],"datasets":[28],"categories":[31],"is,":[32],"however,":[33],"far":[34],"more":[35,120],"tedious":[36],"than":[37],"obtaining":[38],"generic":[40,62],"dataset":[41,81],"same":[44],"classes.":[45],"We":[46,93],"propose":[47],"an":[48,137],"Incremental":[49],"Multiple":[50],"Kernel":[51],"Learning":[52],"(IMKL)":[53],"approach":[54],"to":[55,69,88,136,166,169,179],"object":[56],"recognition":[57],"that":[58],"initializes":[59],"on":[60,97],"database":[64],"and":[65],"then":[66],"tunes":[67],"itself":[68,113,168],"classification":[71,100],"at":[73],"hand.":[74],"Our":[75,110],"system":[76,96,111,153,162],"simultaneously":[77],"updates":[78,112],"as":[82,84,126,128,151,176],"well":[83,127],"weights":[86,148],"used":[87],"combine":[89],"multiple":[90],"information":[91],"sources.":[92],"demonstrate":[94],"our":[95],"vehicle":[99],"problem":[101],"video":[104],"stream":[105],"overlooking":[106],"traffic":[108],"intersection.":[109],"with":[114,129],"vehicles":[117],"poses":[119],"commonly":[121],"observed":[122,150],"scene,":[125],"image":[130],"patches":[131],"background,":[134],"leading":[135],"increase":[138],"performance.":[140],"considerable":[142],"change":[143,172],"kernel":[146],"combination":[147],"gathers":[154],"scene":[155,175],"data":[158],"over":[159],"time.":[160],"The":[161],"also":[164],"seen":[165],"adapt":[167],"illumination":[171],"day":[177],"transitions":[178],"night.":[180]},"counts_by_year":[{"year":2022,"cited_by_count":1},{"year":2020,"cited_by_count":2},{"year":2019,"cited_by_count":2},{"year":2018,"cited_by_count":3},{"year":2017,"cited_by_count":2},{"year":2016,"cited_by_count":2},{"year":2015,"cited_by_count":4},{"year":2014,"cited_by_count":6},{"year":2013,"cited_by_count":5},{"year":2012,"cited_by_count":7}],"updated_date":"2026-04-04T16:13:02.066488","created_date":"2025-10-10T00:00:00"}
