{"id":"https://openalex.org/W2976397896","doi":"https://doi.org/10.1145/3490035.3490264","title":"Cross-view kernel similarity metric learning using pairwise constraints for person re-identification","display_name":"Cross-view kernel similarity metric learning using pairwise constraints for person re-identification","publication_year":2021,"publication_date":"2021-12-14","ids":{"openalex":"https://openalex.org/W2976397896","doi":"https://doi.org/10.1145/3490035.3490264","mag":"2976397896"},"language":"en","primary_location":{"id":"doi:10.1145/3490035.3490264","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3490035.3490264","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the Twelfth Indian Conference on Computer Vision, Graphics and Image Processing","raw_type":"proceedings-article"},"type":"preprint","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/A5102816576","display_name":"Feroz Ali","orcid":"https://orcid.org/0000-0003-4368-5831"},"institutions":[{"id":"https://openalex.org/I162827531","display_name":"Indian Institute of Technology Bombay","ror":"https://ror.org/02qyf5152","country_code":"IN","type":"education","lineage":["https://openalex.org/I162827531"]}],"countries":["IN"],"is_corresponding":true,"raw_author_name":"T M Feroz Ali","raw_affiliation_strings":["Indian Institute of Technology Bombay, Mumbai, Maharashtra, India"],"affiliations":[{"raw_affiliation_string":"Indian Institute of Technology Bombay, Mumbai, Maharashtra, India","institution_ids":["https://openalex.org/I162827531"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5016405213","display_name":"Subhasis Chaudhuri","orcid":"https://orcid.org/0000-0002-1680-0016"},"institutions":[{"id":"https://openalex.org/I162827531","display_name":"Indian Institute of Technology Bombay","ror":"https://ror.org/02qyf5152","country_code":"IN","type":"education","lineage":["https://openalex.org/I162827531"]}],"countries":["IN"],"is_corresponding":false,"raw_author_name":"Subhasis Chaudhuri","raw_affiliation_strings":["Indian Institute of Technology Bombay, Mumbai, Maharashtra, India"],"affiliations":[{"raw_affiliation_string":"Indian Institute of Technology Bombay, Mumbai, Maharashtra, India","institution_ids":["https://openalex.org/I162827531"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5102816576"],"corresponding_institution_ids":["https://openalex.org/I162827531"],"apc_list":null,"apc_paid":null,"fwci":0.30665844,"has_fulltext":false,"cited_by_count":3,"citation_normalized_percentile":{"value":0.52514549,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":94},"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"9"},"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/T10812","display_name":"Human Pose and Action Recognition","score":0.9930999875068665,"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":0.9909999966621399,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/kernel","display_name":"Kernel (algebra)","score":0.7514135837554932},{"id":"https://openalex.org/keywords/discriminative-model","display_name":"Discriminative model","score":0.7149986028671265},{"id":"https://openalex.org/keywords/pairwise-comparison","display_name":"Pairwise comparison","score":0.6787852048873901},{"id":"https://openalex.org/keywords/metric","display_name":"Metric (unit)","score":0.6457695960998535},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6344530582427979},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6292381286621094},{"id":"https://openalex.org/keywords/similarity","display_name":"Similarity (geometry)","score":0.6106677651405334},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.5589879751205444},{"id":"https://openalex.org/keywords/benchmark","display_name":"Benchmark (surveying)","score":0.5272097587585449},{"id":"https://openalex.org/keywords/subspace-topology","display_name":"Subspace topology","score":0.5171758532524109},{"id":"https://openalex.org/keywords/similarity-learning","display_name":"Similarity learning","score":0.4908602833747864},{"id":"https://openalex.org/keywords/matching","display_name":"Matching (statistics)","score":0.45666131377220154},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.4206588864326477},{"id":"https://openalex.org/keywords/linear-discriminant-analysis","display_name":"Linear discriminant analysis","score":0.4164387583732605},{"id":"https://openalex.org/keywords/support-vector-machine","display_name":"Support vector machine","score":0.41411423683166504},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.2864752411842346},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.17904448509216309}],"concepts":[{"id":"https://openalex.org/C74193536","wikidata":"https://www.wikidata.org/wiki/Q574844","display_name":"Kernel (algebra)","level":2,"score":0.7514135837554932},{"id":"https://openalex.org/C97931131","wikidata":"https://www.wikidata.org/wiki/Q5282087","display_name":"Discriminative model","level":2,"score":0.7149986028671265},{"id":"https://openalex.org/C184898388","wikidata":"https://www.wikidata.org/wiki/Q1435712","display_name":"Pairwise comparison","level":2,"score":0.6787852048873901},{"id":"https://openalex.org/C176217482","wikidata":"https://www.wikidata.org/wiki/Q860554","display_name":"Metric (unit)","level":2,"score":0.6457695960998535},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6344530582427979},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6292381286621094},{"id":"https://openalex.org/C103278499","wikidata":"https://www.wikidata.org/wiki/Q254465","display_name":"Similarity (geometry)","level":3,"score":0.6106677651405334},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.5589879751205444},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.5272097587585449},{"id":"https://openalex.org/C32834561","wikidata":"https://www.wikidata.org/wiki/Q660730","display_name":"Subspace topology","level":2,"score":0.5171758532524109},{"id":"https://openalex.org/C2779597229","wikidata":"https://www.wikidata.org/wiki/Q17146505","display_name":"Similarity learning","level":3,"score":0.4908602833747864},{"id":"https://openalex.org/C165064840","wikidata":"https://www.wikidata.org/wiki/Q1321061","display_name":"Matching (statistics)","level":2,"score":0.45666131377220154},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.4206588864326477},{"id":"https://openalex.org/C69738355","wikidata":"https://www.wikidata.org/wiki/Q1228929","display_name":"Linear discriminant analysis","level":2,"score":0.4164387583732605},{"id":"https://openalex.org/C12267149","wikidata":"https://www.wikidata.org/wiki/Q282453","display_name":"Support vector machine","level":2,"score":0.41411423683166504},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.2864752411842346},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.17904448509216309},{"id":"https://openalex.org/C205649164","wikidata":"https://www.wikidata.org/wiki/Q1071","display_name":"Geography","level":0,"score":0.0},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.0},{"id":"https://openalex.org/C13280743","wikidata":"https://www.wikidata.org/wiki/Q131089","display_name":"Geodesy","level":1,"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/C21547014","wikidata":"https://www.wikidata.org/wiki/Q1423657","display_name":"Operations management","level":1,"score":0.0},{"id":"https://openalex.org/C114614502","wikidata":"https://www.wikidata.org/wiki/Q76592","display_name":"Combinatorics","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3490035.3490264","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3490035.3490264","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the Twelfth Indian Conference on Computer Vision, Graphics and Image Processing","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"display_name":"Reduced inequalities","id":"https://metadata.un.org/sdg/10","score":0.7400000095367432}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":56,"referenced_works":["https://openalex.org/W42769906","https://openalex.org/W46454230","https://openalex.org/W140822990","https://openalex.org/W166429404","https://openalex.org/W1602182271","https://openalex.org/W1709635438","https://openalex.org/W1911311607","https://openalex.org/W1920259731","https://openalex.org/W1927348918","https://openalex.org/W1928419358","https://openalex.org/W1949591461","https://openalex.org/W1962025484","https://openalex.org/W2009907187","https://openalex.org/W2014764728","https://openalex.org/W2068042582","https://openalex.org/W2079972027","https://openalex.org/W2106053110","https://openalex.org/W2109824782","https://openalex.org/W2115669554","https://openalex.org/W2130556178","https://openalex.org/W2151065677","https://openalex.org/W2169495281","https://openalex.org/W2220271458","https://openalex.org/W2289130514","https://openalex.org/W2300840837","https://openalex.org/W2341680599","https://openalex.org/W2342611082","https://openalex.org/W2361187101","https://openalex.org/W2433217581","https://openalex.org/W2467139031","https://openalex.org/W2474013209","https://openalex.org/W2475284720","https://openalex.org/W2491664569","https://openalex.org/W2502225121","https://openalex.org/W2520831962","https://openalex.org/W2584637367","https://openalex.org/W2604211872","https://openalex.org/W2606377603","https://openalex.org/W2736410039","https://openalex.org/W2766623491","https://openalex.org/W2776203024","https://openalex.org/W2778775889","https://openalex.org/W2789117442","https://openalex.org/W2883682053","https://openalex.org/W2884534488","https://openalex.org/W2891772265","https://openalex.org/W2963047834","https://openalex.org/W2963569453","https://openalex.org/W2963690547","https://openalex.org/W2963721283","https://openalex.org/W2964009357","https://openalex.org/W2964186374","https://openalex.org/W2964246751","https://openalex.org/W2997481653","https://openalex.org/W3023708773","https://openalex.org/W3102668440"],"related_works":["https://openalex.org/W4389116644","https://openalex.org/W2153315159","https://openalex.org/W3103844505","https://openalex.org/W259157601","https://openalex.org/W4205463238","https://openalex.org/W2761785940","https://openalex.org/W2487162673","https://openalex.org/W1482209366","https://openalex.org/W2793211469","https://openalex.org/W2110523656"],"abstract_inverted_index":{"Person":[0],"re-identification":[1],"is":[2,60,85,111],"the":[3,82],"task":[4],"of":[5,64],"matching":[6],"pedestrian":[7],"images":[8],"across":[9],"non-overlapping":[10],"cameras.":[11],"In":[12,77],"this":[13],"paper,":[14],"we":[15],"propose":[16],"an":[17],"efficient":[18,88],"kernel":[19],"based":[20,54],"similarity":[21,57],"metric":[22,52],"learning":[23,25,48,53],"for":[24,33],"non-linear":[26,41,73],"features":[27],"using":[28,75],"small":[29],"scale":[30],"training":[31],"data":[32],"practical":[34],"person":[35],"re-ID":[36],"systems.":[37],"The":[38],"method":[39,84,102],"employs":[40],"mappings":[42],"combined":[43],"with":[44],"cross-view":[45,50],"discriminative":[46],"subspace":[47],"and":[49],"distance":[51],"on":[55,95],"pairwise":[56],"constraints.":[58],"It":[59],"a":[61],"natural":[62],"extension":[63],"Cross-view":[65],"Quadratic":[66],"Discriminant":[67],"Analysis":[68],"(XQDA)":[69],"from":[70],"linear":[71],"to":[72,79,90],"model":[74],"kernels.":[76],"addition":[78],"outperforming":[80],"XQDA,":[81],"proposed":[83],"computationally":[86],"very":[87],"compared":[89],"its":[91],"baselines.":[92],"Extensive":[93],"experiments":[94],"four":[96],"benchmark":[97],"datasets":[98],"show":[99],"that":[100],"our":[101],"attains":[103],"competitive":[104],"performance":[105],"against":[106],"state-of-the-art":[107],"methods.":[108],"Our":[109],"code":[110],"available":[112],"at":[113],"https://github.com/ferozalitm/Efficient-Kernel-XQDA.":[114]},"counts_by_year":[{"year":2024,"cited_by_count":1},{"year":2022,"cited_by_count":1},{"year":2021,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
