{"id":"https://openalex.org/W3160112926","doi":"https://doi.org/10.1109/icpr48806.2021.9412596","title":"Weakly Supervised Learning through Rank-based Contextual Measures","display_name":"Weakly Supervised Learning through Rank-based Contextual Measures","publication_year":2021,"publication_date":"2021-01-10","ids":{"openalex":"https://openalex.org/W3160112926","doi":"https://doi.org/10.1109/icpr48806.2021.9412596","mag":"3160112926"},"language":"en","primary_location":{"id":"doi:10.1109/icpr48806.2021.9412596","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icpr48806.2021.9412596","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2020 25th International Conference on Pattern Recognition (ICPR)","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/A5004098367","display_name":"Jo\u00e3o Gabriel Camacho Presotto","orcid":"https://orcid.org/0000-0002-3643-9440"},"institutions":[{"id":"https://openalex.org/I879563668","display_name":"Universidade Estadual Paulista (Unesp)","ror":"https://ror.org/00987cb86","country_code":"BR","type":"education","lineage":["https://openalex.org/I879563668"]}],"countries":["BR"],"is_corresponding":false,"raw_author_name":"Joao Gabriel Camacho Presotto","raw_affiliation_strings":["UNESP - S\u00e3o Paulo State University, Rio Claro, SP, Brazil"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"UNESP - S\u00e3o Paulo State University, Rio Claro, SP, Brazil","institution_ids":["https://openalex.org/I879563668"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5039558288","display_name":"Lucas Pascotti Valem","orcid":"https://orcid.org/0000-0002-3833-9072"},"institutions":[{"id":"https://openalex.org/I879563668","display_name":"Universidade Estadual Paulista (Unesp)","ror":"https://ror.org/00987cb86","country_code":"BR","type":"education","lineage":["https://openalex.org/I879563668"]}],"countries":["BR"],"is_corresponding":false,"raw_author_name":"Lucas Pascotti Valem","raw_affiliation_strings":["UNESP - S\u00e3o Paulo State University, Rio Claro, SP, Brazil"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"UNESP - S\u00e3o Paulo State University, Rio Claro, SP, Brazil","institution_ids":["https://openalex.org/I879563668"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5013999268","display_name":"Nikolas Gomes de S\u00e1","orcid":null},"institutions":[{"id":"https://openalex.org/I879563668","display_name":"Universidade Estadual Paulista (Unesp)","ror":"https://ror.org/00987cb86","country_code":"BR","type":"education","lineage":["https://openalex.org/I879563668"]}],"countries":["BR"],"is_corresponding":false,"raw_author_name":"Nikolas Gomes de Sa","raw_affiliation_strings":["UNESP - S\u00e3o Paulo State University, Rio Claro, SP, Brazil"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"UNESP - S\u00e3o Paulo State University, Rio Claro, SP, Brazil","institution_ids":["https://openalex.org/I879563668"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5078511671","display_name":"Daniel Carlos Guimar\u00e3es Pedronette","orcid":"https://orcid.org/0000-0002-2867-4838"},"institutions":[{"id":"https://openalex.org/I879563668","display_name":"Universidade Estadual Paulista (Unesp)","ror":"https://ror.org/00987cb86","country_code":"BR","type":"education","lineage":["https://openalex.org/I879563668"]}],"countries":["BR"],"is_corresponding":false,"raw_author_name":"Daniel Carlos Guimaraes Pedronette","raw_affiliation_strings":["UNESP - S\u00e3o Paulo State University, Rio Claro, SP, Brazil"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"UNESP - S\u00e3o Paulo State University, Rio Claro, SP, Brazil","institution_ids":["https://openalex.org/I879563668"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5003275797","display_name":"Jo\u00e3o Paulo Papa","orcid":"https://orcid.org/0000-0002-6494-7514"},"institutions":[{"id":"https://openalex.org/I879563668","display_name":"Universidade Estadual Paulista (Unesp)","ror":"https://ror.org/00987cb86","country_code":"BR","type":"education","lineage":["https://openalex.org/I879563668"]}],"countries":["BR"],"is_corresponding":false,"raw_author_name":"Joao Paulo Papa","raw_affiliation_strings":["School of Sciences, UNESP - S\u00e3o Paulo State University, Bauru, SP, Brazil"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Sciences, UNESP - S\u00e3o Paulo State University, Bauru, SP, Brazil","institution_ids":["https://openalex.org/I879563668"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":5,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.3881,"has_fulltext":false,"cited_by_count":5,"citation_normalized_percentile":{"value":0.59715166,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":96},"biblio":{"volume":null,"issue":null,"first_page":"5752","last_page":"5759"},"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":0.9988999962806702,"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/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/T10824","display_name":"Image Retrieval and Classification Techniques","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"}},{"id":"https://openalex.org/T10689","display_name":"Remote-Sensing Image Classification","score":0.9932000041007996,"subfield":{"id":"https://openalex.org/subfields/2214","display_name":"Media Technology"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7971369028091431},{"id":"https://openalex.org/keywords/exploit","display_name":"Exploit","score":0.7042086124420166},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.6828088164329529},{"id":"https://openalex.org/keywords/labeled-data","display_name":"Labeled data","score":0.6768316626548767},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6380881071090698},{"id":"https://openalex.org/keywords/classifier","display_name":"Classifier (UML)","score":0.6244196891784668},{"id":"https://openalex.org/keywords/supervised-learning","display_name":"Supervised learning","score":0.6055673956871033},{"id":"https://openalex.org/keywords/semi-supervised-learning","display_name":"Semi-supervised learning","score":0.559429407119751},{"id":"https://openalex.org/keywords/rank","display_name":"Rank (graph theory)","score":0.5532606244087219},{"id":"https://openalex.org/keywords/learning-to-rank","display_name":"Learning to rank","score":0.4795534312725067},{"id":"https://openalex.org/keywords/correlation","display_name":"Correlation","score":0.4682266414165497},{"id":"https://openalex.org/keywords/training-set","display_name":"Training set","score":0.44918546080589294},{"id":"https://openalex.org/keywords/set","display_name":"Set (abstract data type)","score":0.4486784338951111},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.3704041838645935},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.3676546514034271},{"id":"https://openalex.org/keywords/ranking","display_name":"Ranking (information retrieval)","score":0.15486428141593933},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.15477868914604187},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.0859098732471466}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7971369028091431},{"id":"https://openalex.org/C165696696","wikidata":"https://www.wikidata.org/wiki/Q11287","display_name":"Exploit","level":2,"score":0.7042086124420166},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.6828088164329529},{"id":"https://openalex.org/C2776145971","wikidata":"https://www.wikidata.org/wiki/Q30673951","display_name":"Labeled data","level":2,"score":0.6768316626548767},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6380881071090698},{"id":"https://openalex.org/C95623464","wikidata":"https://www.wikidata.org/wiki/Q1096149","display_name":"Classifier (UML)","level":2,"score":0.6244196891784668},{"id":"https://openalex.org/C136389625","wikidata":"https://www.wikidata.org/wiki/Q334384","display_name":"Supervised learning","level":3,"score":0.6055673956871033},{"id":"https://openalex.org/C58973888","wikidata":"https://www.wikidata.org/wiki/Q1041418","display_name":"Semi-supervised learning","level":2,"score":0.559429407119751},{"id":"https://openalex.org/C164226766","wikidata":"https://www.wikidata.org/wiki/Q7293202","display_name":"Rank (graph theory)","level":2,"score":0.5532606244087219},{"id":"https://openalex.org/C86037889","wikidata":"https://www.wikidata.org/wiki/Q4330127","display_name":"Learning to rank","level":3,"score":0.4795534312725067},{"id":"https://openalex.org/C117220453","wikidata":"https://www.wikidata.org/wiki/Q5172842","display_name":"Correlation","level":2,"score":0.4682266414165497},{"id":"https://openalex.org/C51632099","wikidata":"https://www.wikidata.org/wiki/Q3985153","display_name":"Training set","level":2,"score":0.44918546080589294},{"id":"https://openalex.org/C177264268","wikidata":"https://www.wikidata.org/wiki/Q1514741","display_name":"Set (abstract data type)","level":2,"score":0.4486784338951111},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.3704041838645935},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.3676546514034271},{"id":"https://openalex.org/C189430467","wikidata":"https://www.wikidata.org/wiki/Q7293293","display_name":"Ranking (information retrieval)","level":2,"score":0.15486428141593933},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.15477868914604187},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.0859098732471466},{"id":"https://openalex.org/C2524010","wikidata":"https://www.wikidata.org/wiki/Q8087","display_name":"Geometry","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/C38652104","wikidata":"https://www.wikidata.org/wiki/Q3510521","display_name":"Computer security","level":1,"score":0.0},{"id":"https://openalex.org/C199360897","wikidata":"https://www.wikidata.org/wiki/Q9143","display_name":"Programming language","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/icpr48806.2021.9412596","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icpr48806.2021.9412596","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2020 25th International Conference on Pattern Recognition (ICPR)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G3711989506","display_name":null,"funder_award_id":"2017/00285-6","funder_id":"https://openalex.org/F4320322468","funder_display_name":"Petrobras"}],"funders":[{"id":"https://openalex.org/F4320322468","display_name":"Petrobras","ror":"https://ror.org/0235kyq22"}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":40,"referenced_works":["https://openalex.org/W1552231868","https://openalex.org/W1630959083","https://openalex.org/W1917380066","https://openalex.org/W2021581601","https://openalex.org/W2027752285","https://openalex.org/W2046589280","https://openalex.org/W2046712819","https://openalex.org/W2047822984","https://openalex.org/W2055423828","https://openalex.org/W2080955644","https://openalex.org/W2086320398","https://openalex.org/W2101234009","https://openalex.org/W2106401878","https://openalex.org/W2106404777","https://openalex.org/W2112796928","https://openalex.org/W2119821739","https://openalex.org/W2142992480","https://openalex.org/W2150856297","https://openalex.org/W2154455818","https://openalex.org/W2194775991","https://openalex.org/W2400131829","https://openalex.org/W2613503760","https://openalex.org/W2746791238","https://openalex.org/W2799712919","https://openalex.org/W2897076095","https://openalex.org/W2924719072","https://openalex.org/W2950816581","https://openalex.org/W2962965968","https://openalex.org/W2964317695","https://openalex.org/W2995314979","https://openalex.org/W2998269939","https://openalex.org/W3006528745","https://openalex.org/W4239510810","https://openalex.org/W4248916828","https://openalex.org/W4256046779","https://openalex.org/W6675354045","https://openalex.org/W6676049198","https://openalex.org/W6682494755","https://openalex.org/W6760886919","https://openalex.org/W6771518190"],"related_works":["https://openalex.org/W4312414840","https://openalex.org/W2794908468","https://openalex.org/W4206276646","https://openalex.org/W2943467239","https://openalex.org/W1571801203","https://openalex.org/W101422005","https://openalex.org/W192740413","https://openalex.org/W3004135598","https://openalex.org/W2952937263","https://openalex.org/W2168489430"],"abstract_inverted_index":{"Machine":[0],"learning":[1,14],"approaches":[2],"have":[3],"achieved":[4,162],"remarkable":[5],"advances":[6],"over":[7],"the":[8,35,43,56,80,103,111,176],"last":[9],"decades,":[10],"especially":[11],"in":[12,30,38,48,79,83,106,163],"supervised":[13,88,128,169],"tasks":[15],"such":[16],"as":[17,172],"classification.":[18,89],"Meanwhile,":[19],"multimedia":[20,39,133],"data":[21,40,47,58,82],"and":[22,42,59,101,143,155,168],"applications":[23],"experienced":[24],"an":[25,107],"explosive":[26],"growth,":[27],"becoming":[28],"ubiquitous":[29],"diverse":[31],"domains.":[32],"Due":[33],"to":[34,74,85,120],"huge":[36],"increase":[37],"collections":[41],"lack":[44],"of":[45,54,139,179],"labeled":[46,104,113,180],"several":[49,137],"scenarios,":[50],"creating":[51],"methods":[52],"capable":[53],"exploiting":[55],"unlabeled":[57,81],"operating":[60],"under":[61],"weakly":[62,87,127],"supervision":[63],"is":[64,115],"imperative.":[65],"In":[66],"this":[67],"work,":[68],"we":[69],"propose":[70],"a":[71,118],"rank-based":[72,93],"model":[73],"exploit":[75],"contextual":[76],"information":[77],"encoded":[78],"order":[84],"perform":[86],"We":[90],"employ":[91],"different":[92,156],"correlation":[94,141],"measures":[95,142],"for":[96],"identifying":[97],"strong":[98],"similarities":[99],"relationships":[100],"expanding":[102],"set":[105,114],"unsupervised":[108],"way.":[109],"Subsequently,":[110],"extended":[112],"used":[116],"by":[117],"classifier":[119],"achieve":[121],"better":[122],"accuracy":[123],"results.":[124],"The":[125],"proposed":[126],"approach":[129],"was":[130,148],"evaluated":[131],"on":[132,150],"classification":[134],"tasks,":[135],"considering":[136,175],"combinations":[138],"rank":[140],"classifiers.":[144],"An":[145],"experimental":[146],"evaluation":[147],"conducted":[149],"4":[151],"public":[152],"image":[153],"datasets":[154],"features.":[157],"Very":[158],"positive":[159],"gains":[160],"were":[161],"comparison":[164],"with":[165],"various":[166],"semi-supervised":[167],"classifiers":[170],"taken":[171],"baselines":[173],"when":[174],"same":[177],"amount":[178],"data.":[181]},"counts_by_year":[{"year":2025,"cited_by_count":1},{"year":2024,"cited_by_count":1},{"year":2023,"cited_by_count":1},{"year":2022,"cited_by_count":2}],"updated_date":"2026-06-11T09:08:48.828518","created_date":"2025-10-10T00:00:00"}
