{"id":"https://openalex.org/W3089495002","doi":"https://doi.org/10.1109/icip40778.2020.9190744","title":"Open-Set Metric Learning For Person Re-Identification In The Wild","display_name":"Open-Set Metric Learning For Person Re-Identification In The Wild","publication_year":2020,"publication_date":"2020-09-30","ids":{"openalex":"https://openalex.org/W3089495002","doi":"https://doi.org/10.1109/icip40778.2020.9190744","mag":"3089495002"},"language":"en","primary_location":{"id":"doi:10.1109/icip40778.2020.9190744","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icip40778.2020.9190744","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2020 IEEE International Conference on Image Processing (ICIP)","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/A5043177235","display_name":"Arindam Sikdar","orcid":"https://orcid.org/0000-0002-5697-0060"},"institutions":[{"id":"https://openalex.org/I170979836","display_name":"Jadavpur University","ror":"https://ror.org/02af4h012","country_code":"IN","type":"education","lineage":["https://openalex.org/I170979836"]}],"countries":["IN"],"is_corresponding":true,"raw_author_name":"Arindam Sikdar","raw_affiliation_strings":["Department of Electronics and Telecommunication Engineering, Jadavpur University, Kolkata, India"],"affiliations":[{"raw_affiliation_string":"Department of Electronics and Telecommunication Engineering, Jadavpur University, Kolkata, India","institution_ids":["https://openalex.org/I170979836"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5055761154","display_name":"Dibyadip Chatterjee","orcid":"https://orcid.org/0000-0002-2651-3045"},"institutions":[{"id":"https://openalex.org/I170979836","display_name":"Jadavpur University","ror":"https://ror.org/02af4h012","country_code":"IN","type":"education","lineage":["https://openalex.org/I170979836"]}],"countries":["IN"],"is_corresponding":false,"raw_author_name":"Dibyadip Chatterjee","raw_affiliation_strings":["Department of Electronics and Telecommunication Engineering, Jadavpur University, Kolkata, India"],"affiliations":[{"raw_affiliation_string":"Department of Electronics and Telecommunication Engineering, Jadavpur University, Kolkata, India","institution_ids":["https://openalex.org/I170979836"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5076266300","display_name":"Arpan Bhowmik","orcid":"https://orcid.org/0000-0001-9830-8748"},"institutions":[{"id":"https://openalex.org/I170979836","display_name":"Jadavpur University","ror":"https://ror.org/02af4h012","country_code":"IN","type":"education","lineage":["https://openalex.org/I170979836"]}],"countries":["IN"],"is_corresponding":false,"raw_author_name":"Arpan Bhowmik","raw_affiliation_strings":["Department of Electronics and Telecommunication Engineering, Jadavpur University, Kolkata, India"],"affiliations":[{"raw_affiliation_string":"Department of Electronics and Telecommunication Engineering, Jadavpur University, Kolkata, India","institution_ids":["https://openalex.org/I170979836"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5031703924","display_name":"Ananda S. Chowdhury","orcid":"https://orcid.org/0000-0002-5799-3467"},"institutions":[{"id":"https://openalex.org/I170979836","display_name":"Jadavpur University","ror":"https://ror.org/02af4h012","country_code":"IN","type":"education","lineage":["https://openalex.org/I170979836"]}],"countries":["IN"],"is_corresponding":false,"raw_author_name":"Ananda S. Chowdhury","raw_affiliation_strings":["Department of Electronics and Telecommunication Engineering, Jadavpur University, Kolkata, India"],"affiliations":[{"raw_affiliation_string":"Department of Electronics and Telecommunication Engineering, Jadavpur University, Kolkata, India","institution_ids":["https://openalex.org/I170979836"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5043177235"],"corresponding_institution_ids":["https://openalex.org/I170979836"],"apc_list":null,"apc_paid":null,"fwci":0.5888,"has_fulltext":false,"cited_by_count":6,"citation_normalized_percentile":{"value":0.69701792,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":95,"max":97},"biblio":{"volume":"10","issue":null,"first_page":"2356","last_page":"2360"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10331","display_name":"Video Surveillance and Tracking Methods","score":1.0,"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/T10331","display_name":"Video Surveillance and Tracking Methods","score":1.0,"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/T12740","display_name":"Gait Recognition and Analysis","score":0.9980999827384949,"subfield":{"id":"https://openalex.org/subfields/2204","display_name":"Biomedical Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T10812","display_name":"Human Pose and Action Recognition","score":0.9855999946594238,"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/large-margin-nearest-neighbor","display_name":"Large margin nearest neighbor","score":0.8310657739639282},{"id":"https://openalex.org/keywords/metric","display_name":"Metric (unit)","score":0.6812442541122437},{"id":"https://openalex.org/keywords/mahalanobis-distance","display_name":"Mahalanobis distance","score":0.6783866882324219},{"id":"https://openalex.org/keywords/robustness","display_name":"Robustness (evolution)","score":0.6001224517822266},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5806947946548462},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.5389941334724426},{"id":"https://openalex.org/keywords/open-set","display_name":"Open set","score":0.5196208357810974},{"id":"https://openalex.org/keywords/set","display_name":"Set (abstract data type)","score":0.51591956615448},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.4988832473754883},{"id":"https://openalex.org/keywords/weibull-distribution","display_name":"Weibull distribution","score":0.47100725769996643},{"id":"https://openalex.org/keywords/margin","display_name":"Margin (machine learning)","score":0.46221229434013367},{"id":"https://openalex.org/keywords/k-nearest-neighbors-algorithm","display_name":"k-nearest neighbors algorithm","score":0.4516037702560425},{"id":"https://openalex.org/keywords/frame","display_name":"Frame (networking)","score":0.4324682950973511},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.39987045526504517},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.3162381052970886},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.18559309840202332},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.16302639245986938}],"concepts":[{"id":"https://openalex.org/C94475309","wikidata":"https://www.wikidata.org/wiki/Q6489154","display_name":"Large margin nearest neighbor","level":3,"score":0.8310657739639282},{"id":"https://openalex.org/C176217482","wikidata":"https://www.wikidata.org/wiki/Q860554","display_name":"Metric (unit)","level":2,"score":0.6812442541122437},{"id":"https://openalex.org/C1921717","wikidata":"https://www.wikidata.org/wiki/Q1334846","display_name":"Mahalanobis distance","level":2,"score":0.6783866882324219},{"id":"https://openalex.org/C63479239","wikidata":"https://www.wikidata.org/wiki/Q7353546","display_name":"Robustness (evolution)","level":3,"score":0.6001224517822266},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5806947946548462},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5389941334724426},{"id":"https://openalex.org/C42357961","wikidata":"https://www.wikidata.org/wiki/Q213363","display_name":"Open set","level":2,"score":0.5196208357810974},{"id":"https://openalex.org/C177264268","wikidata":"https://www.wikidata.org/wiki/Q1514741","display_name":"Set (abstract data type)","level":2,"score":0.51591956615448},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.4988832473754883},{"id":"https://openalex.org/C173291955","wikidata":"https://www.wikidata.org/wiki/Q732332","display_name":"Weibull distribution","level":2,"score":0.47100725769996643},{"id":"https://openalex.org/C774472","wikidata":"https://www.wikidata.org/wiki/Q6760393","display_name":"Margin (machine learning)","level":2,"score":0.46221229434013367},{"id":"https://openalex.org/C113238511","wikidata":"https://www.wikidata.org/wiki/Q1071612","display_name":"k-nearest neighbors algorithm","level":2,"score":0.4516037702560425},{"id":"https://openalex.org/C126042441","wikidata":"https://www.wikidata.org/wiki/Q1324888","display_name":"Frame (networking)","level":2,"score":0.4324682950973511},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.39987045526504517},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.3162381052970886},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.18559309840202332},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.16302639245986938},{"id":"https://openalex.org/C118615104","wikidata":"https://www.wikidata.org/wiki/Q121416","display_name":"Discrete mathematics","level":1,"score":0.0},{"id":"https://openalex.org/C104317684","wikidata":"https://www.wikidata.org/wiki/Q7187","display_name":"Gene","level":2,"score":0.0},{"id":"https://openalex.org/C76155785","wikidata":"https://www.wikidata.org/wiki/Q418","display_name":"Telecommunications","level":1,"score":0.0},{"id":"https://openalex.org/C55493867","wikidata":"https://www.wikidata.org/wiki/Q7094","display_name":"Biochemistry","level":1,"score":0.0},{"id":"https://openalex.org/C185592680","wikidata":"https://www.wikidata.org/wiki/Q2329","display_name":"Chemistry","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/C21547014","wikidata":"https://www.wikidata.org/wiki/Q1423657","display_name":"Operations management","level":1,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1109/icip40778.2020.9190744","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icip40778.2020.9190744","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2020 IEEE International Conference on Image Processing (ICIP)","raw_type":"proceedings-article"},{"id":"pmh:oai:pure.atira.dk:publications/c18afc1a-60ba-4dee-a810-6fe6e8bbc981","is_oa":false,"landing_page_url":"https://research.edgehill.ac.uk/en/publications/c18afc1a-60ba-4dee-a810-6fe6e8bbc981","pdf_url":null,"source":{"id":"https://openalex.org/S4306402462","display_name":"Edge Hill University Research Information Repository (Edge Hill University)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I165525304","host_organization_name":"Edge Hill University","host_organization_lineage":["https://openalex.org/I165525304"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"Sikdar, A, Chatterjee, D, Bhowmik, A & Chowdhury, A S 2020, Open-Set Metric Learning for Person Re-Identification in the Wild. in 2020 IEEE International Conference on Image Processing, ICIP 2020 - Proceedings., 9190744, Proceedings - International Conference on Image Processing, ICIP, vol. 2020-October, IEEE Computer Society, pp. 2356-2360, 2020 IEEE International Conference on Image Processing, ICIP 2020, Virtual, Abu Dhabi, United Arab Emirates, 25/09/20. https://doi.org/10.1109/ICIP40778.2020.9190744","raw_type":"info:eu-repo/semantics/publishedVersion"}],"best_oa_location":null,"sustainable_development_goals":[{"display_name":"Peace, Justice and strong institutions","id":"https://metadata.un.org/sdg/16","score":0.5}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":27,"referenced_works":["https://openalex.org/W157648163","https://openalex.org/W166429404","https://openalex.org/W572355794","https://openalex.org/W1536680647","https://openalex.org/W1829502122","https://openalex.org/W1949591461","https://openalex.org/W2000359198","https://openalex.org/W2018459374","https://openalex.org/W2068042582","https://openalex.org/W2106053110","https://openalex.org/W2125447566","https://openalex.org/W2168356304","https://openalex.org/W2204750386","https://openalex.org/W2300840837","https://openalex.org/W2511556322","https://openalex.org/W2565639579","https://openalex.org/W2901946163","https://openalex.org/W2947319827","https://openalex.org/W2963150697","https://openalex.org/W2963901085","https://openalex.org/W2991531996","https://openalex.org/W2999625864","https://openalex.org/W3008706867","https://openalex.org/W6606760634","https://openalex.org/W6638408480","https://openalex.org/W6675751002","https://openalex.org/W6762782019"],"related_works":["https://openalex.org/W140822990","https://openalex.org/W1498155531","https://openalex.org/W2351157934","https://openalex.org/W2519241726","https://openalex.org/W2015442739","https://openalex.org/W2432484786","https://openalex.org/W1985839407","https://openalex.org/W2108882154","https://openalex.org/W2107754327","https://openalex.org/W2125687350"],"abstract_inverted_index":{"Person":[0],"re-identification":[1],"in":[2,17,39],"the":[3,40,75,100,110,114,143,150],"wild":[4],"needs":[5],"to":[6,52],"simultaneously":[7],"(frame-wise)":[8,41],"detect":[9],"and":[10,13,56,113],"re-identify":[11],"persons":[12,33],"has":[14],"wide":[15],"utility":[16],"practical":[18],"scenarios.":[19],"However,":[20],"such":[21,54,67],"tasks":[22],"come":[23],"with":[24,66,137],"an":[25],"additional":[26],"open-set":[27,69],"re-ID":[28,47],"challenge":[29],"as":[30,61],"all":[31],"probe":[32],"may":[34],"not":[35,50],"necessarily":[36],"be":[37],"present":[38],"dynamic":[42],"gallery.":[43],"Traditional":[44],"or":[45],"close-set":[46],"systems":[48],"are":[49],"equipped":[51],"handle":[53],"cases":[55],"raise":[57],"several":[58],"false":[59],"alarms":[60],"a":[62,106],"result.":[63],"To":[64],"cope":[65],"challenges":[68],"metric":[70,116,139],"learning":[71,140],"(OSML),":[72],"based":[73,126],"on":[74,127],"concept":[76],"of":[77,95,109,132,152],"Large":[78],"margin":[79],"nearest":[80],"neighbor":[81],"(LMNN)":[82],"approach,":[83],"is":[84,103,124],"proposed.":[85],"We":[86],"term":[87],"our":[88,153],"method":[89],"Open-Set":[90],"LMNN":[91],"(OS-LMNN).":[92],"The":[93,122],"goal":[94],"separating":[96],"impostor":[97],"samples":[98,102],"from":[99],"genuine":[101],"achieved":[104],"through":[105,118],"joint":[107],"optimization":[108],"Weibull":[111],"distribution":[112],"Mahalanobis":[115],"learned":[117],"this":[119],"OS-LMNN":[120],"approach.":[121,154],"rejection":[123],"performed":[125],"low":[128],"probability":[129],"over":[130,142],"distance":[131],"imposter":[133],"pairs.":[134],"Exhaustive":[135],"experiments":[136],"other":[138],"techniques":[141],"publicly":[144],"available":[145],"PRW":[146],"dataset":[147],"clearly":[148],"demonstrate":[149],"robustness":[151]},"counts_by_year":[{"year":2023,"cited_by_count":3},{"year":2021,"cited_by_count":3}],"updated_date":"2026-04-22T08:38:42.863108","created_date":"2025-10-10T00:00:00"}
