{"id":"https://openalex.org/W2513520830","doi":"https://doi.org/10.1109/icip.2016.7532650","title":"Joint learning hash codes and distance metric for visual tracking","display_name":"Joint learning hash codes and distance metric for visual tracking","publication_year":2016,"publication_date":"2016-08-17","ids":{"openalex":"https://openalex.org/W2513520830","doi":"https://doi.org/10.1109/icip.2016.7532650","mag":"2513520830"},"language":"en","primary_location":{"id":"doi:10.1109/icip.2016.7532650","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icip.2016.7532650","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2016 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/A5101677959","display_name":"Luning Liu","orcid":"https://orcid.org/0000-0002-5539-5623"},"institutions":[{"id":"https://openalex.org/I27357992","display_name":"Dalian University of Technology","ror":"https://ror.org/023hj5876","country_code":"CN","type":"education","lineage":["https://openalex.org/I27357992"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Luning Liu","raw_affiliation_strings":["Dalian University of Technology, Dalian, China"],"affiliations":[{"raw_affiliation_string":"Dalian University of Technology, Dalian, China","institution_ids":["https://openalex.org/I27357992"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5006986293","display_name":"Huchuan Lu","orcid":"https://orcid.org/0000-0002-6668-9758"},"institutions":[{"id":"https://openalex.org/I27357992","display_name":"Dalian University of Technology","ror":"https://ror.org/023hj5876","country_code":"CN","type":"education","lineage":["https://openalex.org/I27357992"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Huchuan Lu","raw_affiliation_strings":["Dalian University of Technology, Dalian, China"],"affiliations":[{"raw_affiliation_string":"Dalian University of Technology, Dalian, China","institution_ids":["https://openalex.org/I27357992"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5101986286","display_name":"Xue Mei","orcid":"https://orcid.org/0000-0002-8237-1539"},"institutions":[{"id":"https://openalex.org/I4391768151","display_name":"Toyota Research Institute","ror":"https://ror.org/04fpkc108","country_code":null,"type":"facility","lineage":["https://openalex.org/I4210125472","https://openalex.org/I4391768151"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Xue Mei","raw_affiliation_strings":["Toyota Research Institute of North America, Ann Arbor, MI, USA"],"affiliations":[{"raw_affiliation_string":"Toyota Research Institute of North America, Ann Arbor, MI, USA","institution_ids":["https://openalex.org/I4391768151"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5101677959"],"corresponding_institution_ids":["https://openalex.org/I27357992"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":2,"citation_normalized_percentile":{"value":0.07865942,"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":"1709","last_page":"1713"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10331","display_name":"Video Surveillance and Tracking Methods","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/T10331","display_name":"Video Surveillance and Tracking Methods","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/T10627","display_name":"Advanced Image and Video Retrieval Techniques","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/T11439","display_name":"Video Analysis and Summarization","score":0.9947999715805054,"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/hash-function","display_name":"Hash function","score":0.7866511344909668},{"id":"https://openalex.org/keywords/feature-hashing","display_name":"Feature hashing","score":0.6420908570289612},{"id":"https://openalex.org/keywords/metric","display_name":"Metric (unit)","score":0.6320022940635681},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6165929436683655},{"id":"https://openalex.org/keywords/double-hashing","display_name":"Double hashing","score":0.568359911441803},{"id":"https://openalex.org/keywords/euclidean-distance","display_name":"Euclidean distance","score":0.5339022874832153},{"id":"https://openalex.org/keywords/similarity","display_name":"Similarity (geometry)","score":0.524722158908844},{"id":"https://openalex.org/keywords/locality-sensitive-hashing","display_name":"Locality-sensitive hashing","score":0.5241696834564209},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5097150206565857},{"id":"https://openalex.org/keywords/distance-matrix","display_name":"Distance matrix","score":0.506545901298523},{"id":"https://openalex.org/keywords/nearest-neighbor-search","display_name":"Nearest neighbor search","score":0.4664226174354553},{"id":"https://openalex.org/keywords/universal-hashing","display_name":"Universal hashing","score":0.46295860409736633},{"id":"https://openalex.org/keywords/hash-table","display_name":"Hash table","score":0.4578806757926941},{"id":"https://openalex.org/keywords/rolling-hash","display_name":"Rolling hash","score":0.4519484043121338},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.45057162642478943},{"id":"https://openalex.org/keywords/dynamic-perfect-hashing","display_name":"Dynamic perfect hashing","score":0.44921329617500305},{"id":"https://openalex.org/keywords/hamming-distance","display_name":"Hamming distance","score":0.41899242997169495},{"id":"https://openalex.org/keywords/k-nearest-neighbors-algorithm","display_name":"k-nearest neighbors algorithm","score":0.41790005564689636},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.3827155828475952},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.1733916699886322}],"concepts":[{"id":"https://openalex.org/C99138194","wikidata":"https://www.wikidata.org/wiki/Q183427","display_name":"Hash function","level":2,"score":0.7866511344909668},{"id":"https://openalex.org/C133667856","wikidata":"https://www.wikidata.org/wiki/Q5439682","display_name":"Feature hashing","level":5,"score":0.6420908570289612},{"id":"https://openalex.org/C176217482","wikidata":"https://www.wikidata.org/wiki/Q860554","display_name":"Metric (unit)","level":2,"score":0.6320022940635681},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6165929436683655},{"id":"https://openalex.org/C138111711","wikidata":"https://www.wikidata.org/wiki/Q478351","display_name":"Double hashing","level":4,"score":0.568359911441803},{"id":"https://openalex.org/C120174047","wikidata":"https://www.wikidata.org/wiki/Q847073","display_name":"Euclidean distance","level":2,"score":0.5339022874832153},{"id":"https://openalex.org/C103278499","wikidata":"https://www.wikidata.org/wiki/Q254465","display_name":"Similarity (geometry)","level":3,"score":0.524722158908844},{"id":"https://openalex.org/C74270461","wikidata":"https://www.wikidata.org/wiki/Q1625299","display_name":"Locality-sensitive hashing","level":4,"score":0.5241696834564209},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5097150206565857},{"id":"https://openalex.org/C111208986","wikidata":"https://www.wikidata.org/wiki/Q901698","display_name":"Distance matrix","level":2,"score":0.506545901298523},{"id":"https://openalex.org/C116738811","wikidata":"https://www.wikidata.org/wiki/Q608751","display_name":"Nearest neighbor search","level":2,"score":0.4664226174354553},{"id":"https://openalex.org/C116058348","wikidata":"https://www.wikidata.org/wiki/Q846912","display_name":"Universal hashing","level":5,"score":0.46295860409736633},{"id":"https://openalex.org/C67388219","wikidata":"https://www.wikidata.org/wiki/Q207440","display_name":"Hash table","level":3,"score":0.4578806757926941},{"id":"https://openalex.org/C108546238","wikidata":"https://www.wikidata.org/wiki/Q4228982","display_name":"Rolling hash","level":5,"score":0.4519484043121338},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.45057162642478943},{"id":"https://openalex.org/C122907437","wikidata":"https://www.wikidata.org/wiki/Q5318999","display_name":"Dynamic perfect hashing","level":5,"score":0.44921329617500305},{"id":"https://openalex.org/C193319292","wikidata":"https://www.wikidata.org/wiki/Q272172","display_name":"Hamming distance","level":2,"score":0.41899242997169495},{"id":"https://openalex.org/C113238511","wikidata":"https://www.wikidata.org/wiki/Q1071612","display_name":"k-nearest neighbors algorithm","level":2,"score":0.41790005564689636},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.3827155828475952},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.1733916699886322},{"id":"https://openalex.org/C38652104","wikidata":"https://www.wikidata.org/wiki/Q3510521","display_name":"Computer security","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/C162324750","wikidata":"https://www.wikidata.org/wiki/Q8134","display_name":"Economics","level":0,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/icip.2016.7532650","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icip.2016.7532650","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2016 IEEE International Conference on Image Processing (ICIP)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":21,"referenced_works":["https://openalex.org/W95072457","https://openalex.org/W1835419070","https://openalex.org/W1992502250","https://openalex.org/W2016075127","https://openalex.org/W2016618780","https://openalex.org/W2060814785","https://openalex.org/W2061736157","https://openalex.org/W2098854771","https://openalex.org/W2098941887","https://openalex.org/W2121193292","https://openalex.org/W2124211486","https://openalex.org/W2139047213","https://openalex.org/W2144935315","https://openalex.org/W2154889144","https://openalex.org/W2158917775","https://openalex.org/W2162670057","https://openalex.org/W2167089254","https://openalex.org/W2293597654","https://openalex.org/W6665839387","https://openalex.org/W6683636472","https://openalex.org/W6697214482"],"related_works":["https://openalex.org/W2100189723","https://openalex.org/W1998749283","https://openalex.org/W1554555624","https://openalex.org/W2184777945","https://openalex.org/W2921167217","https://openalex.org/W4212830455","https://openalex.org/W1997107867","https://openalex.org/W2783286101","https://openalex.org/W2000284985","https://openalex.org/W2253231004"],"abstract_inverted_index":{"In":[0],"this":[1],"paper,":[2],"we":[3,64,105],"propose":[4,75],"a":[5,76,89],"novel":[6],"tracking":[7,22,87],"algorithm":[8,84,132],"based":[9],"on":[10,45,124],"joint":[11,77],"learning":[12,68,78],"hash":[13,60,79,109],"codes":[14,61,80],"and":[15,81,88,115,117],"distance":[16,66,82],"metric.":[17],"We":[18,74],"formulate":[19],"the":[20,72,107,113,119,130,136],"visual":[21,86],"as":[23],"an":[24,46],"Approximate":[25],"Nearest":[26],"Neighbor":[27],"(ANN)":[28],"searching":[29],"process":[30],"in":[31],"which":[32],"hashing":[33,42],"methods":[34,43],"have":[35],"achieved":[36],"promising":[37],"performances.":[38],"But":[39],"most":[40],"existing":[41],"rely":[44],"affinity":[47],"or":[48],"similarity":[49],"matrix":[50],"measured":[51],"by":[52,100],"simple":[53],"Euclidean":[54],"distance.":[55],"To":[56],"obtain":[57],"more":[58],"robust":[59],"for":[62,85],"tracking,":[63],"utilize":[65],"metric":[67,83],"method":[69],"to":[70,94,111],"measure":[71],"similarity.":[73],"fast":[90],"solution":[91],"is":[92],"developed":[93],"solve":[95],"these":[96],"two":[97],"problems":[98],"simultaneously":[99],"cross":[101],"gradient":[102],"descent.":[103],"Then":[104],"use":[106],"learnt":[108],"function":[110],"encode":[112],"templates":[114],"candidates":[116],"conduct":[118],"ANN":[120],"searching.":[121],"Extensive":[122],"experiments":[123],"various":[125],"challenging":[126],"sequences":[127],"show":[128],"that":[129],"proposed":[131],"performs":[133],"favorably":[134],"against":[135],"state-of-the-art":[137],"methods.":[138]},"counts_by_year":[{"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"}
