{"id":"https://openalex.org/W2794354276","doi":"https://doi.org/10.1109/icip.2017.8297131","title":"Discriminative canonical correlation analysis network for image classification","display_name":"Discriminative canonical correlation analysis network for image classification","publication_year":2017,"publication_date":"2017-09-01","ids":{"openalex":"https://openalex.org/W2794354276","doi":"https://doi.org/10.1109/icip.2017.8297131","mag":"2794354276"},"language":"en","primary_location":{"id":"doi:10.1109/icip.2017.8297131","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icip.2017.8297131","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2017 IEEE International Conference on Image Processing (ICIP)","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/A5064373853","display_name":"Bernardo B. Gatto","orcid":"https://orcid.org/0000-0001-9639-8188"},"institutions":[{"id":"https://openalex.org/I62885914","display_name":"Universidade Federal do Amazonas","ror":"https://ror.org/02263ky35","country_code":"BR","type":"education","lineage":["https://openalex.org/I62885914"]}],"countries":["BR"],"is_corresponding":false,"raw_author_name":"Bernardo B. Gatto","raw_affiliation_strings":["Institute of Computing (ICOMP), Federal University of Amazonas, Brazil"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Institute of Computing (ICOMP), Federal University of Amazonas, Brazil","institution_ids":["https://openalex.org/I62885914"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5029548136","display_name":"Eulanda M. dos Santos","orcid":"https://orcid.org/0000-0002-5671-581X"},"institutions":[{"id":"https://openalex.org/I62885914","display_name":"Universidade Federal do Amazonas","ror":"https://ror.org/02263ky35","country_code":"BR","type":"education","lineage":["https://openalex.org/I62885914"]}],"countries":["BR"],"is_corresponding":false,"raw_author_name":"Eulanda M. dos Santos","raw_affiliation_strings":["Institute of Computing (ICOMP), Federal University of Amazonas, Brazil"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Institute of Computing (ICOMP), Federal University of Amazonas, Brazil","institution_ids":["https://openalex.org/I62885914"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I62885914"],"apc_list":null,"apc_paid":null,"fwci":0.398,"has_fulltext":false,"cited_by_count":14,"citation_normalized_percentile":{"value":0.74846048,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":98},"biblio":{"volume":"60","issue":null,"first_page":"4487","last_page":"4491"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10057","display_name":"Face and Expression Recognition","score":0.9998000264167786,"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/T10057","display_name":"Face and Expression Recognition","score":0.9998000264167786,"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/T10331","display_name":"Video Surveillance and Tracking Methods","score":0.9990000128746033,"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":0.9980000257492065,"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/discriminative-model","display_name":"Discriminative model","score":0.9362434148788452},{"id":"https://openalex.org/keywords/canonical-correlation","display_name":"Canonical correlation","score":0.7911581993103027},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7384622693061829},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.7300307154655457},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.6770274639129639},{"id":"https://openalex.org/keywords/linear-discriminant-analysis","display_name":"Linear discriminant analysis","score":0.6017809510231018},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.5755868554115295},{"id":"https://openalex.org/keywords/principal-component-analysis","display_name":"Principal component analysis","score":0.4527561366558075},{"id":"https://openalex.org/keywords/contextual-image-classification","display_name":"Contextual image classification","score":0.4355959892272949},{"id":"https://openalex.org/keywords/network-architecture","display_name":"Network architecture","score":0.4185592830181122},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.4085127115249634},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.2562103271484375}],"concepts":[{"id":"https://openalex.org/C97931131","wikidata":"https://www.wikidata.org/wiki/Q5282087","display_name":"Discriminative model","level":2,"score":0.9362434148788452},{"id":"https://openalex.org/C153874254","wikidata":"https://www.wikidata.org/wiki/Q115542","display_name":"Canonical correlation","level":2,"score":0.7911581993103027},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7384622693061829},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7300307154655457},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.6770274639129639},{"id":"https://openalex.org/C69738355","wikidata":"https://www.wikidata.org/wiki/Q1228929","display_name":"Linear discriminant analysis","level":2,"score":0.6017809510231018},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.5755868554115295},{"id":"https://openalex.org/C27438332","wikidata":"https://www.wikidata.org/wiki/Q2873","display_name":"Principal component analysis","level":2,"score":0.4527561366558075},{"id":"https://openalex.org/C75294576","wikidata":"https://www.wikidata.org/wiki/Q5165192","display_name":"Contextual image classification","level":3,"score":0.4355959892272949},{"id":"https://openalex.org/C193415008","wikidata":"https://www.wikidata.org/wiki/Q639681","display_name":"Network architecture","level":2,"score":0.4185592830181122},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.4085127115249634},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.2562103271484375},{"id":"https://openalex.org/C38652104","wikidata":"https://www.wikidata.org/wiki/Q3510521","display_name":"Computer security","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/icip.2017.8297131","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icip.2017.8297131","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2017 IEEE International Conference on Image Processing (ICIP)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/10","score":0.75,"display_name":"Reduced inequalities"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":37,"referenced_works":["https://openalex.org/W1487528910","https://openalex.org/W1550919879","https://openalex.org/W1616262590","https://openalex.org/W1782590233","https://openalex.org/W1865351371","https://openalex.org/W1959016151","https://openalex.org/W2022508996","https://openalex.org/W2085411191","https://openalex.org/W2144172034","https://openalex.org/W2150600350","https://openalex.org/W2151103935","https://openalex.org/W2157558673","https://openalex.org/W2161969291","https://openalex.org/W2163352848","https://openalex.org/W2163605009","https://openalex.org/W2293078015","https://openalex.org/W2312958246","https://openalex.org/W2335728318","https://openalex.org/W2403846478","https://openalex.org/W2561741492","https://openalex.org/W2563764042","https://openalex.org/W2570903723","https://openalex.org/W2575348539","https://openalex.org/W2585372182","https://openalex.org/W2618530766","https://openalex.org/W2963494009","https://openalex.org/W2963892472","https://openalex.org/W3102431071","https://openalex.org/W3118608800","https://openalex.org/W4293380554","https://openalex.org/W6681239517","https://openalex.org/W6684191040","https://openalex.org/W6703116779","https://openalex.org/W6730227458","https://openalex.org/W6730753511","https://openalex.org/W6743257778","https://openalex.org/W6743465096"],"related_works":["https://openalex.org/W4389116644","https://openalex.org/W2153315159","https://openalex.org/W2093195256","https://openalex.org/W2154562908","https://openalex.org/W2356150353","https://openalex.org/W2354323478","https://openalex.org/W2155796846","https://openalex.org/W2761785940","https://openalex.org/W2129933262","https://openalex.org/W2092806427"],"abstract_inverted_index":{"Recently,":[0],"convolutional":[1],"neural":[2],"network":[3,29,36,41,47,120,139],"has":[4],"attracted":[5],"an":[6],"increasing":[7],"amount":[8],"of":[9,21,72,82,101,106,154],"attention":[10],"in":[11,91],"machine":[12],"learning":[13,28],"and":[14,43,54,147],"computer":[15,94],"vision":[16,95],"areas,":[17],"improving":[18],"the":[19,104,107,138,152],"performance":[20],"several":[22],"related":[23],"applications.":[24,76],"Currently,":[25],"many":[26],"deep":[27],"architectures":[30],"such":[31],"as":[32],"principal":[33],"component":[34],"analysis":[35,40,46,130],"(PCANet),":[37],"linear":[38],"discriminant":[39],"(LDANet)":[42],"canonical":[44,118,128],"correlation":[45,119],"(CCANet)":[48],"have":[49,60],"been":[50],"proposed":[51],"for":[52],"object":[53],"face":[55],"classification.":[56],"These":[57],"architecture":[58],"solutions":[59,79],"demonstrated":[61],"high":[62,88,99],"efficiency,":[63],"with":[64],"a":[65,69,116],"simple":[66],"implementation,":[67],"providing":[68],"fast":[70],"prototyping":[71],"efficient":[73],"image":[74],"classification":[75],"However,":[77],"these":[78],"take":[80],"advantage":[81],"filters":[83,124,133],"that":[84,122,137],"may":[85],"not":[86],"extract":[87],"discriminative":[89,112,117,127,142,148],"features":[90],"more":[92,111,145],"complicated":[93],"problems":[96],"(e.g.":[97],"containing":[98],"degree":[100],"overlap":[102],"between":[103],"distributions":[105],"data).":[108],"To":[109],"generate":[110],"information,":[113],"we":[114],"introduce":[115],"(DCCNet),":[121],"employs":[123],"constructed":[125],"from":[126,134],"correlations":[129],"(DCC).":[131],"Learning":[132],"DCC":[135],"ensures":[136],"will":[140],"produce":[141],"features,":[143],"generating":[144],"representative":[146],"information.":[149],"We":[150],"demonstrate":[151],"applicability":[153],"DCCNet":[155],"through":[156],"experiments":[157],"on":[158],"four":[159],"datasets.":[160]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2025,"cited_by_count":1},{"year":2022,"cited_by_count":2},{"year":2021,"cited_by_count":5},{"year":2020,"cited_by_count":2},{"year":2019,"cited_by_count":2},{"year":2018,"cited_by_count":1}],"updated_date":"2026-07-29T14:22:42.915294","created_date":"2025-10-10T00:00:00"}