{"id":"https://openalex.org/W2793808377","doi":"https://doi.org/10.1109/icip.2017.8296914","title":"Unsupervised person re-identification via re-ranking enhanced sample-specific metric learning","display_name":"Unsupervised person re-identification via re-ranking enhanced sample-specific metric learning","publication_year":2017,"publication_date":"2017-09-01","ids":{"openalex":"https://openalex.org/W2793808377","doi":"https://doi.org/10.1109/icip.2017.8296914","mag":"2793808377"},"language":"en","primary_location":{"id":"doi:10.1109/icip.2017.8296914","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icip.2017.8296914","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":"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/A5043365677","display_name":"Heng Zhao","orcid":"https://orcid.org/0000-0003-2938-5613"},"institutions":[{"id":"https://openalex.org/I4210165038","display_name":"University of Chinese Academy of Sciences","ror":"https://ror.org/05qbk4x57","country_code":"CN","type":"education","lineage":["https://openalex.org/I19820366","https://openalex.org/I4210165038"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Heng Zhao","raw_affiliation_strings":["School of Electronics, University of Chinese Academy of Sciences, Beijing, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Electronics, University of Chinese Academy of Sciences, Beijing, China","institution_ids":["https://openalex.org/I4210165038"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5000034551","display_name":"Zhenjun Han","orcid":"https://orcid.org/0000-0002-9970-5152"},"institutions":[{"id":"https://openalex.org/I19820366","display_name":"Chinese Academy of Sciences","ror":"https://ror.org/034t30j35","country_code":"CN","type":"government","lineage":["https://openalex.org/I19820366"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Zhenjun Han","raw_affiliation_strings":["Insititute of Electrics, Chinese Academy of Sciences"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Insititute of Electrics, Chinese Academy of Sciences","institution_ids":["https://openalex.org/I19820366"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5012622067","display_name":"Zhaoju Li","orcid":"https://orcid.org/0000-0003-3917-3921"},"institutions":[{"id":"https://openalex.org/I4210165038","display_name":"University of Chinese Academy of Sciences","ror":"https://ror.org/05qbk4x57","country_code":"CN","type":"education","lineage":["https://openalex.org/I19820366","https://openalex.org/I4210165038"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Zhaoju Li","raw_affiliation_strings":["School of Electronics, University of Chinese Academy of Sciences, Beijing, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Electronics, University of Chinese Academy of Sciences, Beijing, China","institution_ids":["https://openalex.org/I4210165038"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5074507539","display_name":"Fei Qin","orcid":"https://orcid.org/0000-0003-1671-8968"},"institutions":[{"id":"https://openalex.org/I19820366","display_name":"Chinese Academy of Sciences","ror":"https://ror.org/034t30j35","country_code":"CN","type":"government","lineage":["https://openalex.org/I19820366"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Fei Qin","raw_affiliation_strings":["Insititute of Electrics, Chinese Academy of Sciences"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Insititute of Electrics, Chinese Academy of Sciences","institution_ids":["https://openalex.org/I19820366"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":4,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.1847,"has_fulltext":false,"cited_by_count":3,"citation_normalized_percentile":{"value":0.60909505,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":95},"biblio":{"volume":null,"issue":null,"first_page":"3405","last_page":"3409"},"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":0.9965999722480774,"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/T11448","display_name":"Face recognition and analysis","score":0.9919000267982483,"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/metric","display_name":"Metric (unit)","score":0.7357316017150879},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7279419898986816},{"id":"https://openalex.org/keywords/ranking","display_name":"Ranking (information retrieval)","score":0.7266134023666382},{"id":"https://openalex.org/keywords/margin","display_name":"Margin (machine learning)","score":0.7264533042907715},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6743555068969727},{"id":"https://openalex.org/keywords/identification","display_name":"Identification (biology)","score":0.6724860668182373},{"id":"https://openalex.org/keywords/sample","display_name":"Sample (material)","score":0.6219154596328735},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.6014459133148193},{"id":"https://openalex.org/keywords/intersection","display_name":"Intersection (aeronautics)","score":0.5712301731109619},{"id":"https://openalex.org/keywords/unsupervised-learning","display_name":"Unsupervised learning","score":0.5391741991043091},{"id":"https://openalex.org/keywords/aggregate","display_name":"Aggregate (composite)","score":0.5093055963516235},{"id":"https://openalex.org/keywords/supervised-learning","display_name":"Supervised learning","score":0.421689510345459},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.4185616075992584},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.0630635917186737}],"concepts":[{"id":"https://openalex.org/C176217482","wikidata":"https://www.wikidata.org/wiki/Q860554","display_name":"Metric (unit)","level":2,"score":0.7357316017150879},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7279419898986816},{"id":"https://openalex.org/C189430467","wikidata":"https://www.wikidata.org/wiki/Q7293293","display_name":"Ranking (information retrieval)","level":2,"score":0.7266134023666382},{"id":"https://openalex.org/C774472","wikidata":"https://www.wikidata.org/wiki/Q6760393","display_name":"Margin (machine learning)","level":2,"score":0.7264533042907715},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6743555068969727},{"id":"https://openalex.org/C116834253","wikidata":"https://www.wikidata.org/wiki/Q2039217","display_name":"Identification (biology)","level":2,"score":0.6724860668182373},{"id":"https://openalex.org/C198531522","wikidata":"https://www.wikidata.org/wiki/Q485146","display_name":"Sample (material)","level":2,"score":0.6219154596328735},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.6014459133148193},{"id":"https://openalex.org/C64543145","wikidata":"https://www.wikidata.org/wiki/Q162942","display_name":"Intersection (aeronautics)","level":2,"score":0.5712301731109619},{"id":"https://openalex.org/C8038995","wikidata":"https://www.wikidata.org/wiki/Q1152135","display_name":"Unsupervised learning","level":2,"score":0.5391741991043091},{"id":"https://openalex.org/C4679612","wikidata":"https://www.wikidata.org/wiki/Q866298","display_name":"Aggregate (composite)","level":2,"score":0.5093055963516235},{"id":"https://openalex.org/C136389625","wikidata":"https://www.wikidata.org/wiki/Q334384","display_name":"Supervised learning","level":3,"score":0.421689510345459},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.4185616075992584},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.0630635917186737},{"id":"https://openalex.org/C185592680","wikidata":"https://www.wikidata.org/wiki/Q2329","display_name":"Chemistry","level":0,"score":0.0},{"id":"https://openalex.org/C192562407","wikidata":"https://www.wikidata.org/wiki/Q228736","display_name":"Materials science","level":0,"score":0.0},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.0},{"id":"https://openalex.org/C162324750","wikidata":"https://www.wikidata.org/wiki/Q8134","display_name":"Economics","level":0,"score":0.0},{"id":"https://openalex.org/C159985019","wikidata":"https://www.wikidata.org/wiki/Q181790","display_name":"Composite material","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},{"id":"https://openalex.org/C59822182","wikidata":"https://www.wikidata.org/wiki/Q441","display_name":"Botany","level":1,"score":0.0},{"id":"https://openalex.org/C43617362","wikidata":"https://www.wikidata.org/wiki/Q170050","display_name":"Chromatography","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},{"id":"https://openalex.org/C146978453","wikidata":"https://www.wikidata.org/wiki/Q3798668","display_name":"Aerospace engineering","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/icip.2017.8296914","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icip.2017.8296914","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":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":31,"referenced_works":["https://openalex.org/W41482161","https://openalex.org/W42769906","https://openalex.org/W46454230","https://openalex.org/W166429404","https://openalex.org/W1518138188","https://openalex.org/W1596233070","https://openalex.org/W1602182271","https://openalex.org/W1949591461","https://openalex.org/W1963702692","https://openalex.org/W1979260620","https://openalex.org/W2009907187","https://openalex.org/W2022469758","https://openalex.org/W2068042582","https://openalex.org/W2079972027","https://openalex.org/W2089074647","https://openalex.org/W2113609219","https://openalex.org/W2161969291","https://openalex.org/W2163808566","https://openalex.org/W2168356304","https://openalex.org/W2168812120","https://openalex.org/W2300840837","https://openalex.org/W2327827989","https://openalex.org/W2433217581","https://openalex.org/W2441160157","https://openalex.org/W4297731226","https://openalex.org/W6601733005","https://openalex.org/W6601887387","https://openalex.org/W6606760634","https://openalex.org/W6636160605","https://openalex.org/W6645089675","https://openalex.org/W6684802500"],"related_works":["https://openalex.org/W3148060700","https://openalex.org/W3080681248","https://openalex.org/W4376646226","https://openalex.org/W3047177827","https://openalex.org/W4287685660","https://openalex.org/W2057778272","https://openalex.org/W4319302697","https://openalex.org/W2986085304","https://openalex.org/W2163389298","https://openalex.org/W205223620"],"abstract_inverted_index":{"Despite":[0],"of":[1,5,91],"the":[2,60,73,89,95,114],"great":[3],"progress":[4],"image-based":[6],"person":[7,104],"re-identification,":[8],"most":[9],"existing":[10],"methods":[11],"use":[12],"supervised":[13],"metric":[14,41,67],"learning":[15,42,75],"to":[16,26,45,59,86],"build":[17],"re-identification":[18,96,105],"models":[19],"and":[20,93,109],"thus":[21],"require":[22],"repeated":[23],"human":[24],"effort":[25],"annotate":[27],"sample":[28,71],"pairs":[29],"from":[30],"non-overlapping":[31],"cameras.":[32],"In":[33],"this":[34,47],"paper,":[35],"we":[36,63,111],"propose":[37],"an":[38],"unsupervised":[39],"sample-specific":[40],"approach":[43,116],"(SSML)":[44],"alleviate":[46],"problem.":[48],"Specifically,":[49],"using":[50],"samples":[51,92],"those":[52],"are":[53],"negatives":[54],"(with":[55],"a":[56,65,78],"high":[57],"probability)":[58],"query":[61,70],"samples,":[62],"train":[64],"local":[66],"for":[68],"each":[69],"following":[72],"max-margin":[74],"theory.":[76],"Moreover,":[77],"KNN":[79],"intersection":[80],"re-ranking":[81],"(KIRR)":[82],"method":[83],"is":[84,117],"used":[85,103],"further":[87],"decrease":[88],"ambiguity":[90],"aggregate":[94],"performance.":[97],"With":[98],"experiments":[99],"on":[100],"three":[101],"widely":[102],"datasets:":[106],"VIPeR,":[107],"CUHK01,":[108],"PRID,":[110],"demonstrate":[112],"that":[113],"proposed":[115],"simple":[118],"but":[119],"effective.":[120]},"counts_by_year":[{"year":2025,"cited_by_count":1},{"year":2020,"cited_by_count":1},{"year":2019,"cited_by_count":1}],"updated_date":"2026-06-11T09:08:48.828518","created_date":"2025-10-10T00:00:00"}