{"id":"https://openalex.org/W2512032049","doi":"https://doi.org/10.1109/tip.2016.2607421","title":"Large-Scale Cross-Modality Search via Collective Matrix Factorization Hashing","display_name":"Large-Scale Cross-Modality Search via Collective Matrix Factorization Hashing","publication_year":2016,"publication_date":"2016-09-08","ids":{"openalex":"https://openalex.org/W2512032049","doi":"https://doi.org/10.1109/tip.2016.2607421","mag":"2512032049","pmid":"https://pubmed.ncbi.nlm.nih.gov/27623584"},"language":"en","primary_location":{"id":"doi:10.1109/tip.2016.2607421","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tip.2016.2607421","pdf_url":null,"source":{"id":"https://openalex.org/S4210173141","display_name":"IEEE Transactions on Image Processing","issn_l":"1057-7149","issn":["1057-7149","1941-0042"],"is_oa":false,"is_in_doaj":false,"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":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Transactions on Image Processing","raw_type":"journal-article"},"type":"article","indexed_in":["crossref","pubmed"],"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/A5057732142","display_name":"Guiguang Ding","orcid":"https://orcid.org/0000-0003-0137-9975"},"institutions":[{"id":"https://openalex.org/I99065089","display_name":"Tsinghua University","ror":"https://ror.org/03cve4549","country_code":"CN","type":"education","lineage":["https://openalex.org/I99065089"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Guiguang Ding","raw_affiliation_strings":["School of Software, Tsinghua University, Beijing, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Software, Tsinghua University, Beijing, China","institution_ids":["https://openalex.org/I99065089"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5035519525","display_name":"Yuchen Guo","orcid":"https://orcid.org/0000-0001-9808-9805"},"institutions":[{"id":"https://openalex.org/I99065089","display_name":"Tsinghua University","ror":"https://ror.org/03cve4549","country_code":"CN","type":"education","lineage":["https://openalex.org/I99065089"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yuchen Guo","raw_affiliation_strings":["School of Software, Tsinghua University, Beijing, China"],"raw_orcid":"https://orcid.org/0000-0001-9808-9805","affiliations":[{"raw_affiliation_string":"School of Software, Tsinghua University, Beijing, China","institution_ids":["https://openalex.org/I99065089"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5028464154","display_name":"Jile Zhou","orcid":null},"institutions":[{"id":"https://openalex.org/I99065089","display_name":"Tsinghua University","ror":"https://ror.org/03cve4549","country_code":"CN","type":"education","lineage":["https://openalex.org/I99065089"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Jile Zhou","raw_affiliation_strings":["School of Software, Tsinghua University, Beijing, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Software, Tsinghua University, Beijing, China","institution_ids":["https://openalex.org/I99065089"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5100602494","display_name":"Yue Gao","orcid":"https://orcid.org/0000-0002-4971-590X"},"institutions":[{"id":"https://openalex.org/I99065089","display_name":"Tsinghua University","ror":"https://ror.org/03cve4549","country_code":"CN","type":"education","lineage":["https://openalex.org/I99065089"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yue Gao","raw_affiliation_strings":["School of Software, Tsinghua University, Beijing, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Software, Tsinghua University, Beijing, China","institution_ids":["https://openalex.org/I99065089"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I99065089"],"apc_list":null,"apc_paid":null,"fwci":21.6923,"has_fulltext":false,"cited_by_count":226,"citation_normalized_percentile":{"value":0.9963247,"is_in_top_1_percent":true,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":99,"max":100},"biblio":{"volume":"25","issue":"11","first_page":"5427","last_page":"5440"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10627","display_name":"Advanced Image and Video Retrieval Techniques","score":0.9998999834060669,"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/T10627","display_name":"Advanced Image and Video Retrieval Techniques","score":0.9998999834060669,"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/T11714","display_name":"Multimodal Machine Learning Applications","score":0.9886999726295471,"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/T11478","display_name":"Caching and Content Delivery","score":0.9878000020980835,"subfield":{"id":"https://openalex.org/subfields/1705","display_name":"Computer Networks and Communications"},"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":0.7825135588645935},{"id":"https://openalex.org/keywords/hash-function","display_name":"Hash function","score":0.6508426666259766},{"id":"https://openalex.org/keywords/locality-sensitive-hashing","display_name":"Locality-sensitive hashing","score":0.6221878528594971},{"id":"https://openalex.org/keywords/dynamic-perfect-hashing","display_name":"Dynamic perfect hashing","score":0.5800995826721191},{"id":"https://openalex.org/keywords/modality","display_name":"Modality (human\u2013computer interaction)","score":0.5428095459938049},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.46435120701789856},{"id":"https://openalex.org/keywords/benchmark","display_name":"Benchmark (surveying)","score":0.46310728788375854},{"id":"https://openalex.org/keywords/universal-hashing","display_name":"Universal hashing","score":0.4478849768638611},{"id":"https://openalex.org/keywords/hash-table","display_name":"Hash table","score":0.44324126839637756},{"id":"https://openalex.org/keywords/information-retrieval","display_name":"Information retrieval","score":0.3988867998123169},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.34622305631637573},{"id":"https://openalex.org/keywords/theoretical-computer-science","display_name":"Theoretical computer science","score":0.3448074460029602},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.34415149688720703},{"id":"https://openalex.org/keywords/double-hashing","display_name":"Double hashing","score":0.34151774644851685}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7825135588645935},{"id":"https://openalex.org/C99138194","wikidata":"https://www.wikidata.org/wiki/Q183427","display_name":"Hash function","level":2,"score":0.6508426666259766},{"id":"https://openalex.org/C74270461","wikidata":"https://www.wikidata.org/wiki/Q1625299","display_name":"Locality-sensitive hashing","level":4,"score":0.6221878528594971},{"id":"https://openalex.org/C122907437","wikidata":"https://www.wikidata.org/wiki/Q5318999","display_name":"Dynamic perfect hashing","level":5,"score":0.5800995826721191},{"id":"https://openalex.org/C2780226545","wikidata":"https://www.wikidata.org/wiki/Q6888030","display_name":"Modality (human\u2013computer interaction)","level":2,"score":0.5428095459938049},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.46435120701789856},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.46310728788375854},{"id":"https://openalex.org/C116058348","wikidata":"https://www.wikidata.org/wiki/Q846912","display_name":"Universal hashing","level":5,"score":0.4478849768638611},{"id":"https://openalex.org/C67388219","wikidata":"https://www.wikidata.org/wiki/Q207440","display_name":"Hash table","level":3,"score":0.44324126839637756},{"id":"https://openalex.org/C23123220","wikidata":"https://www.wikidata.org/wiki/Q816826","display_name":"Information retrieval","level":1,"score":0.3988867998123169},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.34622305631637573},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.3448074460029602},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.34415149688720703},{"id":"https://openalex.org/C138111711","wikidata":"https://www.wikidata.org/wiki/Q478351","display_name":"Double hashing","level":4,"score":0.34151774644851685},{"id":"https://openalex.org/C13280743","wikidata":"https://www.wikidata.org/wiki/Q131089","display_name":"Geodesy","level":1,"score":0.0},{"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/C205649164","wikidata":"https://www.wikidata.org/wiki/Q1071","display_name":"Geography","level":0,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1109/tip.2016.2607421","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tip.2016.2607421","pdf_url":null,"source":{"id":"https://openalex.org/S4210173141","display_name":"IEEE Transactions on Image Processing","issn_l":"1057-7149","issn":["1057-7149","1941-0042"],"is_oa":false,"is_in_doaj":false,"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":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Transactions on Image Processing","raw_type":"journal-article"},{"id":"pmid:27623584","is_oa":false,"landing_page_url":"https://pubmed.ncbi.nlm.nih.gov/27623584","pdf_url":null,"source":{"id":"https://openalex.org/S4306525036","display_name":"PubMed","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I1299303238","host_organization_name":"National Institutes of Health","host_organization_lineage":["https://openalex.org/I1299303238"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE transactions on image processing : a publication of the IEEE Signal Processing Society","raw_type":null}],"best_oa_location":null,"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/9","score":0.46000000834465027,"display_name":"Industry, innovation and infrastructure"}],"awards":[{"id":"https://openalex.org/G187184461","display_name":"\u57fa\u4e8e\u6df1\u5ea6\u5377\u79ef\u795e\u7ecf\u7f51\u7edc\u7684\u5bf9\u8c61\u68c0\u6d4b\u5173\u952e\u6280\u672f\u7814\u7a76","funder_award_id":"61571269","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G6201887244","display_name":"\u6570\u636e\u9a71\u52a8\u7684\u5927\u89c4\u6a21\u56fe\u50cf\u81ea\u52a8\u6807\u6ce8\u5173\u952e\u6280\u672f\u7814\u7a76","funder_award_id":"61271394","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"}],"funders":[{"id":"https://openalex.org/F4320321001","display_name":"National Natural Science Foundation of China","ror":"https://ror.org/01h0zpd94"}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":75,"referenced_works":["https://openalex.org/W20696413","https://openalex.org/W189447628","https://openalex.org/W199018803","https://openalex.org/W1502916507","https://openalex.org/W1663973292","https://openalex.org/W1835419070","https://openalex.org/W1880262756","https://openalex.org/W1920291194","https://openalex.org/W1965766334","https://openalex.org/W1967035541","https://openalex.org/W1970055505","https://openalex.org/W1974647172","https://openalex.org/W1985258161","https://openalex.org/W1988423806","https://openalex.org/W1989902617","https://openalex.org/W1992371516","https://openalex.org/W2007972815","https://openalex.org/W2012592962","https://openalex.org/W2024668293","https://openalex.org/W2029205712","https://openalex.org/W2036718463","https://openalex.org/W2044195942","https://openalex.org/W2049993534","https://openalex.org/W2054141820","https://openalex.org/W2054854521","https://openalex.org/W2063069198","https://openalex.org/W2064797228","https://openalex.org/W2074668987","https://openalex.org/W2076511276","https://openalex.org/W2084363474","https://openalex.org/W2090575037","https://openalex.org/W2093432797","https://openalex.org/W2104290444","https://openalex.org/W2106277773","https://openalex.org/W2106890447","https://openalex.org/W2107799335","https://openalex.org/W2108119513","https://openalex.org/W2117420919","https://openalex.org/W2118585731","https://openalex.org/W2132914434","https://openalex.org/W2148603752","https://openalex.org/W2151103935","https://openalex.org/W2154872931","https://openalex.org/W2155803963","https://openalex.org/W2156909104","https://openalex.org/W2162670057","https://openalex.org/W2163502583","https://openalex.org/W2171626009","https://openalex.org/W2203543769","https://openalex.org/W2267050401","https://openalex.org/W2280985394","https://openalex.org/W2293597654","https://openalex.org/W2411707397","https://openalex.org/W2460139639","https://openalex.org/W2798909945","https://openalex.org/W2913932916","https://openalex.org/W2997701990","https://openalex.org/W4230674625","https://openalex.org/W4238530616","https://openalex.org/W4245049592","https://openalex.org/W6600811662","https://openalex.org/W6608183366","https://openalex.org/W6629956336","https://openalex.org/W6639619044","https://openalex.org/W6648560114","https://openalex.org/W6675747103","https://openalex.org/W6675918080","https://openalex.org/W6677656871","https://openalex.org/W6682644385","https://openalex.org/W6685282478","https://openalex.org/W6688143368","https://openalex.org/W6693399182","https://openalex.org/W6695456611","https://openalex.org/W6697214482","https://openalex.org/W6715177018"],"related_works":["https://openalex.org/W102859714","https://openalex.org/W2044784665","https://openalex.org/W1870428314","https://openalex.org/W2333289748","https://openalex.org/W2000284985","https://openalex.org/W2293015666","https://openalex.org/W2158169729","https://openalex.org/W2086731314","https://openalex.org/W2975588143","https://openalex.org/W2120031510"],"abstract_inverted_index":{"By":[0],"transforming":[1],"data":[2,36,75,186],"into":[3],"binary":[4],"representation,":[5],"i.e.,":[6],"Hashing,":[7],"we":[8,140],"can":[9,125,197],"perform":[10],"high-speed":[11],"search":[12,51,132],"with":[13,39,43],"low":[14],"storage":[15],"cost,":[16],"and":[17,45,155,173],"thus,":[18],"Hashing":[19,88,96,203],"has":[20],"collected":[21],"increasing":[22],"research":[23,66],"interest":[24],"in":[25,57,76,115,121,143,156],"the":[26,70,77,116,137,144,152,157,164,174,207,210],"recent":[27],"years.":[28],"Recently,":[29],"how":[30],"to":[31,104,150],"generate":[32],"Hashcode":[33],"for":[34,48,59,86,108,188],"multimodal":[35,74,87,113],"(e.g.,":[37,52],"images":[38,56],"textual":[40],"tags,":[41],"documents":[42],"photos,":[44],"so":[46],"on)":[47],"large-scale":[49],"cross-modality":[50,131,189,202],"searching":[53],"semantically":[54],"related":[55],"database":[58],"a":[60,83],"document":[61],"query)":[62],"is":[63,89,103,133],"an":[64],"important":[65],"issue":[67],"because":[68],"of":[69,73,101,111,167,209],"fast":[71],"growth":[72],"Web.":[78],"To":[79],"address":[80],"this":[81],"issue,":[82],"novel":[84],"framework":[85],"proposed,":[90],"termed":[91],"as":[92],"Collective":[93],"Matrix":[94],"Factorization":[95],"(CMFH).":[97],"The":[98,169,191],"key":[99],"idea":[100],"CMFH":[102,196],"learn":[105],"unified":[106],"Hashcodes":[107],"different":[109,123],"modalities":[110,124],"one":[112],"instance":[114],"shared":[117],"latent":[118],"semantic":[119],"space":[120],"which":[122,205],"be":[126],"effectively":[127],"connected.":[128],"Therefore,":[129],"accurate":[130],"supported.":[134],"Based":[135],"on":[136,183],"general":[138],"framework,":[139],"extend":[141],"it":[142,148,161],"unsupervised":[145],"scenario":[146,159],"where":[147,160],"tries":[149],"preserve":[151],"Euclidean":[153],"structure,":[154],"supervised":[158],"fully":[162],"exploits":[163],"label":[165],"information":[166],"data.":[168],"corresponding":[170],"theoretical":[171],"analysis":[172],"optimization":[175],"algorithms":[176],"are":[177],"given.":[178],"We":[179],"conducted":[180],"comprehensive":[181],"experiments":[182],"three":[184],"benchmark":[185],"sets":[187],"search.":[190],"experimental":[192],"results":[193],"demonstrate":[194],"that":[195],"significantly":[198],"outperform":[199],"several":[200],"state-of-the-art":[201],"methods,":[204],"validates":[206],"effectiveness":[208],"proposed":[211],"CMFH.":[212]},"counts_by_year":[{"year":2026,"cited_by_count":4},{"year":2025,"cited_by_count":14},{"year":2024,"cited_by_count":18},{"year":2023,"cited_by_count":16},{"year":2022,"cited_by_count":27},{"year":2021,"cited_by_count":18},{"year":2020,"cited_by_count":26},{"year":2019,"cited_by_count":29},{"year":2018,"cited_by_count":36},{"year":2017,"cited_by_count":38}],"updated_date":"2026-07-19T07:52:34.831488","created_date":"2025-10-10T00:00:00"}