{"id":"https://openalex.org/W4205371873","doi":"https://doi.org/10.1109/access.2021.3136647","title":"A Reinforced Active Learning Algorithm for Semantic Segmentation in Complex Imaging","display_name":"A Reinforced Active Learning Algorithm for Semantic Segmentation in Complex Imaging","publication_year":2021,"publication_date":"2021-01-01","ids":{"openalex":"https://openalex.org/W4205371873","doi":"https://doi.org/10.1109/access.2021.3136647"},"language":"en","primary_location":{"id":"doi:10.1109/access.2021.3136647","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2021.3136647","pdf_url":"https://ieeexplore.ieee.org/ielx7/6287639/6514899/09656121.pdf","source":{"id":"https://openalex.org/S2485537415","display_name":"IEEE Access","issn_l":"2169-3536","issn":["2169-3536"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Access","raw_type":"journal-article"},"type":"article","indexed_in":["crossref","doaj"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://ieeexplore.ieee.org/ielx7/6287639/6514899/09656121.pdf","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5065259115","display_name":"Usman Ahmad Usmani","orcid":"https://orcid.org/0000-0003-0341-8288"},"institutions":[{"id":"https://openalex.org/I203899302","display_name":"Universiti Teknologi Petronas","ror":"https://ror.org/048g2sh07","country_code":"MY","type":"education","lineage":["https://openalex.org/I203899302"]}],"countries":["MY"],"is_corresponding":false,"raw_author_name":"Usman Ahmad Usmani","raw_affiliation_strings":["Universiti Teknologi Petronas, UTP, Seri Iskandar, 32610, Perak, Malaysia"],"raw_orcid":"https://orcid.org/0000-0003-0341-8288","affiliations":[{"raw_affiliation_string":"Universiti Teknologi Petronas, UTP, Seri Iskandar, 32610, Perak, Malaysia","institution_ids":["https://openalex.org/I203899302"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5036511259","display_name":"Junzo Watada","orcid":"https://orcid.org/0000-0002-3322-2086"},"institutions":[{"id":"https://openalex.org/I150744194","display_name":"Waseda University","ror":"https://ror.org/00ntfnx83","country_code":"JP","type":"education","lineage":["https://openalex.org/I150744194"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"Junzo Watada","raw_affiliation_strings":["Waseda University, Japan, 82610, Japan"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Waseda University, Japan, 82610, Japan","institution_ids":["https://openalex.org/I150744194"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5077789570","display_name":"Jafreezal Jaafar","orcid":"https://orcid.org/0000-0002-8850-6203"},"institutions":[{"id":"https://openalex.org/I203899302","display_name":"Universiti Teknologi Petronas","ror":"https://ror.org/048g2sh07","country_code":"MY","type":"education","lineage":["https://openalex.org/I203899302"]}],"countries":["MY"],"is_corresponding":false,"raw_author_name":"Jafreezal Jaafar","raw_affiliation_strings":["Universiti Teknologi Petronas, UTP, Seri Iskandar, 32610, Perak, Malaysia"],"raw_orcid":"https://orcid.org/0000-0002-8850-6203","affiliations":[{"raw_affiliation_string":"Universiti Teknologi Petronas, UTP, Seri Iskandar, 32610, Perak, Malaysia","institution_ids":["https://openalex.org/I203899302"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5011080351","display_name":"Izzatdin Abdul Aziz","orcid":"https://orcid.org/0000-0003-2654-4463"},"institutions":[{"id":"https://openalex.org/I203899302","display_name":"Universiti Teknologi Petronas","ror":"https://ror.org/048g2sh07","country_code":"MY","type":"education","lineage":["https://openalex.org/I203899302"]}],"countries":["MY"],"is_corresponding":false,"raw_author_name":"Izzatdin Abdul Aziz","raw_affiliation_strings":["Universiti Teknologi Petronas, UTP, Seri Iskandar, 32610, Perak, Malaysia"],"raw_orcid":"https://orcid.org/0000-0003-2654-4463","affiliations":[{"raw_affiliation_string":"Universiti Teknologi Petronas, UTP, Seri Iskandar, 32610, Perak, Malaysia","institution_ids":["https://openalex.org/I203899302"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5056195038","display_name":"Arunava Roy","orcid":"https://orcid.org/0000-0003-3523-1960"},"institutions":[{"id":"https://openalex.org/I11662577","display_name":"Monash University Malaysia","ror":"https://ror.org/00yncr324","country_code":"MY","type":"education","lineage":["https://openalex.org/I11662577"]}],"countries":["MY"],"is_corresponding":false,"raw_author_name":"Arunava Roy","raw_affiliation_strings":["Monash University, Bandar Sunway, Subang Jaya, 47500, Selangor, Malaysia"],"raw_orcid":"https://orcid.org/0000-0003-3523-1960","affiliations":[{"raw_affiliation_string":"Monash University, Bandar Sunway, Subang Jaya, 47500, Selangor, Malaysia","institution_ids":["https://openalex.org/I11662577"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":5,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":{"value":1850,"currency":"USD","value_usd":1850},"apc_paid":{"value":1850,"currency":"USD","value_usd":1850},"fwci":2.1346,"has_fulltext":true,"cited_by_count":31,"citation_normalized_percentile":{"value":0.89509847,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":99},"biblio":{"volume":"9","issue":null,"first_page":"168415","last_page":"168432"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10036","display_name":"Advanced Neural Network Applications","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/T10036","display_name":"Advanced Neural Network Applications","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/T11307","display_name":"Domain Adaptation and Few-Shot Learning","score":0.9994999766349792,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"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/T12072","display_name":"Machine Learning and Algorithms","score":0.9990000128746033,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"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":0.8427099585533142},{"id":"https://openalex.org/keywords/segmentation","display_name":"Segmentation","score":0.7548494935035706},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.7426499724388123},{"id":"https://openalex.org/keywords/task","display_name":"Task (project management)","score":0.5877143144607544},{"id":"https://openalex.org/keywords/reinforcement-learning","display_name":"Reinforcement learning","score":0.5857031941413879},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.5247205495834351},{"id":"https://openalex.org/keywords/image-segmentation","display_name":"Image segmentation","score":0.506742537021637},{"id":"https://openalex.org/keywords/pixel","display_name":"Pixel","score":0.505779504776001},{"id":"https://openalex.org/keywords/object","display_name":"Object (grammar)","score":0.5050762295722961},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.5010616779327393},{"id":"https://openalex.org/keywords/class","display_name":"Class (philosophy)","score":0.48368293046951294},{"id":"https://openalex.org/keywords/rgb-color-model","display_name":"RGB color model","score":0.4650396406650543},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.4112653434276581}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8427099585533142},{"id":"https://openalex.org/C89600930","wikidata":"https://www.wikidata.org/wiki/Q1423946","display_name":"Segmentation","level":2,"score":0.7548494935035706},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7426499724388123},{"id":"https://openalex.org/C2780451532","wikidata":"https://www.wikidata.org/wiki/Q759676","display_name":"Task (project management)","level":2,"score":0.5877143144607544},{"id":"https://openalex.org/C97541855","wikidata":"https://www.wikidata.org/wiki/Q830687","display_name":"Reinforcement learning","level":2,"score":0.5857031941413879},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.5247205495834351},{"id":"https://openalex.org/C124504099","wikidata":"https://www.wikidata.org/wiki/Q56933","display_name":"Image segmentation","level":3,"score":0.506742537021637},{"id":"https://openalex.org/C160633673","wikidata":"https://www.wikidata.org/wiki/Q355198","display_name":"Pixel","level":2,"score":0.505779504776001},{"id":"https://openalex.org/C2781238097","wikidata":"https://www.wikidata.org/wiki/Q175026","display_name":"Object (grammar)","level":2,"score":0.5050762295722961},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.5010616779327393},{"id":"https://openalex.org/C2777212361","wikidata":"https://www.wikidata.org/wiki/Q5127848","display_name":"Class (philosophy)","level":2,"score":0.48368293046951294},{"id":"https://openalex.org/C82990744","wikidata":"https://www.wikidata.org/wiki/Q166194","display_name":"RGB color model","level":2,"score":0.4650396406650543},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.4112653434276581},{"id":"https://openalex.org/C162324750","wikidata":"https://www.wikidata.org/wiki/Q8134","display_name":"Economics","level":0,"score":0.0},{"id":"https://openalex.org/C187736073","wikidata":"https://www.wikidata.org/wiki/Q2920921","display_name":"Management","level":1,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1109/access.2021.3136647","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2021.3136647","pdf_url":"https://ieeexplore.ieee.org/ielx7/6287639/6514899/09656121.pdf","source":{"id":"https://openalex.org/S2485537415","display_name":"IEEE Access","issn_l":"2169-3536","issn":["2169-3536"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Access","raw_type":"journal-article"},{"id":"pmh:oai:doaj.org/article:04dd56686f434e659765d2a7020a221c","is_oa":true,"landing_page_url":"https://doaj.org/article/04dd56686f434e659765d2a7020a221c","pdf_url":null,"source":{"id":"https://openalex.org/S4306401280","display_name":"DOAJ (DOAJ: Directory of Open Access Journals)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by-sa","license_id":"https://openalex.org/licenses/cc-by-sa","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"IEEE Access, Vol 9, Pp 168415-168432 (2021)","raw_type":"article"}],"best_oa_location":{"id":"doi:10.1109/access.2021.3136647","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2021.3136647","pdf_url":"https://ieeexplore.ieee.org/ielx7/6287639/6514899/09656121.pdf","source":{"id":"https://openalex.org/S2485537415","display_name":"IEEE Access","issn_l":"2169-3536","issn":["2169-3536"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Access","raw_type":"journal-article"},"sustainable_development_goals":[{"display_name":"Peace, Justice and strong institutions","id":"https://metadata.un.org/sdg/16","score":0.5799999833106995}],"awards":[{"id":"https://openalex.org/G3591868050","display_name":null,"funder_award_id":"015LC0-281","funder_id":"https://openalex.org/F4320323380","funder_display_name":"Universiti Teknologi Petronas"},{"id":"https://openalex.org/G8507552086","display_name":null,"funder_award_id":"015LC0-281","funder_id":"https://openalex.org/F4320329394","funder_display_name":"Yayasan UTP"}],"funders":[{"id":"https://openalex.org/F4320323380","display_name":"Universiti Teknologi Petronas","ror":"https://ror.org/048g2sh07"},{"id":"https://openalex.org/F4320329394","display_name":"Yayasan UTP","ror":null}],"has_content":{"pdf":true,"grobid_xml":true},"content_urls":{"pdf":"https://content.openalex.org/works/W4205371873.pdf","grobid_xml":"https://content.openalex.org/works/W4205371873.grobid-xml"},"referenced_works_count":98,"referenced_works":["https://openalex.org/W7746136","https://openalex.org/W129501612","https://openalex.org/W181701252","https://openalex.org/W603830301","https://openalex.org/W1423339008","https://openalex.org/W1542723449","https://openalex.org/W1610707153","https://openalex.org/W1666672054","https://openalex.org/W1686810756","https://openalex.org/W1745334888","https://openalex.org/W1903029394","https://openalex.org/W1913356549","https://openalex.org/W1923184257","https://openalex.org/W1923697677","https://openalex.org/W1992946551","https://openalex.org/W2022508996","https://openalex.org/W2027327099","https://openalex.org/W2037227137","https://openalex.org/W2054279472","https://openalex.org/W2056707879","https://openalex.org/W2059424674","https://openalex.org/W2083053289","https://openalex.org/W2097117768","https://openalex.org/W2100588357","https://openalex.org/W2116877738","https://openalex.org/W2117539524","https://openalex.org/W2123108597","https://openalex.org/W2153423793","https://openalex.org/W2155968351","https://openalex.org/W2171943915","https://openalex.org/W2340897893","https://openalex.org/W2460470859","https://openalex.org/W2462710403","https://openalex.org/W2487365028","https://openalex.org/W2535516436","https://openalex.org/W2536208356","https://openalex.org/W2544953446","https://openalex.org/W2592939477","https://openalex.org/W2620671107","https://openalex.org/W2689869580","https://openalex.org/W2734349601","https://openalex.org/W2746553466","https://openalex.org/W2783702157","https://openalex.org/W2785494456","https://openalex.org/W2798587560","https://openalex.org/W2886934227","https://openalex.org/W2889094138","https://openalex.org/W2897007254","https://openalex.org/W2897093520","https://openalex.org/W2897771603","https://openalex.org/W2903987318","https://openalex.org/W2914805075","https://openalex.org/W2949899225","https://openalex.org/W2951025196","https://openalex.org/W2951786554","https://openalex.org/W2963505445","https://openalex.org/W2963696295","https://openalex.org/W2963881378","https://openalex.org/W2964217532","https://openalex.org/W2970452316","https://openalex.org/W2977942577","https://openalex.org/W2988640543","https://openalex.org/W3005933814","https://openalex.org/W3006246324","https://openalex.org/W3009138919","https://openalex.org/W3012303644","https://openalex.org/W3037038302","https://openalex.org/W3041202696","https://openalex.org/W3045907623","https://openalex.org/W3090885652","https://openalex.org/W3101940057","https://openalex.org/W3111000109","https://openalex.org/W3143417647","https://openalex.org/W3176777339","https://openalex.org/W4244914727","https://openalex.org/W4293406525","https://openalex.org/W4294526978","https://openalex.org/W4300126339","https://openalex.org/W6618347045","https://openalex.org/W6628124331","https://openalex.org/W6637001620","https://openalex.org/W6637373629","https://openalex.org/W6640295612","https://openalex.org/W6675415620","https://openalex.org/W6717372056","https://openalex.org/W6729542845","https://openalex.org/W6733556328","https://openalex.org/W6735374517","https://openalex.org/W6738741286","https://openalex.org/W6739560345","https://openalex.org/W6741753795","https://openalex.org/W6741776117","https://openalex.org/W6747944915","https://openalex.org/W6755271026","https://openalex.org/W6756852718","https://openalex.org/W6757677476","https://openalex.org/W6759251327","https://openalex.org/W6772057793"],"related_works":["https://openalex.org/W4306904969","https://openalex.org/W2138720691","https://openalex.org/W4362501864","https://openalex.org/W4380318855","https://openalex.org/W3084456289","https://openalex.org/W2024136090","https://openalex.org/W4391331176","https://openalex.org/W2031695474","https://openalex.org/W2586732548","https://openalex.org/W2964765435"],"abstract_inverted_index":{"Semantic":[0],"segmentation":[1,86,178,247],"annotation":[2],"helps":[3],"train":[4],"computer":[5],"vision":[6],"based":[7,118],"Artificial":[8],"Intelligence":[9],"models":[10,235],"where":[11],"each":[12],"image":[13,148],"pixel":[14],"is":[15,46,71,76,172,228],"assigned":[16],"to":[17,26,51,73,105,195,230],"a":[18,47,112,120,128,144,256,262,281],"specific":[19],"object":[20,55],"class.":[21],"The":[22,67,165],"model":[23,103,179],"developers":[24],"try":[25],"identify":[27],"the":[28,33,52,61,80,84,98,102,106,140,155,168,175,187,197,214,222,231,244,268,289],"features":[29],"helpful":[30],"for":[31,64,83,134,182],"determining":[32],"objects":[34],"of":[35,54,142,146,158,170,199,240,254,259,265,277,284],"interest":[36],"by":[37],"using":[38],"various":[39],"supervised":[40],"deep":[41,121,233],"learning":[42,116,123,234],"techniques.":[43],"However,":[44],"this":[45],"difficult":[48],"task":[49,87],"due":[50],"complexity":[53],"structures.":[56],"Two":[57],"difficulties":[58],"arise":[59],"in":[60,236],"current":[62,290],"approaches":[63],"semantic":[65,85],"segmentation.":[66],"pixel-wise":[68],"label":[69],"approach":[70,206],"costly":[72],"obtain":[74],"and":[75,177,189,220,261,280],"time":[77],"consuming.":[78],"Second,":[79],"datasets":[81],"taken":[82,181],"are":[88,94,151],"not":[89],"balanced":[90],"since":[91],"certain":[92],"classes":[93,242],"present":[95],"more":[96,152,208],"than":[97,154,213],"others.":[99],"This":[100,125],"biases":[101],"performance":[104,227],"most":[107],"represented":[108],"ones.":[109],"We":[110,185],"propose":[111],"new":[113],"reinforced":[114],"active":[115,135],"strategy":[117,141],"on":[119,167,174,243],"reinforcement":[122],"algorithm.":[124],"work":[126],"presents":[127],"modified":[129],"Deep":[130],"Q":[131],"Learning":[132],"formulation":[133],"learning.":[136],"An":[137],"agent":[138],"learns":[139],"selecting":[143],"subset":[145],"small":[147],"regions,":[149],"which":[150],"knowledgeable":[153],"who":[156],"leset":[157],"images":[159],"from":[160,210],"an":[161,252,275],"unlabeled":[162],"data":[163],"pool.":[164],"decision":[166],"area":[169],"selection":[171],"dependent":[173],"assumptions":[176],"uncertainties":[180],"training":[183],"purposes.":[184],"use":[186],"CamVid":[188],"RGB":[190],"indoor":[191,270],"test":[192],"scenes":[193,271],"dataset":[194],"evaluate":[196],"proof":[198],"concept.":[200],"Our":[201,225],"results":[202],"infer":[203],"that":[204],"our":[205],"demands":[207],"labels":[209],"under-represented":[211],"groups":[212],"baselines,":[215],"thus":[216,286],"enhancing":[217],"their":[218],"efficiency":[219],"mitigating":[221],"class":[223],"imbalance.":[224],"method\u2019s":[226],"superior":[229],"conventional":[232],"detecting":[237],"8":[238],"out":[239,287],"11":[241],"Camvid":[245],"road":[246],"scene":[248],"dataset.":[249],"It":[250],"achieves":[251],"accuracy":[253,276],"90.56%,":[255],"mIoU":[257],"score":[258,264,283],"87.17%,":[260],"BF":[263,282],"93.14%.":[266],"On":[267],"SUNRGB":[269],"dataset,":[272],"it":[273],"gives":[274],"around":[278],"75.82%":[279],"77.25%,":[285],"performing":[288],"state-of-the-art":[291],"methods.":[292]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2025,"cited_by_count":8},{"year":2024,"cited_by_count":12},{"year":2023,"cited_by_count":9},{"year":2022,"cited_by_count":1}],"updated_date":"2026-06-11T09:08:48.828518","created_date":"2025-10-10T00:00:00"}
