{"id":"https://openalex.org/W2415415256","doi":"https://doi.org/10.1109/acpr.2015.7486501","title":"Bayesian nonparametric inference of latent topic hierarchies for multimodal data","display_name":"Bayesian nonparametric inference of latent topic hierarchies for multimodal data","publication_year":2015,"publication_date":"2015-11-01","ids":{"openalex":"https://openalex.org/W2415415256","doi":"https://doi.org/10.1109/acpr.2015.7486501","mag":"2415415256"},"language":"en","primary_location":{"id":"doi:10.1109/acpr.2015.7486501","is_oa":false,"landing_page_url":"https://doi.org/10.1109/acpr.2015.7486501","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2015 3rd IAPR Asian Conference on Pattern Recognition (ACPR)","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/A5023658289","display_name":"Takuji Shimamawari","orcid":null},"institutions":[{"id":"https://openalex.org/I65837984","display_name":"Kobe University","ror":"https://ror.org/03tgsfw79","country_code":"JP","type":"education","lineage":["https://openalex.org/I65837984"]}],"countries":["JP"],"is_corresponding":true,"raw_author_name":"Takuji Shimamawari","raw_affiliation_strings":["Kobe University"],"affiliations":[{"raw_affiliation_string":"Kobe University","institution_ids":["https://openalex.org/I65837984"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5103291241","display_name":"Koji Eguchi","orcid":"https://orcid.org/0009-0004-6743-2972"},"institutions":[{"id":"https://openalex.org/I65837984","display_name":"Kobe University","ror":"https://ror.org/03tgsfw79","country_code":"JP","type":"education","lineage":["https://openalex.org/I65837984"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"Koji Eguchi","raw_affiliation_strings":["Kobe University"],"affiliations":[{"raw_affiliation_string":"Kobe University","institution_ids":["https://openalex.org/I65837984"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5087434029","display_name":"Atsuhiro Takasu","orcid":"https://orcid.org/0000-0002-9061-7949"},"institutions":[{"id":"https://openalex.org/I184597095","display_name":"National Institute of Informatics","ror":"https://ror.org/04ksd4g47","country_code":"JP","type":"facility","lineage":["https://openalex.org/I1319490839","https://openalex.org/I184597095","https://openalex.org/I4210158934"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"Atsuhiro Takasu","raw_affiliation_strings":["National Institute of Informatics"],"affiliations":[{"raw_affiliation_string":"National Institute of Informatics","institution_ids":["https://openalex.org/I184597095"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5023658289"],"corresponding_institution_ids":["https://openalex.org/I65837984"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.20997715,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":"3","issue":null,"first_page":"236","last_page":"240"},"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":0.9987999796867371,"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":0.9987999796867371,"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":0.9987000226974487,"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/T10824","display_name":"Image Retrieval and Classification Techniques","score":0.9961000084877014,"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/latent-dirichlet-allocation","display_name":"Latent Dirichlet allocation","score":0.8963897228240967},{"id":"https://openalex.org/keywords/topic-model","display_name":"Topic model","score":0.8413875102996826},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7570985555648804},{"id":"https://openalex.org/keywords/hierarchy","display_name":"Hierarchy","score":0.6394733190536499},{"id":"https://openalex.org/keywords/inference","display_name":"Inference","score":0.5993818640708923},{"id":"https://openalex.org/keywords/hierarchical-database-model","display_name":"Hierarchical database model","score":0.5606203079223633},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5065897703170776},{"id":"https://openalex.org/keywords/hierarchical-dirichlet-process","display_name":"Hierarchical Dirichlet process","score":0.47364556789398193},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.4680499732494354},{"id":"https://openalex.org/keywords/bayesian-probability","display_name":"Bayesian probability","score":0.45542022585868835},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.38164597749710083}],"concepts":[{"id":"https://openalex.org/C500882744","wikidata":"https://www.wikidata.org/wiki/Q269236","display_name":"Latent Dirichlet allocation","level":3,"score":0.8963897228240967},{"id":"https://openalex.org/C171686336","wikidata":"https://www.wikidata.org/wiki/Q3532085","display_name":"Topic model","level":2,"score":0.8413875102996826},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7570985555648804},{"id":"https://openalex.org/C31170391","wikidata":"https://www.wikidata.org/wiki/Q188619","display_name":"Hierarchy","level":2,"score":0.6394733190536499},{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.5993818640708923},{"id":"https://openalex.org/C144986985","wikidata":"https://www.wikidata.org/wiki/Q871236","display_name":"Hierarchical database model","level":2,"score":0.5606203079223633},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5065897703170776},{"id":"https://openalex.org/C141318989","wikidata":"https://www.wikidata.org/wiki/Q5753066","display_name":"Hierarchical Dirichlet process","level":4,"score":0.47364556789398193},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.4680499732494354},{"id":"https://openalex.org/C107673813","wikidata":"https://www.wikidata.org/wiki/Q812534","display_name":"Bayesian probability","level":2,"score":0.45542022585868835},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.38164597749710083},{"id":"https://openalex.org/C34447519","wikidata":"https://www.wikidata.org/wiki/Q179522","display_name":"Market economy","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/acpr.2015.7486501","is_oa":false,"landing_page_url":"https://doi.org/10.1109/acpr.2015.7486501","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2015 3rd IAPR Asian Conference on Pattern Recognition (ACPR)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[{"id":"https://openalex.org/F4320327550","display_name":"National Institute of Informatics","ror":"https://ror.org/04ksd4g47"}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":16,"referenced_works":["https://openalex.org/W263845233","https://openalex.org/W1880262756","https://openalex.org/W1979936637","https://openalex.org/W2001082470","https://openalex.org/W2020842694","https://openalex.org/W2025147484","https://openalex.org/W2107034620","https://openalex.org/W2132827946","https://openalex.org/W2147625498","https://openalex.org/W2151103935","https://openalex.org/W2155803963","https://openalex.org/W2160061993","https://openalex.org/W4231510805","https://openalex.org/W4248892431","https://openalex.org/W6639619044","https://openalex.org/W6683410722"],"related_works":["https://openalex.org/W2914864478","https://openalex.org/W2008338582","https://openalex.org/W2097627380","https://openalex.org/W1999586157","https://openalex.org/W4291700620","https://openalex.org/W22044811","https://openalex.org/W2625329765","https://openalex.org/W2529577912","https://openalex.org/W2766840109","https://openalex.org/W2955328590"],"abstract_inverted_index":{"Research":[0],"on":[1],"multimodal":[2,33,51,58,112,130,142],"data":[3,59,72,78,102,113,131],"analysis":[4,9],"such":[5,83],"as":[6,36,84],"annotated":[7,167],"image":[8],"is":[10,32],"becoming":[11],"more":[12],"important":[13],"than":[14],"ever":[15],"due":[16],"to":[17,29,124],"the":[18,21,27,134,137,151],"increase":[19],"in":[20],"amount":[22,76],"of":[23,26,39,77,95,136,153,166],"data.":[24],"One":[25],"approaches":[28],"this":[30,119],"problem":[31],"topic":[34,46,52,81,107,127,143,147],"models":[35,47,53,82,108,144,160],"an":[37],"extension":[38],"latent":[40,86,96,126],"Dirichlet":[41,87],"allocation":[42,88],"(LDA).":[43],"Symmetric":[44],"correspondence":[45],"(SymCorrLDA)":[48],"are":[49],"state-of-the-art":[50],"that":[54],"can":[55,67,90,109],"appropriately":[56,110],"model":[57,155],"considering":[60,114],"inter-modal":[61],"dependencies.":[62,117],"Incidentally,":[63],"hierarchically":[64],"structured":[65],"categories":[66],"help":[68],"users":[69],"find":[70],"relevant":[71],"from":[73,98,129],"a":[74,92,99],"large":[75],"collection.":[79],"Hierarchical":[80],"hierarchical":[85,106,146],"(hLDA)":[89],"discover":[91,125],"tree-structured":[93],"hierarchy":[94],"topics":[97],"given":[100],"unimodal":[101],"collection;":[103],"however,":[104],"no":[105],"handle":[111],"intermodal":[115],"mutual":[116],"In":[118],"paper,":[120],"we":[121],"propose":[122],"h-SymCorrLDA":[123],"hierarchies":[128],"by":[132],"combining":[133],"ideas":[135],"two":[138,164],"previously":[139],"mentioned":[140],"models:":[141],"and":[145],"models.":[148],"We":[149],"demonstrate":[150],"effectiveness":[152],"our":[154],"compared":[156],"with":[157,163],"several":[158],"baseline":[159],"through":[161],"experiments":[162],"datasets":[165],"images.":[168]},"counts_by_year":[],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}