{"id":"https://openalex.org/W1968039196","doi":"https://doi.org/10.1109/wacv.2014.6836094","title":"Feature combination with Multi-Kernel Learning for fine-grained visual classification","display_name":"Feature combination with Multi-Kernel Learning for fine-grained visual classification","publication_year":2014,"publication_date":"2014-03-01","ids":{"openalex":"https://openalex.org/W1968039196","doi":"https://doi.org/10.1109/wacv.2014.6836094","mag":"1968039196"},"language":"en","primary_location":{"id":"doi:10.1109/wacv.2014.6836094","is_oa":false,"landing_page_url":"https://doi.org/10.1109/wacv.2014.6836094","pdf_url":null,"source":{"id":"https://openalex.org/S4393918690","display_name":"IEEE Winter Conference on Applications of Computer Vision","issn_l":"2472-6737","issn":["2472-6737"],"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":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Winter Conference on Applications of 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/A5066709641","display_name":"Anelia Angelova","orcid":"https://orcid.org/0000-0003-1822-7943"},"institutions":[{"id":"https://openalex.org/I1291425158","display_name":"Google (United States)","ror":"https://ror.org/00njsd438","country_code":"US","type":"company","lineage":["https://openalex.org/I1291425158","https://openalex.org/I4210128969"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Anelia Angelova","raw_affiliation_strings":["Google Inc","Google, Inc, USA"],"affiliations":[{"raw_affiliation_string":"Google Inc","institution_ids":["https://openalex.org/I1291425158"]},{"raw_affiliation_string":"Google, Inc, USA","institution_ids":["https://openalex.org/I1291425158"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5029480393","display_name":"Alexandru Niculescu-Mizil","orcid":null},"institutions":[{"id":"https://openalex.org/I4210107353","display_name":"NEC (United States)","ror":"https://ror.org/01v791m31","country_code":"US","type":"company","lineage":["https://openalex.org/I118347220","https://openalex.org/I4210107353"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Alexandru Niculescu-Mizil","raw_affiliation_strings":["NEC Labs America","NEC Labs America, USA"],"affiliations":[{"raw_affiliation_string":"NEC Labs America","institution_ids":["https://openalex.org/I4210107353"]},{"raw_affiliation_string":"NEC Labs America, USA","institution_ids":["https://openalex.org/I4210107353"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5066709641"],"corresponding_institution_ids":["https://openalex.org/I1291425158"],"apc_list":null,"apc_paid":null,"fwci":0.2556,"has_fulltext":false,"cited_by_count":2,"citation_normalized_percentile":{"value":0.46098563,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":94},"biblio":{"volume":null,"issue":null,"first_page":"241","last_page":"246"},"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":0.9997000098228455,"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":0.9997000098228455,"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":0.9994999766349792,"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/T10812","display_name":"Human Pose and Action Recognition","score":0.9984999895095825,"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":0.7948483228683472},{"id":"https://openalex.org/keywords/concatenation","display_name":"Concatenation (mathematics)","score":0.7200816869735718},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.7051810622215271},{"id":"https://openalex.org/keywords/kernel","display_name":"Kernel (algebra)","score":0.6947882175445557},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.6101865172386169},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.6065464615821838},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.5895619988441467},{"id":"https://openalex.org/keywords/contextual-image-classification","display_name":"Contextual image classification","score":0.4847997725009918},{"id":"https://openalex.org/keywords/feature-extraction","display_name":"Feature extraction","score":0.4311577379703522},{"id":"https://openalex.org/keywords/multiple-kernel-learning","display_name":"Multiple kernel learning","score":0.4285355806350708},{"id":"https://openalex.org/keywords/kernel-method","display_name":"Kernel method","score":0.3378637433052063},{"id":"https://openalex.org/keywords/support-vector-machine","display_name":"Support vector machine","score":0.32066404819488525},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.14682650566101074},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.08078038692474365}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7948483228683472},{"id":"https://openalex.org/C87619178","wikidata":"https://www.wikidata.org/wiki/Q126002","display_name":"Concatenation (mathematics)","level":2,"score":0.7200816869735718},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7051810622215271},{"id":"https://openalex.org/C74193536","wikidata":"https://www.wikidata.org/wiki/Q574844","display_name":"Kernel (algebra)","level":2,"score":0.6947882175445557},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.6101865172386169},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.6065464615821838},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.5895619988441467},{"id":"https://openalex.org/C75294576","wikidata":"https://www.wikidata.org/wiki/Q5165192","display_name":"Contextual image classification","level":3,"score":0.4847997725009918},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.4311577379703522},{"id":"https://openalex.org/C2776879701","wikidata":"https://www.wikidata.org/wiki/Q25048660","display_name":"Multiple kernel learning","level":4,"score":0.4285355806350708},{"id":"https://openalex.org/C122280245","wikidata":"https://www.wikidata.org/wiki/Q620622","display_name":"Kernel method","level":3,"score":0.3378637433052063},{"id":"https://openalex.org/C12267149","wikidata":"https://www.wikidata.org/wiki/Q282453","display_name":"Support vector machine","level":2,"score":0.32066404819488525},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.14682650566101074},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.08078038692474365},{"id":"https://openalex.org/C114614502","wikidata":"https://www.wikidata.org/wiki/Q76592","display_name":"Combinatorics","level":1,"score":0.0},{"id":"https://openalex.org/C41895202","wikidata":"https://www.wikidata.org/wiki/Q8162","display_name":"Linguistics","level":1,"score":0.0},{"id":"https://openalex.org/C138885662","wikidata":"https://www.wikidata.org/wiki/Q5891","display_name":"Philosophy","level":0,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1109/wacv.2014.6836094","is_oa":false,"landing_page_url":"https://doi.org/10.1109/wacv.2014.6836094","pdf_url":null,"source":{"id":"https://openalex.org/S4393918690","display_name":"IEEE Winter Conference on Applications of Computer Vision","issn_l":"2472-6737","issn":["2472-6737"],"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":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Winter Conference on Applications of Computer Vision","raw_type":"proceedings-article"},{"id":"pmh:oai:CiteSeerX.psu:10.1.1.681.341","is_oa":false,"landing_page_url":"http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.681.341","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"http://www.niculescu-mizil.org/papers/fine_grained_mkl.pdf","raw_type":"text"}],"best_oa_location":null,"sustainable_development_goals":[{"score":0.6499999761581421,"id":"https://metadata.un.org/sdg/4","display_name":"Quality Education"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":32,"referenced_works":["https://openalex.org/W1740618102","https://openalex.org/W1944448017","https://openalex.org/W1994213117","https://openalex.org/W2003033789","https://openalex.org/W2083367367","https://openalex.org/W2091759811","https://openalex.org/W2103490241","https://openalex.org/W2110015572","https://openalex.org/W2110765924","https://openalex.org/W2124372976","https://openalex.org/W2125993116","https://openalex.org/W2128154306","https://openalex.org/W2142387771","https://openalex.org/W2142623206","https://openalex.org/W2152411181","https://openalex.org/W2153635508","https://openalex.org/W2161969291","https://openalex.org/W2213241010","https://openalex.org/W2229419338","https://openalex.org/W2533598788","https://openalex.org/W2538008885","https://openalex.org/W2585858557","https://openalex.org/W4246066915","https://openalex.org/W4285719527","https://openalex.org/W6637692013","https://openalex.org/W6640526699","https://openalex.org/W6651233513","https://openalex.org/W6671218710","https://openalex.org/W6675696936","https://openalex.org/W6680751671","https://openalex.org/W6688387854","https://openalex.org/W6689283729"],"related_works":["https://openalex.org/W2900715739","https://openalex.org/W2289496068","https://openalex.org/W2547116720","https://openalex.org/W2043864454","https://openalex.org/W2188831877","https://openalex.org/W2599254681","https://openalex.org/W4291669689","https://openalex.org/W2153379791","https://openalex.org/W2001173190","https://openalex.org/W2295320501"],"abstract_inverted_index":{"This":[0],"paper":[1],"addresses":[2],"the":[3,21,27,31,41,63,75,82],"problem":[4],"of":[5,30],"fine-grained":[6,58],"recognition":[7],"in":[8,44,85],"which":[9,79],"local,":[10],"mid-level":[11],"features":[12,32,37,87],"are":[13],"used":[14],"for":[15,57],"classification.":[16],"We":[17],"propose":[18],"to":[19,25,34,40,62],"use":[20],"Multi-Kernel":[22],"Learning":[23],"framework":[24],"learn":[26],"relative":[28],"importance":[29],"and":[33],"select":[35],"optimal":[36],"with":[38],"regards":[39],"classification":[42,52],"performance,":[43],"a":[45],"principled":[46],"way.":[47],"Our":[48],"results":[49,53],"show":[50],"improved":[51],"on":[54],"common":[55],"benchmarks":[56],"classification,":[59],"as":[60],"compared":[61],"best":[64],"prior":[65],"state-of-the-art":[66],"methods.":[67],"The":[68],"proposed":[69],"learning-based":[70],"combination":[71,77],"method":[72],"also":[73],"improves":[74],"concatenation":[76],"approach":[78],"has":[80],"been":[81],"standard":[83],"practice":[84],"combining":[86],"so":[88],"far.":[89]},"counts_by_year":[{"year":2018,"cited_by_count":1},{"year":2017,"cited_by_count":1}],"updated_date":"2026-04-05T17:49:38.594831","created_date":"2025-10-10T00:00:00"}
