{"id":"https://openalex.org/W2134380836","doi":"https://doi.org/10.1109/cvpr.2008.4587630","title":"Classification using intersection kernel support vector machines is efficient","display_name":"Classification using intersection kernel support vector machines is efficient","publication_year":2008,"publication_date":"2008-06-01","ids":{"openalex":"https://openalex.org/W2134380836","doi":"https://doi.org/10.1109/cvpr.2008.4587630","mag":"2134380836"},"language":"en","primary_location":{"id":"doi:10.1109/cvpr.2008.4587630","is_oa":false,"landing_page_url":"https://doi.org/10.1109/cvpr.2008.4587630","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2008 IEEE Conference on Computer Vision and Pattern Recognition","raw_type":"proceedings-article"},"type":"conference-paper","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/A5052551454","display_name":"Subhransu Maji","orcid":"https://orcid.org/0000-0002-3869-9334"},"institutions":[{"id":"https://openalex.org/I95457486","display_name":"University of California, Berkeley","ror":"https://ror.org/01an7q238","country_code":"US","type":"education","lineage":["https://openalex.org/I95457486"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Subhransu Maji","raw_affiliation_strings":["EECS Department, University of California, Berkeley, USA","EECS Department, U.C. Berkeley, Berkeley, CA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"EECS Department, University of California, Berkeley, USA","institution_ids":["https://openalex.org/I95457486"]},{"raw_affiliation_string":"EECS Department, U.C. Berkeley, Berkeley, CA","institution_ids":["https://openalex.org/I95457486"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5104361813","display_name":"Alexander C. Berg","orcid":null},"institutions":[{"id":"https://openalex.org/I1325784139","display_name":"Yahoo (United Kingdom)","ror":"https://ror.org/038p3gq39","country_code":"GB","type":"company","lineage":["https://openalex.org/I1325784139","https://openalex.org/I4210134091"]},{"id":"https://openalex.org/I4210134091","display_name":"Yahoo (United States)","ror":"https://ror.org/040dkzz12","country_code":"US","type":"company","lineage":["https://openalex.org/I4210134091"]}],"countries":["GB","US"],"is_corresponding":false,"raw_author_name":"Alexander C. Berg","raw_affiliation_strings":["Yahoo Research, USA","[Yahoo Research, Chile]"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Yahoo Research, USA","institution_ids":["https://openalex.org/I4210134091"]},{"raw_affiliation_string":"[Yahoo Research, Chile]","institution_ids":["https://openalex.org/I1325784139"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5001594573","display_name":"Jitendra Malik","orcid":"https://orcid.org/0000-0003-3695-1580"},"institutions":[{"id":"https://openalex.org/I95457486","display_name":"University of California, Berkeley","ror":"https://ror.org/01an7q238","country_code":"US","type":"education","lineage":["https://openalex.org/I95457486"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Jitendra Malik","raw_affiliation_strings":["EECS Department, University of California, Berkeley, USA","EECS Dept., U.C. Berkeley, USA#TAB#"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"EECS Department, University of California, Berkeley, USA","institution_ids":["https://openalex.org/I95457486"]},{"raw_affiliation_string":"EECS Dept., U.C. Berkeley, USA#TAB#","institution_ids":["https://openalex.org/I95457486"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":3,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":45.3568,"has_fulltext":false,"cited_by_count":938,"citation_normalized_percentile":{"value":0.99947614,"is_in_top_1_percent":true,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":94,"max":100},"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"8"},"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":0.9986000061035156,"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":0.9986000061035156,"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/T12535","display_name":"Machine Learning and Data Classification","score":0.9973999857902527,"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/T10057","display_name":"Face and Expression Recognition","score":0.9957000017166138,"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/support-vector-machine","display_name":"Support vector machine","score":0.7532167434692383},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6079766750335693},{"id":"https://openalex.org/keywords/histogram","display_name":"Histogram","score":0.6055917739868164},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.525917649269104},{"id":"https://openalex.org/keywords/classifier","display_name":"Classifier (UML)","score":0.5203740000724792},{"id":"https://openalex.org/keywords/kernel","display_name":"Kernel (algebra)","score":0.4845563471317291},{"id":"https://openalex.org/keywords/pedestrian-detection","display_name":"Pedestrian detection","score":0.475364625453949},{"id":"https://openalex.org/keywords/kernel-method","display_name":"Kernel method","score":0.4734337329864502},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.46431538462638855},{"id":"https://openalex.org/keywords/logarithm","display_name":"Logarithm","score":0.4372090697288513},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.30535393953323364},{"id":"https://openalex.org/keywords/combinatorics","display_name":"Combinatorics","score":0.10604920983314514},{"id":"https://openalex.org/keywords/pedestrian","display_name":"Pedestrian","score":0.0802568793296814}],"concepts":[{"id":"https://openalex.org/C12267149","wikidata":"https://www.wikidata.org/wiki/Q282453","display_name":"Support vector machine","level":2,"score":0.7532167434692383},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6079766750335693},{"id":"https://openalex.org/C53533937","wikidata":"https://www.wikidata.org/wiki/Q185020","display_name":"Histogram","level":3,"score":0.6055917739868164},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.525917649269104},{"id":"https://openalex.org/C95623464","wikidata":"https://www.wikidata.org/wiki/Q1096149","display_name":"Classifier (UML)","level":2,"score":0.5203740000724792},{"id":"https://openalex.org/C74193536","wikidata":"https://www.wikidata.org/wiki/Q574844","display_name":"Kernel (algebra)","level":2,"score":0.4845563471317291},{"id":"https://openalex.org/C2780156472","wikidata":"https://www.wikidata.org/wiki/Q2355550","display_name":"Pedestrian detection","level":3,"score":0.475364625453949},{"id":"https://openalex.org/C122280245","wikidata":"https://www.wikidata.org/wiki/Q620622","display_name":"Kernel method","level":3,"score":0.4734337329864502},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.46431538462638855},{"id":"https://openalex.org/C39927690","wikidata":"https://www.wikidata.org/wiki/Q11197","display_name":"Logarithm","level":2,"score":0.4372090697288513},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.30535393953323364},{"id":"https://openalex.org/C114614502","wikidata":"https://www.wikidata.org/wiki/Q76592","display_name":"Combinatorics","level":1,"score":0.10604920983314514},{"id":"https://openalex.org/C2777113093","wikidata":"https://www.wikidata.org/wiki/Q221488","display_name":"Pedestrian","level":2,"score":0.0802568793296814},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.0},{"id":"https://openalex.org/C134306372","wikidata":"https://www.wikidata.org/wiki/Q7754","display_name":"Mathematical analysis","level":1,"score":0.0},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.0},{"id":"https://openalex.org/C22212356","wikidata":"https://www.wikidata.org/wiki/Q775325","display_name":"Transport engineering","level":1,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1109/cvpr.2008.4587630","is_oa":false,"landing_page_url":"https://doi.org/10.1109/cvpr.2008.4587630","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2008 IEEE Conference on Computer Vision and Pattern Recognition","raw_type":"proceedings-article"},{"id":"pmh:oai:CiteSeerX.psu:10.1.1.141.3974","is_oa":false,"landing_page_url":"http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.141.3974","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"http://www.cs.berkeley.edu/~aberg/papers/mbm08cvpr.pdf","raw_type":"text"}],"best_oa_location":null,"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/7","score":0.6700000166893005,"display_name":"Affordable and clean energy"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":31,"referenced_works":["https://openalex.org/W26816478","https://openalex.org/W1579877966","https://openalex.org/W1608462934","https://openalex.org/W1676317136","https://openalex.org/W2097998671","https://openalex.org/W2100052226","https://openalex.org/W2104978738","https://openalex.org/W2115733720","https://openalex.org/W2119821739","https://openalex.org/W2120419212","https://openalex.org/W2120738995","https://openalex.org/W2122808326","https://openalex.org/W2122903255","https://openalex.org/W2124351082","https://openalex.org/W2126326837","https://openalex.org/W2137285073","https://openalex.org/W2147079026","https://openalex.org/W2154683974","https://openalex.org/W2154740476","https://openalex.org/W2157788863","https://openalex.org/W2160794449","https://openalex.org/W2161969291","https://openalex.org/W2162915993","https://openalex.org/W2165688580","https://openalex.org/W2171188998","https://openalex.org/W2914885528","https://openalex.org/W3097096317","https://openalex.org/W4239510810","https://openalex.org/W6601080436","https://openalex.org/W6675734358","https://openalex.org/W6679958704"],"related_works":["https://openalex.org/W2089892314","https://openalex.org/W1603091392","https://openalex.org/W4386075310","https://openalex.org/W2169565408","https://openalex.org/W2127229869","https://openalex.org/W3123056048","https://openalex.org/W2150638158","https://openalex.org/W2121506664","https://openalex.org/W2363184354","https://openalex.org/W2137862631"],"abstract_inverted_index":{"Straightforward":[0],"classification":[1,99],"using":[2,227],"kernelized":[3],"SVMs":[4,45],"requires":[5,214],"evaluating":[6],"the":[7,16,51,55,65,90,139,152,168,177,187,236],"kernel":[8,44],"for":[9,64],"a":[10,20,124,157,210,222],"test":[11],"vector":[12],"and":[13,85,131,213],"each":[14],"of":[15,22,50,57,89,92,114,127,172,243],"support":[17,58,93],"vectors.":[18],"For":[19],"class":[21],"kernels":[23,113],"we":[24,36,76,219],"show":[25,37,70,220],"that":[26,38,71,221],"one":[27,39],"can":[28,40,77],"do":[29],"this":[30],"much":[31],"more":[32],"efficiently.":[33],"In":[34,198],"particular":[35],"build":[41],"histogram":[42],"intersection":[43],"(IKSVMs)":[46],"with":[47,82,95,156,240],"runtime":[48,84],"complexity":[49],"classifier":[52,81,146],"logarithmic":[53],"in":[54,98],"number":[56,91],"vectors":[59],"as":[60],"opposed":[61],"to":[62,108,186,206],"linear":[63,169],"standard":[66,211],"approach.":[67],"We":[68,117],"further":[69],"by":[72],"precomputing":[73],"auxiliary":[74],"tables":[75],"construct":[78],"an":[79,143],"approximate":[80,144,202,228],"constant":[83],"space":[86],"requirements,":[87],"independent":[88],"vectors,":[94],"negligible":[96,241],"loss":[97,242],"accuracy":[100,185],"on":[101,123,134,148,191,231,235],"various":[102,135],"tasks.":[103],"This":[104],"approximation":[105],"also":[106,118],"applies":[107],"1":[109],"-":[110],"chi2and":[111],"other":[112],"similar":[115],"form.":[116],"introduce":[119],"novel":[120],"features":[121,150,234],"based":[122,147,230],"multi-level":[125],"histograms":[126],"oriented":[128],"edge":[129],"energy":[130],"present":[132],"experiments":[133,200],"detection":[136],"datasets.":[137],"On":[138,176],"INRIA":[140],"pedestrian":[141,180],"dataset":[142,181,239],"IKSVM":[145,182,203,229],"these":[149,199],"has":[151],"current":[153],"best":[154,188],"performance,":[155],"miss":[158],"rate":[159],"13%":[160],"lower":[161],"at":[162],"10-6False":[163],"Positive":[164],"Per":[165],"Window":[166],"than":[167,209],"SVM":[170],"detector":[171],"Dalal":[173],"&":[174],"Triggs.":[175],"Daimler":[178],"Chrysler":[179],"gives":[183],"comparable":[184],"results":[189],"(based":[190],"quadratic":[192],"SVM),":[193],"while":[194],"being":[195],"15times":[196],"faster.":[197],"our":[201],"is":[204,225],"up":[205],"2000times":[207],"faster":[208],"implementation":[212],"200times":[215],"less":[216],"memory.":[217],"Finally":[218],"50times":[223],"speedup":[224],"possible":[226],"spatial":[232],"pyramid":[233],"Caltech":[237],"101":[238],"accuracy.":[244]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2025,"cited_by_count":6},{"year":2024,"cited_by_count":9},{"year":2023,"cited_by_count":13},{"year":2022,"cited_by_count":25},{"year":2021,"cited_by_count":30},{"year":2020,"cited_by_count":24},{"year":2019,"cited_by_count":42},{"year":2018,"cited_by_count":56},{"year":2017,"cited_by_count":57},{"year":2016,"cited_by_count":68},{"year":2015,"cited_by_count":102},{"year":2014,"cited_by_count":115},{"year":2013,"cited_by_count":118},{"year":2012,"cited_by_count":97}],"updated_date":"2026-07-29T14:22:42.915294","created_date":"2025-10-10T00:00:00"}