{"id":"https://openalex.org/W1592593258","doi":"https://doi.org/10.1109/icra.2015.7139614","title":"Gesture recognition using hybrid generative-discriminative approach with Fisher Vector","display_name":"Gesture recognition using hybrid generative-discriminative approach with Fisher Vector","publication_year":2015,"publication_date":"2015-05-01","ids":{"openalex":"https://openalex.org/W1592593258","doi":"https://doi.org/10.1109/icra.2015.7139614","mag":"1592593258"},"language":"en","primary_location":{"id":"doi:10.1109/icra.2015.7139614","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icra.2015.7139614","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2015 IEEE International Conference on Robotics and Automation (ICRA)","raw_type":"proceedings-article"},"type":"conference-paper","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/A5062663906","display_name":"Yusuke Goutsu","orcid":"https://orcid.org/0000-0002-9712-6720"},"institutions":[{"id":"https://openalex.org/I74801974","display_name":"The University of Tokyo","ror":"https://ror.org/057zh3y96","country_code":"JP","type":"education","lineage":["https://openalex.org/I74801974"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"Yusuke Goutsu","raw_affiliation_strings":["Department of Mechano-Informatics, The University of Tokyo, Bunkyo-ku, Tokyo, Japan","Department of Mechano-Informatics, Graduate School of Information Science and Technology, The University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, 113-8656, Japan"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Mechano-Informatics, The University of Tokyo, Bunkyo-ku, Tokyo, Japan","institution_ids":["https://openalex.org/I74801974"]},{"raw_affiliation_string":"Department of Mechano-Informatics, Graduate School of Information Science and Technology, The University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, 113-8656, Japan","institution_ids":["https://openalex.org/I74801974"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5019765580","display_name":"Wataru Takano","orcid":"https://orcid.org/0000-0002-4846-3552"},"institutions":[{"id":"https://openalex.org/I74801974","display_name":"The University of Tokyo","ror":"https://ror.org/057zh3y96","country_code":"JP","type":"education","lineage":["https://openalex.org/I74801974"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"Wataru Takano","raw_affiliation_strings":["Tokyo Daigaku, Bunkyo-ku, Tokyo, JP","Department of Mechano-Informatics, Graduate School of Information Science and Technology, The University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, 113-8656, Japan"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Tokyo Daigaku, Bunkyo-ku, Tokyo, JP","institution_ids":["https://openalex.org/I74801974"]},{"raw_affiliation_string":"Department of Mechano-Informatics, Graduate School of Information Science and Technology, The University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, 113-8656, Japan","institution_ids":["https://openalex.org/I74801974"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5040633710","display_name":"Yoshihiko Nakamura","orcid":"https://orcid.org/0000-0001-7162-5102"},"institutions":[{"id":"https://openalex.org/I74801974","display_name":"The University of Tokyo","ror":"https://ror.org/057zh3y96","country_code":"JP","type":"education","lineage":["https://openalex.org/I74801974"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"Yoshihiko Nakamura","raw_affiliation_strings":["Department of Mechano-Informatics, The University of Tokyo, Bunkyo-ku, Tokyo, Japan","Department of Mechano-Informatics, Graduate School of Information Science and Technology, The University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, 113-8656, Japan"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Mechano-Informatics, The University of Tokyo, Bunkyo-ku, Tokyo, Japan","institution_ids":["https://openalex.org/I74801974"]},{"raw_affiliation_string":"Department of Mechano-Informatics, Graduate School of Information Science and Technology, The University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, 113-8656, Japan","institution_ids":["https://openalex.org/I74801974"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I74801974"],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":11,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":"5","issue":null,"first_page":"3024","last_page":"3031"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11398","display_name":"Hand Gesture Recognition Systems","score":0.9998999834060669,"subfield":{"id":"https://openalex.org/subfields/1709","display_name":"Human-Computer Interaction"},"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/T11398","display_name":"Hand Gesture Recognition Systems","score":0.9998999834060669,"subfield":{"id":"https://openalex.org/subfields/1709","display_name":"Human-Computer Interaction"},"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":0.9991000294685364,"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":0.9968000054359436,"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/discriminative-model","display_name":"Discriminative model","score":0.8353163599967957},{"id":"https://openalex.org/keywords/hidden-markov-model","display_name":"Hidden Markov model","score":0.8140236139297485},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7591732740402222},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6925619840621948},{"id":"https://openalex.org/keywords/gesture-recognition","display_name":"Gesture recognition","score":0.67154461145401},{"id":"https://openalex.org/keywords/gesture","display_name":"Gesture","score":0.6333209872245789},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.6103906631469727},{"id":"https://openalex.org/keywords/support-vector-machine","display_name":"Support vector machine","score":0.6026897430419922},{"id":"https://openalex.org/keywords/generative-grammar","display_name":"Generative grammar","score":0.5196112990379333},{"id":"https://openalex.org/keywords/generative-model","display_name":"Generative model","score":0.49860620498657227},{"id":"https://openalex.org/keywords/kernel","display_name":"Kernel (algebra)","score":0.459448903799057},{"id":"https://openalex.org/keywords/speech-recognition","display_name":"Speech recognition","score":0.44718822836875916},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.3373299241065979},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.10014092922210693}],"concepts":[{"id":"https://openalex.org/C97931131","wikidata":"https://www.wikidata.org/wiki/Q5282087","display_name":"Discriminative model","level":2,"score":0.8353163599967957},{"id":"https://openalex.org/C23224414","wikidata":"https://www.wikidata.org/wiki/Q176769","display_name":"Hidden Markov model","level":2,"score":0.8140236139297485},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7591732740402222},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6925619840621948},{"id":"https://openalex.org/C159437735","wikidata":"https://www.wikidata.org/wiki/Q1519524","display_name":"Gesture recognition","level":3,"score":0.67154461145401},{"id":"https://openalex.org/C207347870","wikidata":"https://www.wikidata.org/wiki/Q371174","display_name":"Gesture","level":2,"score":0.6333209872245789},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.6103906631469727},{"id":"https://openalex.org/C12267149","wikidata":"https://www.wikidata.org/wiki/Q282453","display_name":"Support vector machine","level":2,"score":0.6026897430419922},{"id":"https://openalex.org/C39890363","wikidata":"https://www.wikidata.org/wiki/Q36108","display_name":"Generative grammar","level":2,"score":0.5196112990379333},{"id":"https://openalex.org/C167966045","wikidata":"https://www.wikidata.org/wiki/Q5532625","display_name":"Generative model","level":3,"score":0.49860620498657227},{"id":"https://openalex.org/C74193536","wikidata":"https://www.wikidata.org/wiki/Q574844","display_name":"Kernel (algebra)","level":2,"score":0.459448903799057},{"id":"https://openalex.org/C28490314","wikidata":"https://www.wikidata.org/wiki/Q189436","display_name":"Speech recognition","level":1,"score":0.44718822836875916},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3373299241065979},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.10014092922210693},{"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:10.1109/icra.2015.7139614","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icra.2015.7139614","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2015 IEEE International Conference on Robotics and Automation (ICRA)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"score":0.75,"id":"https://metadata.un.org/sdg/10","display_name":"Reduced inequalities"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":25,"referenced_works":["https://openalex.org/W46428128","https://openalex.org/W1593859739","https://openalex.org/W1846690939","https://openalex.org/W1984929054","https://openalex.org/W2015537519","https://openalex.org/W2018630598","https://openalex.org/W2096858107","https://openalex.org/W2098770944","https://openalex.org/W2125838338","https://openalex.org/W2140409019","https://openalex.org/W2151531457","https://openalex.org/W2156606946","https://openalex.org/W2163614729","https://openalex.org/W2166473218","https://openalex.org/W2185862263","https://openalex.org/W3003413895","https://openalex.org/W3141731869","https://openalex.org/W4231339208","https://openalex.org/W6601840242","https://openalex.org/W6654355120","https://openalex.org/W6675086664","https://openalex.org/W6680522077","https://openalex.org/W6682443497","https://openalex.org/W6684116732","https://openalex.org/W6686711039"],"related_works":["https://openalex.org/W4396941953","https://openalex.org/W2093104230","https://openalex.org/W2987280934","https://openalex.org/W2028966255","https://openalex.org/W2466763065","https://openalex.org/W1994032303","https://openalex.org/W4390874210","https://openalex.org/W2029299808","https://openalex.org/W4384918963","https://openalex.org/W4241564561"],"abstract_inverted_index":{"Gesture":[0],"recognition":[1,34,174,207],"is":[2,38,76,105,202],"used":[3],"for":[4],"many":[5],"practical":[6],"applications":[7],"such":[8],"as":[9,85],"human-robot":[10],"interaction,":[11],"medical":[12],"rehabilitation":[13],"and":[14,73,136,187],"sign":[15],"language.":[16],"In":[17,123],"this":[18],"paper,":[19],"we":[20,126],"apply":[21],"a":[22],"hybrid":[23,179],"generative-discriminative":[24,180],"approach":[25,43,56,129,167,181,186,191,201],"by":[26,96,108,121,130,143],"using":[27],"the":[28,33,41,54,63,86,89,92,97,109,124,144,178,183,188,193,206],"Fisher":[29],"Vector":[30,59],"to":[31,39,78,101,117,204],"improve":[32,205],"performance.":[35,208],"The":[36,66,153],"strategy":[37],"merge":[40],"generative":[42,189,194],"of":[44,57,88,91],"Hidden":[45],"Markov":[46],"Model":[47],"dealing":[48],"with":[49,53,99],"spatio-temporal":[50],"motion":[51,67],"data":[52],"discriminative":[55],"Support":[58],"Machine":[60],"focusing":[61],"on":[62,140],"classification":[64],"task.":[65],"segments":[68],"are":[69,160],"encoded":[70],"into":[71],"HMMs,":[72],"each":[74],"segment":[75,93],"converted":[77],"FV,":[79],"whose":[80],"elements":[81],"can":[82,114],"be":[83,115],"obtained":[84],"derivative":[87],"probability":[90],"being":[94],"generated":[95],"HMMs":[98,170],"respect":[100],"their":[102],"parameters.":[103],"SVM":[104],"subsequently":[106],"trained":[107],"FVs.":[110],"An":[111],"input":[112],"gesture":[113,119,158,173],"classified":[116],"corresponding":[118],"category":[120],"SVM.":[122],"experiments,":[125],"test":[127],"our":[128,200],"comparing":[131],"three":[132],"HMM":[133,185],"chain":[134],"models":[135],"four":[137],"categorization":[138],"methods":[139],"dataset":[141],"provided":[142],"ChaLearn":[145],"Looking":[146],"at":[147],"People":[148],"Challenge":[149],"2014":[150],"(LAP":[151],"2014).":[152],"results":[154],"show":[155],"that":[156],"similar":[157],"patterns":[159],"clustered":[161],"closely":[162],"in":[163],"several":[164],"categories.":[165],"Our":[166],"based":[168],"left-to-right":[169],"outperforms":[171],"other":[172],"methods.":[175],"More":[176],"specifically,":[177],"overcomes":[182,192],"standard":[184],"kernel":[190],"embedding":[195],"approach.":[196],"For":[197],"these":[198],"results,":[199],"effective":[203]},"counts_by_year":[{"year":2022,"cited_by_count":1},{"year":2020,"cited_by_count":2},{"year":2019,"cited_by_count":2},{"year":2018,"cited_by_count":1},{"year":2017,"cited_by_count":2},{"year":2016,"cited_by_count":1},{"year":2015,"cited_by_count":2}],"updated_date":"2026-07-14T23:27:15.235271","created_date":"2025-10-10T00:00:00"}
