{"id":"https://openalex.org/W2963952934","doi":"https://doi.org/10.1109/fskd.2017.8393364","title":"Synchronous prediction of arousal and valence using LSTM network for affective video content analysis","display_name":"Synchronous prediction of arousal and valence using LSTM network for affective video content analysis","publication_year":2017,"publication_date":"2017-07-01","ids":{"openalex":"https://openalex.org/W2963952934","doi":"https://doi.org/10.1109/fskd.2017.8393364","mag":"2963952934"},"language":"en","primary_location":{"id":"doi:10.1109/fskd.2017.8393364","is_oa":false,"landing_page_url":"https://doi.org/10.1109/fskd.2017.8393364","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2017 13th International Conference on Natural Computation, Fuzzy Systems and Knowledge Discovery (ICNC-FSKD)","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/A5100778318","display_name":"Ligang Zhang","orcid":"https://orcid.org/0000-0001-6925-9086"},"institutions":[{"id":"https://openalex.org/I4210131919","display_name":"Xi'an University of Technology","ror":"https://ror.org/038avdt50","country_code":"CN","type":"education","lineage":["https://openalex.org/I4210131919"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Ligang Zhang","raw_affiliation_strings":["School of Computer and Engineering, Xi'an University of Technology, Xi'an, China"],"affiliations":[{"raw_affiliation_string":"School of Computer and Engineering, Xi'an University of Technology, Xi'an, China","institution_ids":["https://openalex.org/I4210131919"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5006226605","display_name":"Jiulong Zhang","orcid":null},"institutions":[{"id":"https://openalex.org/I4210131919","display_name":"Xi'an University of Technology","ror":"https://ror.org/038avdt50","country_code":"CN","type":"education","lineage":["https://openalex.org/I4210131919"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Jiulong Zhang","raw_affiliation_strings":["School of Computer and Engineering, Xi'an University of Technology, Xi'an, China"],"affiliations":[{"raw_affiliation_string":"School of Computer and Engineering, Xi'an University of Technology, Xi'an, China","institution_ids":["https://openalex.org/I4210131919"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5100778318"],"corresponding_institution_ids":["https://openalex.org/I4210131919"],"apc_list":null,"apc_paid":null,"fwci":1.741,"has_fulltext":false,"cited_by_count":12,"citation_normalized_percentile":{"value":0.85567802,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":98},"biblio":{"volume":null,"issue":null,"first_page":"727","last_page":"732"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10667","display_name":"Emotion and Mood Recognition","score":0.9965999722480774,"subfield":{"id":"https://openalex.org/subfields/3205","display_name":"Experimental and Cognitive Psychology"},"field":{"id":"https://openalex.org/fields/32","display_name":"Psychology"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}},"topics":[{"id":"https://openalex.org/T10667","display_name":"Emotion and Mood Recognition","score":0.9965999722480774,"subfield":{"id":"https://openalex.org/subfields/3205","display_name":"Experimental and Cognitive Psychology"},"field":{"id":"https://openalex.org/fields/32","display_name":"Psychology"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}},{"id":"https://openalex.org/T11165","display_name":"Image and Video Quality Assessment","score":0.9861999750137329,"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/T12496","display_name":"Color perception and design","score":0.9782000184059143,"subfield":{"id":"https://openalex.org/subfields/3207","display_name":"Social Psychology"},"field":{"id":"https://openalex.org/fields/32","display_name":"Psychology"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/valence","display_name":"Valence (chemistry)","score":0.8499293327331543},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7446557879447937},{"id":"https://openalex.org/keywords/arousal","display_name":"Arousal","score":0.7317521572113037},{"id":"https://openalex.org/keywords/video-content-analysis","display_name":"Video content analysis","score":0.6634328365325928},{"id":"https://openalex.org/keywords/support-vector-machine","display_name":"Support vector machine","score":0.6013043522834778},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5102479457855225},{"id":"https://openalex.org/keywords/affective-computing","display_name":"Affective computing","score":0.4954594373703003},{"id":"https://openalex.org/keywords/feature-extraction","display_name":"Feature extraction","score":0.4351264238357544},{"id":"https://openalex.org/keywords/perception","display_name":"Perception","score":0.42440712451934814},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.35016268491744995},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.3213252127170563},{"id":"https://openalex.org/keywords/video-processing","display_name":"Video processing","score":0.18788307905197144},{"id":"https://openalex.org/keywords/video-tracking","display_name":"Video tracking","score":0.11401176452636719},{"id":"https://openalex.org/keywords/psychology","display_name":"Psychology","score":0.09860625863075256}],"concepts":[{"id":"https://openalex.org/C168900304","wikidata":"https://www.wikidata.org/wiki/Q171407","display_name":"Valence (chemistry)","level":2,"score":0.8499293327331543},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7446557879447937},{"id":"https://openalex.org/C36951298","wikidata":"https://www.wikidata.org/wiki/Q379784","display_name":"Arousal","level":2,"score":0.7317521572113037},{"id":"https://openalex.org/C2778598663","wikidata":"https://www.wikidata.org/wiki/Q1407599","display_name":"Video content analysis","level":4,"score":0.6634328365325928},{"id":"https://openalex.org/C12267149","wikidata":"https://www.wikidata.org/wiki/Q282453","display_name":"Support vector machine","level":2,"score":0.6013043522834778},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5102479457855225},{"id":"https://openalex.org/C6438553","wikidata":"https://www.wikidata.org/wiki/Q1185804","display_name":"Affective computing","level":2,"score":0.4954594373703003},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.4351264238357544},{"id":"https://openalex.org/C26760741","wikidata":"https://www.wikidata.org/wiki/Q160402","display_name":"Perception","level":2,"score":0.42440712451934814},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.35016268491744995},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.3213252127170563},{"id":"https://openalex.org/C65483669","wikidata":"https://www.wikidata.org/wiki/Q3536669","display_name":"Video processing","level":2,"score":0.18788307905197144},{"id":"https://openalex.org/C202474056","wikidata":"https://www.wikidata.org/wiki/Q1931635","display_name":"Video tracking","level":3,"score":0.11401176452636719},{"id":"https://openalex.org/C15744967","wikidata":"https://www.wikidata.org/wiki/Q9418","display_name":"Psychology","level":0,"score":0.09860625863075256},{"id":"https://openalex.org/C62520636","wikidata":"https://www.wikidata.org/wiki/Q944","display_name":"Quantum mechanics","level":1,"score":0.0},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0},{"id":"https://openalex.org/C169760540","wikidata":"https://www.wikidata.org/wiki/Q207011","display_name":"Neuroscience","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/fskd.2017.8393364","is_oa":false,"landing_page_url":"https://doi.org/10.1109/fskd.2017.8393364","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2017 13th International Conference on Natural Computation, Fuzzy Systems and Knowledge Discovery (ICNC-FSKD)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":21,"referenced_works":["https://openalex.org/W78522148","https://openalex.org/W1501669607","https://openalex.org/W1560107318","https://openalex.org/W2003856922","https://openalex.org/W2030936037","https://openalex.org/W2061942331","https://openalex.org/W2064675550","https://openalex.org/W2075953807","https://openalex.org/W2088951041","https://openalex.org/W2099965794","https://openalex.org/W2149628368","https://openalex.org/W2167854178","https://openalex.org/W2173163709","https://openalex.org/W2278113816","https://openalex.org/W2293617031","https://openalex.org/W2416812335","https://openalex.org/W2422171769","https://openalex.org/W2580719207","https://openalex.org/W2613550941","https://openalex.org/W6732434907","https://openalex.org/W6737711710"],"related_works":["https://openalex.org/W2029072726","https://openalex.org/W91913183","https://openalex.org/W2936882366","https://openalex.org/W2736893848","https://openalex.org/W2128698257","https://openalex.org/W1544055438","https://openalex.org/W3003450285","https://openalex.org/W2013608943","https://openalex.org/W4399628019","https://openalex.org/W4310841718"],"abstract_inverted_index":{"The":[0,77,124,198],"affect":[1],"embedded":[2],"in":[3],"video":[4,42,136,175],"data":[5,137],"conveys":[6],"high-level":[7],"semantic":[8],"information":[9,93],"about":[10],"the":[11,18,45,59,117,150,156,165,168,193,203,208],"content":[12,43],"and":[13,20,44,52,112,132,138,146,160,185,187],"has":[14],"direct":[15,39],"impact":[16],"on":[17,171],"understanding":[19],"perception":[21],"of":[22,97,129,144,167,202,210],"reviewers,":[23],"as":[24,26,50,72,182],"well":[25],"their":[27],"emotional":[28],"responses.":[29],"Affective":[30],"Video":[31],"Content":[32],"Analysis":[33],"(AVCA)":[34],"attempts":[35],"to":[36,90,109,207],"generate":[37],"a":[38,127,172],"mapping":[40,60],"between":[41,80,158,213],"corresponding":[46],"affective":[47,81,98,214],"states":[48],"such":[49,71,181],"arousal":[51,111,145,159],"valence":[53,147],"dimensions.":[54,99,162],"Most":[55],"existing":[56],"studies":[57],"establish":[58],"for":[61,94],"each":[62],"dimension":[63],"separately":[64],"using":[65,116,149],"knowledge-based":[66],"rules":[67],"or":[68],"traditional":[69,194],"classifiers":[70],"Support":[73],"Vector":[74],"Machine":[75],"(SVM).":[76],"inherent":[78],"correlations":[79,157,212],"dimensions":[82,114,215],"have":[83],"largely":[84],"been":[85],"unexploited,":[86],"which":[87,153],"are":[88],"anticipated":[89],"include":[91],"important":[92],"accurate":[95,217],"prediction":[96],"To":[100],"address":[101],"this":[102,104],"issue,":[103],"paper":[105],"presents":[106],"an":[107],"approach":[108,125,170],"predict":[110],"valance":[113,161],"synchronously":[115,141],"Long":[118],"Short":[119],"Term":[120],"Memory":[121],"(LSTM)":[122],"network.":[123],"extracts":[126],"set":[128],"low-level":[130],"audio":[131],"visual":[133],"features":[134],"from":[135,178],"projects":[139],"them":[140],"into":[142],"pairs":[143],"values":[148],"LSTM":[151],"network":[152],"automatically":[154],"incorporates":[155],"We":[163],"evaluate":[164],"performance":[166,191],"proposed":[169],"dataset":[173],"comprising":[174],"clips":[176],"segmented":[177],"real-world":[179],"resources":[180],"film,":[183],"drama,":[184],"news,":[186],"demonstrate":[188],"its":[189],"superior":[190],"over":[192],"SVM":[195],"based":[196],"method.":[197],"results":[199],"provide":[200],"one":[201],"earliest":[204],"preliminary":[205],"evidence":[206],"benefit":[209],"considering":[211],"towards":[216],"AVCA.":[218]},"counts_by_year":[{"year":2025,"cited_by_count":1},{"year":2024,"cited_by_count":1},{"year":2022,"cited_by_count":2},{"year":2020,"cited_by_count":6},{"year":2019,"cited_by_count":1},{"year":2018,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}