{"id":"https://openalex.org/W2120027739","doi":"https://doi.org/10.1109/cvpr.2012.6248047","title":"Efficient object detection using cascades of nearest convex model classifiers","display_name":"Efficient object detection using cascades of nearest convex model classifiers","publication_year":2012,"publication_date":"2012-06-01","ids":{"openalex":"https://openalex.org/W2120027739","doi":"https://doi.org/10.1109/cvpr.2012.6248047","mag":"2120027739"},"language":"en","primary_location":{"id":"doi:10.1109/cvpr.2012.6248047","is_oa":false,"landing_page_url":"https://doi.org/10.1109/cvpr.2012.6248047","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2012 IEEE Conference on Computer Vision and Pattern Recognition","raw_type":"proceedings-article"},"type":"preprint","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/A5072358879","display_name":"Hakan \u00c7ev\u0131kalp","orcid":"https://orcid.org/0000-0002-1708-8817"},"institutions":[{"id":"https://openalex.org/I51209816","display_name":"Eski\u015fehir Osmangazi University","ror":"https://ror.org/01dzjez04","country_code":"TR","type":"education","lineage":["https://openalex.org/I51209816"]}],"countries":["TR"],"is_corresponding":true,"raw_author_name":"H. Cevikalp","raw_affiliation_strings":["Eskisehir Osmangazi University, Eskisehir, Turkey","Eskisehir Osmangazi University, Meselik Kampusu, 26480, Eskisehir, Turkey"],"affiliations":[{"raw_affiliation_string":"Eskisehir Osmangazi University, Eskisehir, Turkey","institution_ids":["https://openalex.org/I51209816"]},{"raw_affiliation_string":"Eskisehir Osmangazi University, Meselik Kampusu, 26480, Eskisehir, Turkey","institution_ids":["https://openalex.org/I51209816"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5062363302","display_name":"Bill Triggs","orcid":"https://orcid.org/0000-0003-4116-6296"},"institutions":[{"id":"https://openalex.org/I4210149092","display_name":"Laboratoire Jean Kuntzmann","ror":"https://ror.org/04ett5b41","country_code":"FR","type":"facility","lineage":["https://openalex.org/I106785703","https://openalex.org/I1294671590","https://openalex.org/I1326498283","https://openalex.org/I4210149092","https://openalex.org/I899635006","https://openalex.org/I899635006"]}],"countries":["FR"],"is_corresponding":false,"raw_author_name":"B. Triggs","raw_affiliation_strings":["Laboratoire Jean Kuntzmann, Grenoble, France","Laboratoire Jean Kuntzmann, B.P. 53, 38041 Grenoble Cedex 9, France"],"affiliations":[{"raw_affiliation_string":"Laboratoire Jean Kuntzmann, Grenoble, France","institution_ids":["https://openalex.org/I4210149092"]},{"raw_affiliation_string":"Laboratoire Jean Kuntzmann, B.P. 53, 38041 Grenoble Cedex 9, France","institution_ids":["https://openalex.org/I4210149092"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5072358879"],"corresponding_institution_ids":["https://openalex.org/I51209816"],"apc_list":null,"apc_paid":null,"fwci":4.7186,"has_fulltext":false,"cited_by_count":45,"citation_normalized_percentile":{"value":0.95686197,"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":"3138","last_page":"3145"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10331","display_name":"Video Surveillance and Tracking Methods","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"}},"topics":[{"id":"https://openalex.org/T10331","display_name":"Video Surveillance and Tracking Methods","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/T11448","display_name":"Face recognition and analysis","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"}},{"id":"https://openalex.org/T11307","display_name":"Domain Adaptation and Few-Shot Learning","score":0.9976999759674072,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.7943789958953857},{"id":"https://openalex.org/keywords/hypersphere","display_name":"Hypersphere","score":0.733230471611023},{"id":"https://openalex.org/keywords/object-detection","display_name":"Object detection","score":0.6752811074256897},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.6505056619644165},{"id":"https://openalex.org/keywords/hyperplane","display_name":"Hyperplane","score":0.6464815735816956},{"id":"https://openalex.org/keywords/support-vector-machine","display_name":"Support vector machine","score":0.6408358216285706},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6347759366035461},{"id":"https://openalex.org/keywords/boosting","display_name":"Boosting (machine learning)","score":0.6314824819564819},{"id":"https://openalex.org/keywords/sliding-window-protocol","display_name":"Sliding window protocol","score":0.6120030879974365},{"id":"https://openalex.org/keywords/classifier","display_name":"Classifier (UML)","score":0.6049367189407349},{"id":"https://openalex.org/keywords/cascade","display_name":"Cascade","score":0.5568380355834961},{"id":"https://openalex.org/keywords/viola\u2013jones-object-detection-framework","display_name":"Viola\u2013Jones object detection framework","score":0.5171898603439331},{"id":"https://openalex.org/keywords/affine-transformation","display_name":"Affine transformation","score":0.47809669375419617},{"id":"https://openalex.org/keywords/contextual-image-classification","display_name":"Contextual image classification","score":0.4388169050216675},{"id":"https://openalex.org/keywords/cascading-classifiers","display_name":"Cascading classifiers","score":0.429551362991333},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.42382386326789856},{"id":"https://openalex.org/keywords/cognitive-neuroscience-of-visual-object-recognition","display_name":"Cognitive neuroscience of visual object recognition","score":0.41208139061927795},{"id":"https://openalex.org/keywords/face-detection","display_name":"Face detection","score":0.4009268879890442},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.2993893623352051},{"id":"https://openalex.org/keywords/facial-recognition-system","display_name":"Facial recognition system","score":0.2935771346092224},{"id":"https://openalex.org/keywords/feature-extraction","display_name":"Feature extraction","score":0.29303669929504395},{"id":"https://openalex.org/keywords/random-subspace-method","display_name":"Random subspace method","score":0.21166831254959106},{"id":"https://openalex.org/keywords/window","display_name":"Window (computing)","score":0.16797500848770142},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.16738620400428772}],"concepts":[{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7943789958953857},{"id":"https://openalex.org/C2776562905","wikidata":"https://www.wikidata.org/wiki/Q306610","display_name":"Hypersphere","level":2,"score":0.733230471611023},{"id":"https://openalex.org/C2776151529","wikidata":"https://www.wikidata.org/wiki/Q3045304","display_name":"Object detection","level":3,"score":0.6752811074256897},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.6505056619644165},{"id":"https://openalex.org/C68693459","wikidata":"https://www.wikidata.org/wiki/Q657586","display_name":"Hyperplane","level":2,"score":0.6464815735816956},{"id":"https://openalex.org/C12267149","wikidata":"https://www.wikidata.org/wiki/Q282453","display_name":"Support vector machine","level":2,"score":0.6408358216285706},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6347759366035461},{"id":"https://openalex.org/C46686674","wikidata":"https://www.wikidata.org/wiki/Q466303","display_name":"Boosting (machine learning)","level":2,"score":0.6314824819564819},{"id":"https://openalex.org/C102392041","wikidata":"https://www.wikidata.org/wiki/Q592860","display_name":"Sliding window protocol","level":3,"score":0.6120030879974365},{"id":"https://openalex.org/C95623464","wikidata":"https://www.wikidata.org/wiki/Q1096149","display_name":"Classifier (UML)","level":2,"score":0.6049367189407349},{"id":"https://openalex.org/C34146451","wikidata":"https://www.wikidata.org/wiki/Q5048094","display_name":"Cascade","level":2,"score":0.5568380355834961},{"id":"https://openalex.org/C182521987","wikidata":"https://www.wikidata.org/wiki/Q2493877","display_name":"Viola\u2013Jones object detection framework","level":5,"score":0.5171898603439331},{"id":"https://openalex.org/C92757383","wikidata":"https://www.wikidata.org/wiki/Q382497","display_name":"Affine transformation","level":2,"score":0.47809669375419617},{"id":"https://openalex.org/C75294576","wikidata":"https://www.wikidata.org/wiki/Q5165192","display_name":"Contextual image classification","level":3,"score":0.4388169050216675},{"id":"https://openalex.org/C40651066","wikidata":"https://www.wikidata.org/wiki/Q5048220","display_name":"Cascading classifiers","level":4,"score":0.429551362991333},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.42382386326789856},{"id":"https://openalex.org/C64876066","wikidata":"https://www.wikidata.org/wiki/Q5141226","display_name":"Cognitive neuroscience of visual object recognition","level":3,"score":0.41208139061927795},{"id":"https://openalex.org/C4641261","wikidata":"https://www.wikidata.org/wiki/Q11681085","display_name":"Face detection","level":4,"score":0.4009268879890442},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.2993893623352051},{"id":"https://openalex.org/C31510193","wikidata":"https://www.wikidata.org/wiki/Q1192553","display_name":"Facial recognition system","level":3,"score":0.2935771346092224},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.29303669929504395},{"id":"https://openalex.org/C106135958","wikidata":"https://www.wikidata.org/wiki/Q7291993","display_name":"Random subspace method","level":3,"score":0.21166831254959106},{"id":"https://openalex.org/C2778751112","wikidata":"https://www.wikidata.org/wiki/Q835016","display_name":"Window (computing)","level":2,"score":0.16797500848770142},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.16738620400428772},{"id":"https://openalex.org/C185592680","wikidata":"https://www.wikidata.org/wiki/Q2329","display_name":"Chemistry","level":0,"score":0.0},{"id":"https://openalex.org/C43617362","wikidata":"https://www.wikidata.org/wiki/Q170050","display_name":"Chromatography","level":1,"score":0.0},{"id":"https://openalex.org/C202444582","wikidata":"https://www.wikidata.org/wiki/Q837863","display_name":"Pure mathematics","level":1,"score":0.0},{"id":"https://openalex.org/C2524010","wikidata":"https://www.wikidata.org/wiki/Q8087","display_name":"Geometry","level":1,"score":0.0},{"id":"https://openalex.org/C111919701","wikidata":"https://www.wikidata.org/wiki/Q9135","display_name":"Operating system","level":1,"score":0.0}],"mesh":[],"locations_count":3,"locations":[{"id":"doi:10.1109/cvpr.2012.6248047","is_oa":false,"landing_page_url":"https://doi.org/10.1109/cvpr.2012.6248047","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2012 IEEE Conference on Computer Vision and Pattern Recognition","raw_type":"proceedings-article"},{"id":"pmh:oai:CiteSeerX.psu:10.1.1.378.7544","is_oa":false,"landing_page_url":"http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.378.7544","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"http://www-ljk.imag.fr/Publications/Basilic/com.lmc.publi.PUBLI_Inproceedings@13730f58c78_1669a2e/cevikalp-cvpr12.pdf","raw_type":"text"},{"id":"pmh:oai:HAL:hal-00851342v1","is_oa":false,"landing_page_url":"https://hal.science/hal-00851342","pdf_url":null,"source":{"id":"https://openalex.org/S4306402512","display_name":"HAL (Le Centre pour la Communication Scientifique Directe)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I1294671590","host_organization_name":"Centre National de la Recherche Scientifique","host_organization_lineage":["https://openalex.org/I1294671590"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"CVPR 2012 - IEEE Conference on Computer Vision and Pattern Recognition, Jun 2012, Providence, United States. pp.3138-3145, ⟨10.1109/CVPR.2012.6248047⟩","raw_type":"Conference papers"}],"best_oa_location":null,"sustainable_development_goals":[{"display_name":"Reduced inequalities","id":"https://metadata.un.org/sdg/10","score":0.75}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":31,"referenced_works":["https://openalex.org/W1512098439","https://openalex.org/W1596466429","https://openalex.org/W1608462934","https://openalex.org/W1677409904","https://openalex.org/W1782590233","https://openalex.org/W1970088130","https://openalex.org/W1999268518","https://openalex.org/W2031489346","https://openalex.org/W2057175746","https://openalex.org/W2084660905","https://openalex.org/W2099179843","https://openalex.org/W2119605622","https://openalex.org/W2120419212","https://openalex.org/W2120907774","https://openalex.org/W2122044381","https://openalex.org/W2124722975","https://openalex.org/W2131081720","https://openalex.org/W2132425109","https://openalex.org/W2151103935","https://openalex.org/W2161969291","https://openalex.org/W2163808566","https://openalex.org/W2168356304","https://openalex.org/W2217896605","https://openalex.org/W2534457893","https://openalex.org/W2538008885","https://openalex.org/W2548197316","https://openalex.org/W3097096317","https://openalex.org/W6630527977","https://openalex.org/W6635666696","https://openalex.org/W6674677340","https://openalex.org/W6678330678"],"related_works":["https://openalex.org/W2385299555","https://openalex.org/W1975620028","https://openalex.org/W2382071382","https://openalex.org/W3175564083","https://openalex.org/W2004392437","https://openalex.org/W2982358351","https://openalex.org/W3135341440","https://openalex.org/W3157611285","https://openalex.org/W2366871377","https://openalex.org/W4385636413"],"abstract_inverted_index":{"An":[0],"object":[1,11,41],"detector":[2],"must":[3],"detect":[4],"and":[5,52,119,141,144,165,170],"localize":[6],"each":[7],"instance":[8,42],"of":[9,13,31,63,71,89,109,128,132],"the":[10,16,27,34,61,87,90,106,110,126,160,163,174,180,191],"class":[12,92],"interest":[14],"in":[15,60,101,105,125,162,186],"image.":[17],"Many":[18],"recent":[19],"detectors":[20],"adopt":[21],"a":[22,39,95,129],"sliding":[23],"window":[24,36],"approach,":[25],"reducing":[26],"problem":[28],"to":[29,66,98,190],"one":[30],"deciding":[32],"whether":[33],"detection":[35,168],"currently":[37],"contains":[38],"valid":[40],"or":[43],"background.":[44],"Machine":[45],"learning":[46],"based":[47],"discriminants":[48,100],"such":[49,102],"as":[50],"SVM":[51],"boosting":[53],"are":[54,94],"typically":[55],"used":[56],"for":[57],"this,":[58],"often":[59],"form":[62,127],"classifier":[64,187],"cascades":[65],"allow":[67,116],"more":[68],"rapid":[69],"rejection":[70],"easy":[72],"negatives.":[73],"We":[74,122,150],"argue":[75],"that":[76,82,152],"\u201cone":[77],"class\u201d":[78],"methods":[79,154],"-":[80,93],"ones":[81],"focus":[83],"mainly":[84],"on":[85,159,173],"modelling":[86],"range":[88],"positive":[91],"useful":[96],"alternative":[97],"binary":[99],"applications,":[103],"particularly":[104],"early":[107],"stages":[108],"cascade":[111,131],"where":[112],"one-class":[113,135,181],"approaches":[114],"may":[115],"simpler":[117],"classifiers":[118,143],"faster":[120],"rejection.":[121],"implement":[123],"this":[124],"short":[130],"efficient":[133],"nearest-convex-model":[134],"classifiers,":[136],"starting":[137],"with":[138,146],"linear":[139],"distance-to-affine-hyperplane":[140],"interior-of-hypersphere":[142],"finishing":[145],"kernelized":[147],"hypersphere":[148],"classifiers.":[149],"show":[151],"our":[153],"have":[155],"very":[156],"competitive":[157],"performance":[158,172],"Faces":[161],"Wild":[164],"ESOGU":[166],"face":[167],"datasets":[169],"state-of-the-art":[171],"INRIA":[175],"Person":[176],"dataset.":[177],"As":[178],"predicted,":[179],"formulations":[182],"provide":[183],"significant":[184],"reductions":[185],"complexity":[188],"relative":[189],"corresponding":[192],"two-class":[193],"ones.":[194]},"counts_by_year":[{"year":2025,"cited_by_count":1},{"year":2023,"cited_by_count":1},{"year":2022,"cited_by_count":1},{"year":2020,"cited_by_count":5},{"year":2019,"cited_by_count":5},{"year":2018,"cited_by_count":3},{"year":2017,"cited_by_count":8},{"year":2016,"cited_by_count":4},{"year":2015,"cited_by_count":4},{"year":2014,"cited_by_count":5},{"year":2013,"cited_by_count":7},{"year":2012,"cited_by_count":1}],"updated_date":"2026-04-05T17:49:38.594831","created_date":"2025-10-10T00:00:00"}