{"id":"https://openalex.org/W2117908400","doi":"https://doi.org/10.1145/1101826.1101852","title":"Multiple random walk and its application in content-based image retrieval","display_name":"Multiple random walk and its application in content-based image retrieval","publication_year":2005,"publication_date":"2005-11-10","ids":{"openalex":"https://openalex.org/W2117908400","doi":"https://doi.org/10.1145/1101826.1101852","mag":"2117908400"},"language":"en","primary_location":{"id":"doi:10.1145/1101826.1101852","is_oa":false,"landing_page_url":"https://doi.org/10.1145/1101826.1101852","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 7th ACM SIGMM international workshop on Multimedia information retrieval","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/A5073158087","display_name":"Jingrui He","orcid":"https://orcid.org/0000-0002-6429-6272"},"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":"Jingrui He","raw_affiliation_strings":["Tsinghua University, Beijing, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Tsinghua University, Beijing, China","institution_ids":["https://openalex.org/I99065089"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5068043486","display_name":"Hanghang Tong","orcid":"https://orcid.org/0000-0003-4405-3887"},"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":"Hanghang Tong","raw_affiliation_strings":["Tsinghua University, Beijing, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Tsinghua University, Beijing, China","institution_ids":["https://openalex.org/I99065089"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5061131131","display_name":"Mingjing Li","orcid":"https://orcid.org/0000-0002-5290-8104"},"institutions":[{"id":"https://openalex.org/I4210113369","display_name":"Microsoft Research Asia (China)","ror":"https://ror.org/0300m5276","country_code":"CN","type":"company","lineage":["https://openalex.org/I1290206253","https://openalex.org/I4210113369"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Mingjing Li","raw_affiliation_strings":["Microsft Research Asia, Beijing, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Microsft Research Asia, Beijing, China","institution_ids":["https://openalex.org/I4210113369"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5103733614","display_name":"Wei\u2010Ying Ma","orcid":"https://orcid.org/0000-0002-7384-0735"},"institutions":[{"id":"https://openalex.org/I4210113369","display_name":"Microsoft Research Asia (China)","ror":"https://ror.org/0300m5276","country_code":"CN","type":"company","lineage":["https://openalex.org/I1290206253","https://openalex.org/I4210113369"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Wei-Ying Ma","raw_affiliation_strings":["Microsft Research Asia, Beijing, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Microsft Research Asia, Beijing, China","institution_ids":["https://openalex.org/I4210113369"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5065063835","display_name":"Changshui Zhang","orcid":"https://orcid.org/0000-0002-8088-367X"},"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":"Changshui Zhang","raw_affiliation_strings":["Tsinghua University, Beijing, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Tsinghua University, Beijing, China","institution_ids":["https://openalex.org/I99065089"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":5,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.2991,"has_fulltext":false,"cited_by_count":8,"citation_normalized_percentile":{"value":0.62122752,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":96},"biblio":{"volume":null,"issue":null,"first_page":"151","last_page":"158"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10824","display_name":"Image Retrieval and Classification 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"}},"topics":[{"id":"https://openalex.org/T10824","display_name":"Image Retrieval and Classification 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/T10627","display_name":"Advanced Image and Video Retrieval Techniques","score":0.9972000122070312,"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/T11309","display_name":"Music and Audio Processing","score":0.991100013256073,"subfield":{"id":"https://openalex.org/subfields/1711","display_name":"Signal Processing"},"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/random-walk","display_name":"Random walk","score":0.8154157400131226},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6257561445236206},{"id":"https://openalex.org/keywords/prior-probability","display_name":"Prior probability","score":0.5508298873901367},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5481019020080566},{"id":"https://openalex.org/keywords/generative-model","display_name":"Generative model","score":0.5106433033943176},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.5074455142021179},{"id":"https://openalex.org/keywords/markov-chain","display_name":"Markov chain","score":0.49864864349365234},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.4645862877368927},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.3773990571498871},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.33648091554641724},{"id":"https://openalex.org/keywords/generative-grammar","display_name":"Generative grammar","score":0.21418392658233643},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.1455690860748291},{"id":"https://openalex.org/keywords/bayesian-probability","display_name":"Bayesian probability","score":0.10258433222770691}],"concepts":[{"id":"https://openalex.org/C121194460","wikidata":"https://www.wikidata.org/wiki/Q856741","display_name":"Random walk","level":2,"score":0.8154157400131226},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6257561445236206},{"id":"https://openalex.org/C177769412","wikidata":"https://www.wikidata.org/wiki/Q278090","display_name":"Prior probability","level":3,"score":0.5508298873901367},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5481019020080566},{"id":"https://openalex.org/C167966045","wikidata":"https://www.wikidata.org/wiki/Q5532625","display_name":"Generative model","level":3,"score":0.5106433033943176},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.5074455142021179},{"id":"https://openalex.org/C98763669","wikidata":"https://www.wikidata.org/wiki/Q176645","display_name":"Markov chain","level":2,"score":0.49864864349365234},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.4645862877368927},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3773990571498871},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.33648091554641724},{"id":"https://openalex.org/C39890363","wikidata":"https://www.wikidata.org/wiki/Q36108","display_name":"Generative grammar","level":2,"score":0.21418392658233643},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.1455690860748291},{"id":"https://openalex.org/C107673813","wikidata":"https://www.wikidata.org/wiki/Q812534","display_name":"Bayesian probability","level":2,"score":0.10258433222770691}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/1101826.1101852","is_oa":false,"landing_page_url":"https://doi.org/10.1145/1101826.1101852","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 7th ACM SIGMM international workshop on Multimedia information retrieval","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"score":0.7200000286102295,"id":"https://metadata.un.org/sdg/4","display_name":"Quality Education"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":34,"referenced_works":["https://openalex.org/W146900863","https://openalex.org/W1486632395","https://openalex.org/W1514107797","https://openalex.org/W1542769445","https://openalex.org/W1586847502","https://openalex.org/W1917380066","https://openalex.org/W1969294188","https://openalex.org/W1978779397","https://openalex.org/W2011549082","https://openalex.org/W2039118116","https://openalex.org/W2089366864","https://openalex.org/W2101498401","https://openalex.org/W2105568170","https://openalex.org/W2106198479","https://openalex.org/W2108901118","https://openalex.org/W2121100155","https://openalex.org/W2121947440","https://openalex.org/W2122837498","https://openalex.org/W2124087378","https://openalex.org/W2125101937","https://openalex.org/W2125148312","https://openalex.org/W2131791003","https://openalex.org/W2132984323","https://openalex.org/W2133491976","https://openalex.org/W2139823104","https://openalex.org/W2154455818","https://openalex.org/W2164193311","https://openalex.org/W2164547069","https://openalex.org/W2171140863","https://openalex.org/W2914885528","https://openalex.org/W6642652525","https://openalex.org/W6676378522","https://openalex.org/W6679655593","https://openalex.org/W6682494755"],"related_works":["https://openalex.org/W2580650124","https://openalex.org/W4386190339","https://openalex.org/W2968424575","https://openalex.org/W3142333283","https://openalex.org/W3122088529","https://openalex.org/W3041320102","https://openalex.org/W2111669074","https://openalex.org/W2085259108","https://openalex.org/W3123087812","https://openalex.org/W2063076820"],"abstract_inverted_index":{"In":[0],"this":[1,110],"paper,":[2],"we":[3],"propose":[4],"a":[5,142,161],"transductive":[6,143],"learning":[7,144],"method":[8],"for":[9,33,43],"content-based":[10],"image":[11,163],"retrieval:":[12],"Multiple":[13],"Random":[14],"Walk":[15],"(MRW).":[16],"Its":[17],"basic":[18],"idea":[19],"is":[20,49],"to":[21,36,50,95,123,150],"construct":[22],"two":[23,61,85],"generative":[24],"models":[25],"by":[26,154],"means":[27],"of":[28,55,77,100,127,166,173],"Markov":[29],"random":[30,62,86],"walks,":[31],"one":[32],"images":[34,118,169],"relevant":[35],"the":[37,41,44,52,70,74,78,84,101,116,120,171],"query":[38],"concept":[39],"and":[40,68,134],"other":[42],"irrelevant":[45],"ones.":[46],"The":[47],"goal":[48],"obtain":[51],"likelihood":[53,79,102],"functions":[54],"both":[56,132],"classes.":[57],"Firstly,":[58],"MRW":[59,113,137,174],"generates":[60],"walks":[63,87],"with":[64],"virtual":[65],"absorbing":[66,71],"boundaries,":[67],"uses":[69],"probabilities":[72,126],"as":[73,141],"initial":[75],"estimation":[76,99],"functions.":[80,103],"Then":[81],"it":[82],"refines":[83],"through":[88],"an":[89],"EM-like":[90],"iterative":[91],"procedure":[92],"in":[93,109,119],"order":[94],"get":[96],"more":[97],"accurate":[98],"Class":[104],"priors":[105],"are":[106],"also":[107],"obtained":[108],"procedure.":[111],"Finally,":[112],"ranks":[114],"all":[115],"unlabeled":[117,135],"database":[121,164],"according":[122],"their":[124],"posterior":[125],"being":[128],"relevant.":[129],"By":[130],"using":[131],"labeled":[133],"data,":[136],"can":[138],"be":[139],"seen":[140],"method,":[145],"which":[146],"has":[147],"been":[148],"demonstrated":[149],"outperform":[151],"inductive":[152],"ones":[153],"previous":[155],"research":[156],"work.":[157],"Systematic":[158],"experiments":[159],"on":[160],"general-purpose":[162],"consisting":[165],"5,000":[167],"Corel":[168],"demonstrate":[170],"superiority":[172],"over":[175],"state-of-the-art":[176],"techniques.":[177]},"counts_by_year":[{"year":2015,"cited_by_count":1},{"year":2014,"cited_by_count":2},{"year":2013,"cited_by_count":1}],"updated_date":"2026-06-11T09:08:48.828518","created_date":"2025-10-10T00:00:00"}