{"id":"https://openalex.org/W1915943666","doi":"https://doi.org/10.1109/cvpr.2015.7298924","title":"Exemplar SVMs as visual feature encoders","display_name":"Exemplar SVMs as visual feature encoders","publication_year":2015,"publication_date":"2015-06-01","ids":{"openalex":"https://openalex.org/W1915943666","doi":"https://doi.org/10.1109/cvpr.2015.7298924","mag":"1915943666"},"language":"en","primary_location":{"id":"doi:10.1109/cvpr.2015.7298924","is_oa":false,"landing_page_url":"https://doi.org/10.1109/cvpr.2015.7298924","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)","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/A5104090766","display_name":"Joaquin Zepeda","orcid":null},"institutions":[{"id":"https://openalex.org/I2929663463","display_name":"Technicolor (Germany)","ror":"https://ror.org/00besvm65","country_code":"DE","type":"company","lineage":["https://openalex.org/I2929663463","https://openalex.org/I4210121266"]},{"id":"https://openalex.org/I4210121266","display_name":"Technicolor (France)","ror":"https://ror.org/02ya5n776","country_code":"FR","type":"company","lineage":["https://openalex.org/I4210121266"]}],"countries":["DE","FR"],"is_corresponding":true,"raw_author_name":"Joaquin Zepeda","raw_affiliation_strings":["Technicolor","Technicolor, France"],"affiliations":[{"raw_affiliation_string":"Technicolor","institution_ids":["https://openalex.org/I2929663463"]},{"raw_affiliation_string":"Technicolor, France","institution_ids":["https://openalex.org/I4210121266"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5076170578","display_name":"Patrick P\u00e9rez","orcid":"https://orcid.org/0000-0002-8124-1206"},"institutions":[{"id":"https://openalex.org/I4210121266","display_name":"Technicolor (France)","ror":"https://ror.org/02ya5n776","country_code":"FR","type":"company","lineage":["https://openalex.org/I4210121266"]},{"id":"https://openalex.org/I2929663463","display_name":"Technicolor (Germany)","ror":"https://ror.org/00besvm65","country_code":"DE","type":"company","lineage":["https://openalex.org/I2929663463","https://openalex.org/I4210121266"]}],"countries":["DE","FR"],"is_corresponding":false,"raw_author_name":"Patrick Perez","raw_affiliation_strings":["Technicolor","Technicolor, France"],"affiliations":[{"raw_affiliation_string":"Technicolor","institution_ids":["https://openalex.org/I2929663463"]},{"raw_affiliation_string":"Technicolor, France","institution_ids":["https://openalex.org/I4210121266"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5104090766"],"corresponding_institution_ids":["https://openalex.org/I2929663463","https://openalex.org/I4210121266"],"apc_list":null,"apc_paid":null,"fwci":4.8667,"has_fulltext":false,"cited_by_count":32,"citation_normalized_percentile":{"value":0.96732724,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":90,"max":99},"biblio":{"volume":"30","issue":null,"first_page":"3052","last_page":"3060"},"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":0.9998999834060669,"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":0.9998999834060669,"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/T10824","display_name":"Image Retrieval and Classification Techniques","score":0.9994000196456909,"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/T11714","display_name":"Multimodal Machine Learning Applications","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"}}],"keywords":[{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7087069749832153},{"id":"https://openalex.org/keywords/support-vector-machine","display_name":"Support vector machine","score":0.6994196176528931},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6986089944839478},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.6791812181472778},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.6541500687599182},{"id":"https://openalex.org/keywords/encoder","display_name":"Encoder","score":0.6133826375007629},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.4409591555595398},{"id":"https://openalex.org/keywords/feature-extraction","display_name":"Feature extraction","score":0.4315037131309509}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7087069749832153},{"id":"https://openalex.org/C12267149","wikidata":"https://www.wikidata.org/wiki/Q282453","display_name":"Support vector machine","level":2,"score":0.6994196176528931},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6986089944839478},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.6791812181472778},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.6541500687599182},{"id":"https://openalex.org/C118505674","wikidata":"https://www.wikidata.org/wiki/Q42586063","display_name":"Encoder","level":2,"score":0.6133826375007629},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.4409591555595398},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.4315037131309509},{"id":"https://openalex.org/C138885662","wikidata":"https://www.wikidata.org/wiki/Q5891","display_name":"Philosophy","level":0,"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/C111919701","wikidata":"https://www.wikidata.org/wiki/Q9135","display_name":"Operating system","level":1,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1109/cvpr.2015.7298924","is_oa":false,"landing_page_url":"https://doi.org/10.1109/cvpr.2015.7298924","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)","raw_type":"proceedings-article"},{"id":"pmh:oai:CiteSeerX.psu:10.1.1.845.8918","is_oa":false,"landing_page_url":"http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.845.8918","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"http://www.cv-foundation.org/openaccess/content_cvpr_2015/papers/Zepeda_Exemplar_SVMs_as_2015_CVPR_paper.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":49,"referenced_works":["https://openalex.org/W1486469731","https://openalex.org/W1499991161","https://openalex.org/W1513762125","https://openalex.org/W1524680991","https://openalex.org/W1590510366","https://openalex.org/W1606858007","https://openalex.org/W1881649694","https://openalex.org/W1955857676","https://openalex.org/W1976921161","https://openalex.org/W1979931042","https://openalex.org/W1980911747","https://openalex.org/W1984309565","https://openalex.org/W1984553375","https://openalex.org/W1989684337","https://openalex.org/W2005385547","https://openalex.org/W2012592962","https://openalex.org/W2035652042","https://openalex.org/W2044284589","https://openalex.org/W2062118960","https://openalex.org/W2078846315","https://openalex.org/W2088866137","https://openalex.org/W2089888558","https://openalex.org/W2105516263","https://openalex.org/W2109235804","https://openalex.org/W2125993116","https://openalex.org/W2131846894","https://openalex.org/W2141362318","https://openalex.org/W2147238549","https://openalex.org/W2151103935","https://openalex.org/W2155893237","https://openalex.org/W2161381512","https://openalex.org/W2161969291","https://openalex.org/W2163605009","https://openalex.org/W2168356304","https://openalex.org/W2179352600","https://openalex.org/W2396945109","https://openalex.org/W4256379134","https://openalex.org/W4376522650","https://openalex.org/W6629803217","https://openalex.org/W6631498818","https://openalex.org/W6635258101","https://openalex.org/W6636412649","https://openalex.org/W6639435539","https://openalex.org/W6641017118","https://openalex.org/W6646421020","https://openalex.org/W6651928215","https://openalex.org/W6676338569","https://openalex.org/W6684191040","https://openalex.org/W6712818873"],"related_works":["https://openalex.org/W2601157893","https://openalex.org/W2373006798","https://openalex.org/W2131735617","https://openalex.org/W2056912418","https://openalex.org/W2033213769","https://openalex.org/W4312376745","https://openalex.org/W2136016640","https://openalex.org/W2049538278","https://openalex.org/W2886173746","https://openalex.org/W2074736680"],"abstract_inverted_index":{"In":[0],"this":[1,166],"work,":[2],"we":[3,159],"investigate":[4],"the":[5,41,44,51,55,70,73,79,97,107,123],"use":[6],"of":[7,22,43,54,72,93,109,125,165],"exemplar":[8,56],"SVMs":[9,11],"(linear":[10],"trained":[12],"with":[13],"one":[14],"positive":[15],"example":[16],"only":[17],"and":[18,129],"a":[19,91,104,162],"vast":[20],"collection":[21],"negative":[23],"examples)":[24],"as":[25,120,122,145,147],"encoders":[26],"that":[27,102,171],"turn":[28],"generic":[29,98],"image":[30,65,112,118,132],"features":[31,94,144,148],"into":[32,78],"new,":[33],"task-tailored":[34],"features.":[35],"The":[36],"proposed":[37,84],"feature":[38,80],"encoding":[39,81,168],"leverages":[40],"ability":[42],"exemplar-SVM":[45],"(E-SVM)":[46],"classifier":[47],"to":[48,75,90],"extract,":[49],"from":[50,150],"original":[52],"representation":[53],"image,":[57],"what":[58],"is":[59],"unique":[60],"about":[61],"it.":[62],"While":[63],"existing":[64],"description":[66],"pipelines":[67],"rely":[68],"on":[69,116],"intuition":[71],"designer":[74],"encode":[76],"uniqueness":[77],"process,":[82],"our":[83],"approach":[85],"does":[86],"it":[87],"explicitly":[88],"relative":[89],"\u201cuniverse\u201d":[92],"represented":[95],"by":[96],"negatives.":[99],"We":[100,135],"show":[101],"such":[103],"post-processing":[105],"enhances":[106],"performance":[108,124,174],"state-of-the":[110],"art":[111],"retrieval":[113],"methods":[114],"based":[115],"aggregated":[117],"features,":[119,141],"well":[121,146],"nearest":[126],"class":[127],"mean":[128],"K-nearest":[130],"neighbor":[131],"classification":[133],"methods.":[134],"establish":[136],"these":[137],"advantages":[138],"for":[139],"several":[140],"including":[142],"\u201ctraditional\u201d":[143],"derived":[149],"deep":[151],"convolutional":[152],"neural":[153],"nets.":[154],"As":[155],"an":[156],"additional":[157],"contribution,":[158],"also":[160],"propose":[161],"recursive":[163],"extension":[164],"E-SVM":[167],"scheme":[169],"(RE-SVM)":[170],"provides":[172],"further":[173],"gains.":[175]},"counts_by_year":[{"year":2022,"cited_by_count":1},{"year":2019,"cited_by_count":5},{"year":2018,"cited_by_count":6},{"year":2017,"cited_by_count":10},{"year":2016,"cited_by_count":9},{"year":2015,"cited_by_count":1}],"updated_date":"2026-04-05T17:49:38.594831","created_date":"2025-10-10T00:00:00"}
