{"id":"https://openalex.org/W2980726482","doi":"https://doi.org/10.1145/3347317.3357242","title":"Recognizing Characters in Art History Using Deep Learning","display_name":"Recognizing Characters in Art History Using Deep Learning","publication_year":2019,"publication_date":"2019-10-15","ids":{"openalex":"https://openalex.org/W2980726482","doi":"https://doi.org/10.1145/3347317.3357242","mag":"2980726482"},"language":"en","primary_location":{"id":"doi:10.1145/3347317.3357242","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3347317.3357242","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 1st Workshop on Structuring and Understanding of Multimedia heritAge Contents","raw_type":"proceedings-article"},"type":"preprint","indexed_in":["arxiv","crossref"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://arxiv.org/pdf/2003.14171","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5007968883","display_name":"Prathmesh Madhu","orcid":"https://orcid.org/0000-0003-2707-415X"},"institutions":[{"id":"https://openalex.org/I181369854","display_name":"Friedrich-Alexander-Universit\u00e4t Erlangen-N\u00fcrnberg","ror":"https://ror.org/00f7hpc57","country_code":"DE","type":"education","lineage":["https://openalex.org/I181369854"]}],"countries":["DE"],"is_corresponding":true,"raw_author_name":"Prathmesh Madhu","raw_affiliation_strings":["Friedrich-Alexander-Universit\u00e4t Erlangen-N\u00fcrnberg, Erlangen, Germany"],"affiliations":[{"raw_affiliation_string":"Friedrich-Alexander-Universit\u00e4t Erlangen-N\u00fcrnberg, Erlangen, Germany","institution_ids":["https://openalex.org/I181369854"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5001765180","display_name":"Ronak Kosti","orcid":"https://orcid.org/0000-0003-2453-7876"},"institutions":[{"id":"https://openalex.org/I181369854","display_name":"Friedrich-Alexander-Universit\u00e4t Erlangen-N\u00fcrnberg","ror":"https://ror.org/00f7hpc57","country_code":"DE","type":"education","lineage":["https://openalex.org/I181369854"]}],"countries":["DE"],"is_corresponding":false,"raw_author_name":"Ronak Kosti","raw_affiliation_strings":["Friedrich-Alexander-Universit\u00e4t Erlangen-N\u00fcrnberg, Erlangen, Germany"],"affiliations":[{"raw_affiliation_string":"Friedrich-Alexander-Universit\u00e4t Erlangen-N\u00fcrnberg, Erlangen, Germany","institution_ids":["https://openalex.org/I181369854"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5020048243","display_name":"Lara M\u00fchrenberg","orcid":"https://orcid.org/0000-0003-0988-2604"},"institutions":[{"id":"https://openalex.org/I181369854","display_name":"Friedrich-Alexander-Universit\u00e4t Erlangen-N\u00fcrnberg","ror":"https://ror.org/00f7hpc57","country_code":"DE","type":"education","lineage":["https://openalex.org/I181369854"]}],"countries":["DE"],"is_corresponding":false,"raw_author_name":"Lara M\u00fchrenberg","raw_affiliation_strings":["Friedrich-Alexander-Universit\u00e4t Erlangen-N\u00fcrnberg, Erlangen, Germany"],"affiliations":[{"raw_affiliation_string":"Friedrich-Alexander-Universit\u00e4t Erlangen-N\u00fcrnberg, Erlangen, Germany","institution_ids":["https://openalex.org/I181369854"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5081337679","display_name":"Peter Bell","orcid":"https://orcid.org/0000-0003-4415-7408"},"institutions":[{"id":"https://openalex.org/I181369854","display_name":"Friedrich-Alexander-Universit\u00e4t Erlangen-N\u00fcrnberg","ror":"https://ror.org/00f7hpc57","country_code":"DE","type":"education","lineage":["https://openalex.org/I181369854"]}],"countries":["DE"],"is_corresponding":false,"raw_author_name":"Peter Bell","raw_affiliation_strings":["Friedrich-Alexander-Universit\u00e4t Erlangen-N\u00fcrnberg, Erlangen, Germany"],"affiliations":[{"raw_affiliation_string":"Friedrich-Alexander-Universit\u00e4t Erlangen-N\u00fcrnberg, Erlangen, Germany","institution_ids":["https://openalex.org/I181369854"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101619735","display_name":"Andreas Maier","orcid":"https://orcid.org/0000-0002-9550-5284"},"institutions":[{"id":"https://openalex.org/I181369854","display_name":"Friedrich-Alexander-Universit\u00e4t Erlangen-N\u00fcrnberg","ror":"https://ror.org/00f7hpc57","country_code":"DE","type":"education","lineage":["https://openalex.org/I181369854"]}],"countries":["DE"],"is_corresponding":false,"raw_author_name":"Andreas Maier","raw_affiliation_strings":["Friedrich-Alexander-Universit\u00e4t Erlangen-N\u00fcrnberg, Erlangen, Germany"],"affiliations":[{"raw_affiliation_string":"Friedrich-Alexander-Universit\u00e4t Erlangen-N\u00fcrnberg, Erlangen, Germany","institution_ids":["https://openalex.org/I181369854"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5087093169","display_name":"Vincent Christlein","orcid":"https://orcid.org/0000-0003-0455-3799"},"institutions":[{"id":"https://openalex.org/I181369854","display_name":"Friedrich-Alexander-Universit\u00e4t Erlangen-N\u00fcrnberg","ror":"https://ror.org/00f7hpc57","country_code":"DE","type":"education","lineage":["https://openalex.org/I181369854"]}],"countries":["DE"],"is_corresponding":false,"raw_author_name":"Vincent Christlein","raw_affiliation_strings":["Friedrich-Alexander-Universit\u00e4t Erlangen-N\u00fcrnberg, Erlangen, Germany"],"affiliations":[{"raw_affiliation_string":"Friedrich-Alexander-Universit\u00e4t Erlangen-N\u00fcrnberg, Erlangen, Germany","institution_ids":["https://openalex.org/I181369854"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":6,"corresponding_author_ids":["https://openalex.org/A5007968883"],"corresponding_institution_ids":["https://openalex.org/I181369854"],"apc_list":null,"apc_paid":null,"fwci":1.9799,"has_fulltext":false,"cited_by_count":24,"citation_normalized_percentile":{"value":0.86222205,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":94,"max":99},"biblio":{"volume":null,"issue":null,"first_page":"15","last_page":"22"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12650","display_name":"Aesthetic Perception and Analysis","score":0.9980000257492065,"subfield":{"id":"https://openalex.org/subfields/2805","display_name":"Cognitive Neuroscience"},"field":{"id":"https://openalex.org/fields/28","display_name":"Neuroscience"},"domain":{"id":"https://openalex.org/domains/1","display_name":"Life Sciences"}},"topics":[{"id":"https://openalex.org/T12650","display_name":"Aesthetic Perception and Analysis","score":0.9980000257492065,"subfield":{"id":"https://openalex.org/subfields/2805","display_name":"Cognitive Neuroscience"},"field":{"id":"https://openalex.org/fields/28","display_name":"Neuroscience"},"domain":{"id":"https://openalex.org/domains/1","display_name":"Life Sciences"}},{"id":"https://openalex.org/T10775","display_name":"Generative Adversarial Networks and Image Synthesis","score":0.9977999925613403,"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":0.9868000149726868,"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":0.7378541827201843},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.6869304180145264},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6580651998519897},{"id":"https://openalex.org/keywords/ambiguity","display_name":"Ambiguity","score":0.5820032358169556},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.5486980676651001},{"id":"https://openalex.org/keywords/focus","display_name":"Focus (optics)","score":0.5094276070594788},{"id":"https://openalex.org/keywords/representation","display_name":"Representation (politics)","score":0.4916709065437317},{"id":"https://openalex.org/keywords/field","display_name":"Field (mathematics)","score":0.4501606225967407},{"id":"https://openalex.org/keywords/classifier","display_name":"Classifier (UML)","score":0.4388507306575775},{"id":"https://openalex.org/keywords/context","display_name":"Context (archaeology)","score":0.4357575476169586},{"id":"https://openalex.org/keywords/character","display_name":"Character (mathematics)","score":0.42127978801727295},{"id":"https://openalex.org/keywords/feature-learning","display_name":"Feature learning","score":0.4162994921207428},{"id":"https://openalex.org/keywords/iconography","display_name":"Iconography","score":0.4160096049308777},{"id":"https://openalex.org/keywords/natural-language-processing","display_name":"Natural language processing","score":0.4095599949359894},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.34754425287246704},{"id":"https://openalex.org/keywords/art","display_name":"Art","score":0.17060014605522156},{"id":"https://openalex.org/keywords/visual-arts","display_name":"Visual arts","score":0.13697844743728638},{"id":"https://openalex.org/keywords/politics","display_name":"Politics","score":0.07661402225494385}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7378541827201843},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.6869304180145264},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6580651998519897},{"id":"https://openalex.org/C2780522230","wikidata":"https://www.wikidata.org/wiki/Q1140419","display_name":"Ambiguity","level":2,"score":0.5820032358169556},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.5486980676651001},{"id":"https://openalex.org/C192209626","wikidata":"https://www.wikidata.org/wiki/Q190909","display_name":"Focus (optics)","level":2,"score":0.5094276070594788},{"id":"https://openalex.org/C2776359362","wikidata":"https://www.wikidata.org/wiki/Q2145286","display_name":"Representation (politics)","level":3,"score":0.4916709065437317},{"id":"https://openalex.org/C9652623","wikidata":"https://www.wikidata.org/wiki/Q190109","display_name":"Field (mathematics)","level":2,"score":0.4501606225967407},{"id":"https://openalex.org/C95623464","wikidata":"https://www.wikidata.org/wiki/Q1096149","display_name":"Classifier (UML)","level":2,"score":0.4388507306575775},{"id":"https://openalex.org/C2779343474","wikidata":"https://www.wikidata.org/wiki/Q3109175","display_name":"Context (archaeology)","level":2,"score":0.4357575476169586},{"id":"https://openalex.org/C2780861071","wikidata":"https://www.wikidata.org/wiki/Q1062934","display_name":"Character (mathematics)","level":2,"score":0.42127978801727295},{"id":"https://openalex.org/C59404180","wikidata":"https://www.wikidata.org/wiki/Q17013334","display_name":"Feature learning","level":2,"score":0.4162994921207428},{"id":"https://openalex.org/C501303744","wikidata":"https://www.wikidata.org/wiki/Q208145","display_name":"Iconography","level":2,"score":0.4160096049308777},{"id":"https://openalex.org/C204321447","wikidata":"https://www.wikidata.org/wiki/Q30642","display_name":"Natural language processing","level":1,"score":0.4095599949359894},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.34754425287246704},{"id":"https://openalex.org/C142362112","wikidata":"https://www.wikidata.org/wiki/Q735","display_name":"Art","level":0,"score":0.17060014605522156},{"id":"https://openalex.org/C153349607","wikidata":"https://www.wikidata.org/wiki/Q36649","display_name":"Visual arts","level":1,"score":0.13697844743728638},{"id":"https://openalex.org/C94625758","wikidata":"https://www.wikidata.org/wiki/Q7163","display_name":"Politics","level":2,"score":0.07661402225494385},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.0},{"id":"https://openalex.org/C120665830","wikidata":"https://www.wikidata.org/wiki/Q14620","display_name":"Optics","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/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0},{"id":"https://openalex.org/C199360897","wikidata":"https://www.wikidata.org/wiki/Q9143","display_name":"Programming language","level":1,"score":0.0},{"id":"https://openalex.org/C17744445","wikidata":"https://www.wikidata.org/wiki/Q36442","display_name":"Political science","level":0,"score":0.0},{"id":"https://openalex.org/C151730666","wikidata":"https://www.wikidata.org/wiki/Q7205","display_name":"Paleontology","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/C199539241","wikidata":"https://www.wikidata.org/wiki/Q7748","display_name":"Law","level":1,"score":0.0},{"id":"https://openalex.org/C86803240","wikidata":"https://www.wikidata.org/wiki/Q420","display_name":"Biology","level":0,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1145/3347317.3357242","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3347317.3357242","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 1st Workshop on Structuring and Understanding of Multimedia heritAge Contents","raw_type":"proceedings-article"},{"id":"pmh:oai:arXiv.org:2003.14171","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2003.14171","pdf_url":"https://arxiv.org/pdf/2003.14171","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"acceptedVersion","is_accepted":true,"is_published":false,"raw_source_name":null,"raw_type":"text"}],"best_oa_location":{"id":"pmh:oai:arXiv.org:2003.14171","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2003.14171","pdf_url":"https://arxiv.org/pdf/2003.14171","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"acceptedVersion","is_accepted":true,"is_published":false,"raw_source_name":null,"raw_type":"text"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":47,"referenced_works":["https://openalex.org/W300217873","https://openalex.org/W616033002","https://openalex.org/W1499403091","https://openalex.org/W1504108458","https://openalex.org/W1537451167","https://openalex.org/W1567178044","https://openalex.org/W1728842521","https://openalex.org/W1782590233","https://openalex.org/W1834627138","https://openalex.org/W1849277567","https://openalex.org/W1861492603","https://openalex.org/W1899185266","https://openalex.org/W1965804146","https://openalex.org/W1968361834","https://openalex.org/W2007178811","https://openalex.org/W2011615896","https://openalex.org/W2020944503","https://openalex.org/W2101392314","https://openalex.org/W2107034620","https://openalex.org/W2115546586","https://openalex.org/W2121027212","https://openalex.org/W2123687672","https://openalex.org/W2124386111","https://openalex.org/W2163605009","https://openalex.org/W2165698076","https://openalex.org/W2194775991","https://openalex.org/W2239239723","https://openalex.org/W2295107390","https://openalex.org/W2321727850","https://openalex.org/W2325939864","https://openalex.org/W2507296351","https://openalex.org/W2510725918","https://openalex.org/W2597507805","https://openalex.org/W2601564443","https://openalex.org/W2603777577","https://openalex.org/W2740354675","https://openalex.org/W2765225858","https://openalex.org/W2796347433","https://openalex.org/W2901825513","https://openalex.org/W2962895364","https://openalex.org/W2963377935","https://openalex.org/W2963857746","https://openalex.org/W2964086488","https://openalex.org/W2970084480","https://openalex.org/W2981630388","https://openalex.org/W3101577715","https://openalex.org/W4240153047"],"related_works":["https://openalex.org/W606512284","https://openalex.org/W2124970778","https://openalex.org/W3012939930","https://openalex.org/W2479837280","https://openalex.org/W2301776820","https://openalex.org/W2560381779","https://openalex.org/W1829312565","https://openalex.org/W3112542712","https://openalex.org/W4367313226","https://openalex.org/W2808289749"],"abstract_inverted_index":{"In":[0,62],"the":[1,16,21,35,39,42,46,68,72,78,83,89,92,107,127,131,150,158,174,195,210,218,227,232],"field":[2],"of":[3,7,27,48,70,80,82,91,109,123,133,160,176],"Art":[4,75],"History,":[5],"images":[6],"artworks":[8,29,51,100],"and":[9,24,96,101,105,126,162,171,201,207],"their":[10,118,139],"contexts":[11],"are":[12,164],"core":[13],"to":[14,37,137,144,148,166],"understanding":[15],"underlying":[17],"semantic":[18],"information.":[19],"However,":[20],"highly":[22],"complex":[23],"sophisticated":[25],"representation":[26,90,132],"these":[28],"makes":[30],"it":[31],"difficult,":[32],"even":[33],"for":[34,221,231],"experts,":[36],"analyze":[38,145,226],"scene.":[40],"From":[41,77],"computer":[43],"vision":[44],"perspective,":[45],"task":[47],"analyzing":[49],"such":[50],"can":[52],"be":[53],"divided":[54],"into":[55],"sub-problems":[56],"by":[57],"taking":[58],"a":[59,111,181],"bottom-up":[60],"approach.":[61],"this":[63,124],"paper,":[64],"we":[65,87,225],"focus":[66],"on":[67,114,157,203,213],"problem":[69],"recognizing":[71,222],"characters":[73],"in":[74,130,146,194],"History.":[76],"iconography":[79],"Annunciation":[81],"Lord":[84],"(Figure":[85],"1),":[86],"consider":[88,138],"main":[93],"protagonists,":[94],"Mary":[95,161],"Gabriel,":[97,134],"across":[98],"different":[99,205],"styles.":[102],"We":[103,179,198],"investigate":[104],"present":[106,199],"findings":[108],"training":[110],"character":[112,177],"classifier":[113],"features":[115,170],"extracted":[116],"from":[117],"face":[119],"images.":[120],"The":[121],"limitations":[122],"method,":[125],"inherent":[128],"ambiguity":[129],"motivated":[135],"us":[136],"bodies":[140,159],"(a":[141],"bigger":[142],"context)":[143],"order":[147],"recognize":[149],"characters.":[151],"Convolutional":[152],"Neural":[153],"Networks":[154],"(CNN)":[155],"trained":[156,212],"Gabriel":[163],"able":[165],"learn":[167],"person":[168],"related":[169,215],"ultimately":[172],"improve":[173],"performance":[175,220],"recognition.":[178],"introduce":[180],"new":[182],"technique":[183],"that":[184,209],"generates":[185],"more":[186],"data":[187,193,216],"with":[188],"similar":[189,196],"styles,":[190],"effectively":[191],"creating":[192],"domain.":[197],"experiments":[200],"analysis":[202],"three":[204],"models":[206],"show":[208],"model":[211],"domain":[214],"gives":[217],"best":[219],"character.":[223],"Additionally,":[224],"localized":[228],"image":[229],"regions":[230],"network":[233],"predictions.":[234]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2024,"cited_by_count":5},{"year":2023,"cited_by_count":2},{"year":2022,"cited_by_count":5},{"year":2021,"cited_by_count":7},{"year":2020,"cited_by_count":4}],"updated_date":"2026-03-20T23:20:44.827607","created_date":"2025-10-10T00:00:00"}