{"id":"https://openalex.org/W2922306415","doi":"https://doi.org/10.5220/0007347504380445","title":"Deep Neural Networks with Intersection over Union Loss for Binary Image Segmentation","display_name":"Deep Neural Networks with Intersection over Union Loss for Binary Image Segmentation","publication_year":2019,"publication_date":"2019-01-01","ids":{"openalex":"https://openalex.org/W2922306415","doi":"https://doi.org/10.5220/0007347504380445","mag":"2922306415"},"language":"en","primary_location":{"id":"doi:10.5220/0007347504380445","is_oa":true,"landing_page_url":"https://doi.org/10.5220/0007347504380445","pdf_url":null,"source":null,"license":"cc-by-nc-nd","license_id":"https://openalex.org/licenses/cc-by-nc-nd","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 8th International Conference on Pattern Recognition Applications and Methods","raw_type":"proceedings-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://doi.org/10.5220/0007347504380445","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5065089873","display_name":"Floris van Beers","orcid":null},"institutions":[{"id":"https://openalex.org/I169381384","display_name":"University of Groningen","ror":"https://ror.org/012p63287","country_code":"NL","type":"education","lineage":["https://openalex.org/I169381384"]}],"countries":["NL"],"is_corresponding":false,"raw_author_name":"Floris van Beers","raw_affiliation_strings":["Bernoulli Institute, Department of Artificial Intelligence, University of Groningen, Nijenborgh 9, Groningen and The Netherlands, --- Select a Country ---"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Bernoulli Institute, Department of Artificial Intelligence, University of Groningen, Nijenborgh 9, Groningen and The Netherlands, --- Select a Country ---","institution_ids":["https://openalex.org/I169381384"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5062095109","display_name":"Arvid Lindstr\u00f6m","orcid":null},"institutions":[{"id":"https://openalex.org/I169381384","display_name":"University of Groningen","ror":"https://ror.org/012p63287","country_code":"NL","type":"education","lineage":["https://openalex.org/I169381384"]}],"countries":["NL"],"is_corresponding":false,"raw_author_name":"Arvid Lindstr\u00f6m","raw_affiliation_strings":["Bernoulli Institute, Department of Artificial Intelligence, University of Groningen, Nijenborgh 9, Groningen and The Netherlands, --- Select a Country ---"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Bernoulli Institute, Department of Artificial Intelligence, University of Groningen, Nijenborgh 9, Groningen and The Netherlands, --- Select a Country ---","institution_ids":["https://openalex.org/I169381384"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5014716134","display_name":"Emmanuel Okafor","orcid":"https://orcid.org/0000-0001-6929-6880"},"institutions":[{"id":"https://openalex.org/I169381384","display_name":"University of Groningen","ror":"https://ror.org/012p63287","country_code":"NL","type":"education","lineage":["https://openalex.org/I169381384"]}],"countries":["NL"],"is_corresponding":false,"raw_author_name":"Emmanuel Okafor","raw_affiliation_strings":["Bernoulli Institute, Department of Artificial Intelligence, University of Groningen, Nijenborgh 9, Groningen and The Netherlands, --- Select a Country ---"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Bernoulli Institute, Department of Artificial Intelligence, University of Groningen, Nijenborgh 9, Groningen and The Netherlands, --- Select a Country ---","institution_ids":["https://openalex.org/I169381384"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5060596453","display_name":"Marco Wiering","orcid":"https://orcid.org/0000-0003-4331-7537"},"institutions":[{"id":"https://openalex.org/I169381384","display_name":"University of Groningen","ror":"https://ror.org/012p63287","country_code":"NL","type":"education","lineage":["https://openalex.org/I169381384"]}],"countries":["NL"],"is_corresponding":false,"raw_author_name":"Marco Wiering","raw_affiliation_strings":["Bernoulli Institute, Department of Artificial Intelligence, University of Groningen, Nijenborgh 9, Groningen and The Netherlands, --- Select a Country ---"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Bernoulli Institute, Department of Artificial Intelligence, University of Groningen, Nijenborgh 9, Groningen and The Netherlands, --- Select a Country ---","institution_ids":["https://openalex.org/I169381384"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I169381384"],"apc_list":null,"apc_paid":null,"fwci":3.8709,"has_fulltext":true,"cited_by_count":118,"citation_normalized_percentile":{"value":0.948858,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":90,"max":100},"biblio":{"volume":null,"issue":null,"first_page":"438","last_page":"445"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10036","display_name":"Advanced Neural Network Applications","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/T10036","display_name":"Advanced Neural Network Applications","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/T11448","display_name":"Face recognition and analysis","score":0.9993000030517578,"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/T12111","display_name":"Industrial Vision Systems and Defect Detection","score":0.9980999827384949,"subfield":{"id":"https://openalex.org/subfields/2209","display_name":"Industrial and Manufacturing Engineering"},"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/artificial-intelligence","display_name":"Artificial intelligence","score":0.7530171275138855},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6997014284133911},{"id":"https://openalex.org/keywords/segmentation","display_name":"Segmentation","score":0.6524479389190674},{"id":"https://openalex.org/keywords/cross-entropy","display_name":"Cross entropy","score":0.6482273936271667},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.6369538307189941},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.6339771747589111},{"id":"https://openalex.org/keywords/jaccard-index","display_name":"Jaccard index","score":0.6326727867126465},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.6175887584686279},{"id":"https://openalex.org/keywords/image-segmentation","display_name":"Image segmentation","score":0.5460588335990906},{"id":"https://openalex.org/keywords/entropy","display_name":"Entropy (arrow of time)","score":0.5322515964508057},{"id":"https://openalex.org/keywords/intersection","display_name":"Intersection (aeronautics)","score":0.5240923762321472},{"id":"https://openalex.org/keywords/binary-number","display_name":"Binary number","score":0.5160024166107178},{"id":"https://openalex.org/keywords/differentiable-function","display_name":"Differentiable function","score":0.5153193473815918},{"id":"https://openalex.org/keywords/binary-classification","display_name":"Binary classification","score":0.5013604164123535},{"id":"https://openalex.org/keywords/deep-neural-networks","display_name":"Deep neural networks","score":0.4515690505504608},{"id":"https://openalex.org/keywords/pixel","display_name":"Pixel","score":0.4499288499355316},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.19080865383148193},{"id":"https://openalex.org/keywords/support-vector-machine","display_name":"Support vector machine","score":0.0898594856262207},{"id":"https://openalex.org/keywords/cartography","display_name":"Cartography","score":0.07283872365951538}],"concepts":[{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7530171275138855},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6997014284133911},{"id":"https://openalex.org/C89600930","wikidata":"https://www.wikidata.org/wiki/Q1423946","display_name":"Segmentation","level":2,"score":0.6524479389190674},{"id":"https://openalex.org/C167981619","wikidata":"https://www.wikidata.org/wiki/Q1685498","display_name":"Cross entropy","level":3,"score":0.6482273936271667},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.6369538307189941},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.6339771747589111},{"id":"https://openalex.org/C203519979","wikidata":"https://www.wikidata.org/wiki/Q865360","display_name":"Jaccard index","level":3,"score":0.6326727867126465},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.6175887584686279},{"id":"https://openalex.org/C124504099","wikidata":"https://www.wikidata.org/wiki/Q56933","display_name":"Image segmentation","level":3,"score":0.5460588335990906},{"id":"https://openalex.org/C106301342","wikidata":"https://www.wikidata.org/wiki/Q4117933","display_name":"Entropy (arrow of time)","level":2,"score":0.5322515964508057},{"id":"https://openalex.org/C64543145","wikidata":"https://www.wikidata.org/wiki/Q162942","display_name":"Intersection (aeronautics)","level":2,"score":0.5240923762321472},{"id":"https://openalex.org/C48372109","wikidata":"https://www.wikidata.org/wiki/Q3913","display_name":"Binary number","level":2,"score":0.5160024166107178},{"id":"https://openalex.org/C202615002","wikidata":"https://www.wikidata.org/wiki/Q783507","display_name":"Differentiable function","level":2,"score":0.5153193473815918},{"id":"https://openalex.org/C66905080","wikidata":"https://www.wikidata.org/wiki/Q17005494","display_name":"Binary classification","level":3,"score":0.5013604164123535},{"id":"https://openalex.org/C2984842247","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep neural networks","level":3,"score":0.4515690505504608},{"id":"https://openalex.org/C160633673","wikidata":"https://www.wikidata.org/wiki/Q355198","display_name":"Pixel","level":2,"score":0.4499288499355316},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.19080865383148193},{"id":"https://openalex.org/C12267149","wikidata":"https://www.wikidata.org/wiki/Q282453","display_name":"Support vector machine","level":2,"score":0.0898594856262207},{"id":"https://openalex.org/C58640448","wikidata":"https://www.wikidata.org/wiki/Q42515","display_name":"Cartography","level":1,"score":0.07283872365951538},{"id":"https://openalex.org/C134306372","wikidata":"https://www.wikidata.org/wiki/Q7754","display_name":"Mathematical analysis","level":1,"score":0.0},{"id":"https://openalex.org/C94375191","wikidata":"https://www.wikidata.org/wiki/Q11205","display_name":"Arithmetic","level":1,"score":0.0},{"id":"https://openalex.org/C62520636","wikidata":"https://www.wikidata.org/wiki/Q944","display_name":"Quantum mechanics","level":1,"score":0.0},{"id":"https://openalex.org/C205649164","wikidata":"https://www.wikidata.org/wiki/Q1071","display_name":"Geography","level":0,"score":0.0},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0}],"mesh":[],"locations_count":3,"locations":[{"id":"doi:10.5220/0007347504380445","is_oa":true,"landing_page_url":"https://doi.org/10.5220/0007347504380445","pdf_url":null,"source":null,"license":"cc-by-nc-nd","license_id":"https://openalex.org/licenses/cc-by-nc-nd","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 8th International Conference on Pattern Recognition Applications and Methods","raw_type":"proceedings-article"},{"id":"pmh:oai:pure.rug.nl:openaire/7a721acb-fd6b-4e13-b0d1-4431598a169e","is_oa":false,"landing_page_url":"https://research.rug.nl/en/publications/7a721acb-fd6b-4e13-b0d1-4431598a169e","pdf_url":null,"source":{"id":"https://openalex.org/S4306400420","display_name":"University of Groningen research database (University of Groningen / Centre for Information Technology)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I169381384","host_organization_name":"University of Groningen","host_organization_lineage":["https://openalex.org/I169381384"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"van Beers, F, Lindstr\u00f6m, A, Okafor, E & Wiering, M 2019, Deep Neural Networks with Intersection over Union Loss for Binary Image Segmentation. in Proceedings of the 8th International Conference on Pattern Recognition Applications and Methods . vol. 1 ICPRAM, SciTePress, Prague, pp. 438-445. https://doi.org/10.5220/0007347504380445","raw_type":"info:eu-repo/semantics/conferenceObject"},{"id":"pmh:oai:pure.rug.nl:publications/7a721acb-fd6b-4e13-b0d1-4431598a169e","is_oa":true,"landing_page_url":"https://hdl.handle.net/11370/7a721acb-fd6b-4e13-b0d1-4431598a169e","pdf_url":"https://pure.rug.nl/ws/files/87088047/ICPRAM_2019_35.pdf","source":{"id":"https://openalex.org/S4306400420","display_name":"University of Groningen research database (University of Groningen / Centre for Information Technology)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I169381384","host_organization_name":"University of Groningen","host_organization_lineage":["https://openalex.org/I169381384"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by-nc-nd","license_id":"https://openalex.org/licenses/cc-by-nc-nd","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"van Beers, F, Lindstr\u00f6m, A, Okafor, E & Wiering, M 2019, Deep Neural Networks with Intersection over Union Loss for Binary Image Segmentation. in Proceedings of the 8th International Conference on Pattern Recognition Applications and Methods . vol. 1 ICPRAM, SciTePress, Prague, pp. 438-445. https://doi.org/10.5220/0007347504380445","raw_type":"info:eu-repo/semantics/conferenceObject"}],"best_oa_location":{"id":"doi:10.5220/0007347504380445","is_oa":true,"landing_page_url":"https://doi.org/10.5220/0007347504380445","pdf_url":null,"source":null,"license":"cc-by-nc-nd","license_id":"https://openalex.org/licenses/cc-by-nc-nd","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 8th International Conference on Pattern Recognition Applications and Methods","raw_type":"proceedings-article"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":18,"referenced_works":["https://openalex.org/W1686810756","https://openalex.org/W1745334888","https://openalex.org/W1796263212","https://openalex.org/W1903029394","https://openalex.org/W2062385571","https://openalex.org/W2108598243","https://openalex.org/W2114096665","https://openalex.org/W2167090521","https://openalex.org/W2194775991","https://openalex.org/W2205506889","https://openalex.org/W2395611524","https://openalex.org/W2560311620","https://openalex.org/W2562637781","https://openalex.org/W2594265094","https://openalex.org/W2607363228","https://openalex.org/W2617682326","https://openalex.org/W2963881378","https://openalex.org/W2963954267"],"related_works":["https://openalex.org/W4319993456","https://openalex.org/W3037097571","https://openalex.org/W2803187616","https://openalex.org/W2963759070","https://openalex.org/W3080935557","https://openalex.org/W4388821232","https://openalex.org/W2607836359","https://openalex.org/W4308016144","https://openalex.org/W4289766480","https://openalex.org/W3134134808"],"abstract_inverted_index":{"In":[0],"semantic":[1],"segmentation":[2,57],"tasks":[3],"the":[4],"Jaccard":[5],"Index,":[6],"or":[7],"Intersection":[8],"over":[9],"Union":[10],"(IoU),":[11],"is":[12,22,47,92],"often":[13],"used":[14,48,78],"as":[15,79],"a":[16,56,80],"measure":[17,21],"of":[18,59,67],"success.While":[19],"this":[20,43,70],"more":[23],"representative":[24],"than":[25],"per-pixel":[26],"accuracy,":[27],"state-of-the-art":[28],"deep":[29,50,97],"neural":[30,51,98],"networks":[31,52],"are":[32,53],"still":[33],"trained":[34,54],"on":[35,101,112,123],"accuracy":[36],"by":[37,62,94],"using":[38,69],"Binary":[39,88,125],"Cross":[40,89,126],"Entropy":[41,90,127],"loss.In":[42],"research,":[44],"an":[45,65],"alternative":[46],"where":[49],"for":[55,117],"task":[58],"human":[60],"faces":[61],"optimizing":[63],"directly":[64,111],"approximation":[66],"IoU.When":[68],"approximation,":[71],"IoU":[72,85,113],"becomes":[73],"differentiable":[74],"and":[75,87,104],"can":[76],"be":[77],"loss":[81,86,91],"function.The":[82],"comparison":[83],"between":[84],"made":[93],"testing":[95],"two":[96],"network":[99],"models":[100,119],"multiple":[102],"datasets":[103],"data":[105],"splits.The":[106],"results":[107],"show":[108],"that":[109],"training":[110,122],"significantly":[114],"increases":[115],"performance":[116],"both":[118],"compared":[120],"to":[121],"conventional":[124],"loss.":[128]},"counts_by_year":[{"year":2026,"cited_by_count":6},{"year":2025,"cited_by_count":20},{"year":2024,"cited_by_count":23},{"year":2023,"cited_by_count":30},{"year":2022,"cited_by_count":19},{"year":2021,"cited_by_count":13},{"year":2020,"cited_by_count":6},{"year":2019,"cited_by_count":1}],"updated_date":"2026-07-10T07:45:09.275182","created_date":"2025-10-10T00:00:00"}