{"id":"https://openalex.org/W1521773411","doi":"https://doi.org/10.1109/icip.2003.1246755","title":"Video sequence modeling by dynamic Bayesian networks: a systematic approach from coarse-to-fine grains","display_name":"Video sequence modeling by dynamic Bayesian networks: a systematic approach from coarse-to-fine grains","publication_year":2004,"publication_date":"2004-06-03","ids":{"openalex":"https://openalex.org/W1521773411","doi":"https://doi.org/10.1109/icip.2003.1246755","mag":"1521773411"},"language":"en","primary_location":{"id":"doi:10.1109/icip.2003.1246755","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icip.2003.1246755","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings 2003 International Conference on Image Processing (Cat. No.03CH37429)","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/A5101420290","display_name":"Ying Luo","orcid":"https://orcid.org/0000-0003-2380-1567"},"institutions":[{"id":"https://openalex.org/I201448701","display_name":"University of Washington","ror":"https://ror.org/00cvxb145","country_code":"US","type":"education","lineage":["https://openalex.org/I201448701"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Y. Luo","raw_affiliation_strings":["Information Processing Laboratory Department of Electrical Engineering, University of Washington, Seattle, WA, USA","[Dept. of Electr. Eng., Washington Univ., Seattle, WA, USA]"],"affiliations":[{"raw_affiliation_string":"Information Processing Laboratory Department of Electrical Engineering, University of Washington, Seattle, WA, USA","institution_ids":["https://openalex.org/I201448701"]},{"raw_affiliation_string":"[Dept. of Electr. Eng., Washington Univ., Seattle, WA, USA]","institution_ids":["https://openalex.org/I201448701"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5101702810","display_name":"Jenq\u2013Neng Hwang","orcid":"https://orcid.org/0000-0002-8877-2421"},"institutions":[{"id":"https://openalex.org/I201448701","display_name":"University of Washington","ror":"https://ror.org/00cvxb145","country_code":"US","type":"education","lineage":["https://openalex.org/I201448701"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"J.-N. Hwang","raw_affiliation_strings":["Information Processing Laboratory Department of Electrical Engineering, University of Washington, Seattle, WA, USA","[Dept. of Electr. Eng., Washington Univ., Seattle, WA, USA]"],"affiliations":[{"raw_affiliation_string":"Information Processing Laboratory Department of Electrical Engineering, University of Washington, Seattle, WA, USA","institution_ids":["https://openalex.org/I201448701"]},{"raw_affiliation_string":"[Dept. of Electr. Eng., Washington Univ., Seattle, WA, USA]","institution_ids":["https://openalex.org/I201448701"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5101420290"],"corresponding_institution_ids":["https://openalex.org/I201448701"],"apc_list":null,"apc_paid":null,"fwci":0.5466,"has_fulltext":false,"cited_by_count":7,"citation_normalized_percentile":{"value":0.66757525,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":94},"biblio":{"volume":"3","issue":null,"first_page":"II","last_page":"615"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11439","display_name":"Video Analysis and Summarization","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/T11439","display_name":"Video Analysis and Summarization","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/T10812","display_name":"Human Pose and Action Recognition","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/T10531","display_name":"Advanced Vision and Imaging","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"}}],"keywords":[{"id":"https://openalex.org/keywords/dynamic-bayesian-network","display_name":"Dynamic Bayesian network","score":0.8059936165809631},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7836213111877441},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6916407346725464},{"id":"https://openalex.org/keywords/hidden-markov-model","display_name":"Hidden Markov model","score":0.6527484655380249},{"id":"https://openalex.org/keywords/sequence","display_name":"Sequence (biology)","score":0.5645931959152222},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.5019090175628662},{"id":"https://openalex.org/keywords/video-tracking","display_name":"Video tracking","score":0.46857357025146484},{"id":"https://openalex.org/keywords/bayesian-network","display_name":"Bayesian network","score":0.4627779424190521},{"id":"https://openalex.org/keywords/flexibility","display_name":"Flexibility (engineering)","score":0.42438846826553345},{"id":"https://openalex.org/keywords/shot","display_name":"Shot (pellet)","score":0.4219367504119873},{"id":"https://openalex.org/keywords/bayesian-probability","display_name":"Bayesian probability","score":0.41572305560112},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.4067448377609253},{"id":"https://openalex.org/keywords/object","display_name":"Object (grammar)","score":0.36166656017303467},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.07686328887939453}],"concepts":[{"id":"https://openalex.org/C82142266","wikidata":"https://www.wikidata.org/wiki/Q3456604","display_name":"Dynamic Bayesian network","level":3,"score":0.8059936165809631},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7836213111877441},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6916407346725464},{"id":"https://openalex.org/C23224414","wikidata":"https://www.wikidata.org/wiki/Q176769","display_name":"Hidden Markov model","level":2,"score":0.6527484655380249},{"id":"https://openalex.org/C2778112365","wikidata":"https://www.wikidata.org/wiki/Q3511065","display_name":"Sequence (biology)","level":2,"score":0.5645931959152222},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.5019090175628662},{"id":"https://openalex.org/C202474056","wikidata":"https://www.wikidata.org/wiki/Q1931635","display_name":"Video tracking","level":3,"score":0.46857357025146484},{"id":"https://openalex.org/C33724603","wikidata":"https://www.wikidata.org/wiki/Q812540","display_name":"Bayesian network","level":2,"score":0.4627779424190521},{"id":"https://openalex.org/C2780598303","wikidata":"https://www.wikidata.org/wiki/Q65921492","display_name":"Flexibility (engineering)","level":2,"score":0.42438846826553345},{"id":"https://openalex.org/C2778344882","wikidata":"https://www.wikidata.org/wiki/Q278938","display_name":"Shot (pellet)","level":2,"score":0.4219367504119873},{"id":"https://openalex.org/C107673813","wikidata":"https://www.wikidata.org/wiki/Q812534","display_name":"Bayesian probability","level":2,"score":0.41572305560112},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.4067448377609253},{"id":"https://openalex.org/C2781238097","wikidata":"https://www.wikidata.org/wiki/Q175026","display_name":"Object (grammar)","level":2,"score":0.36166656017303467},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.07686328887939453},{"id":"https://openalex.org/C86803240","wikidata":"https://www.wikidata.org/wiki/Q420","display_name":"Biology","level":0,"score":0.0},{"id":"https://openalex.org/C178790620","wikidata":"https://www.wikidata.org/wiki/Q11351","display_name":"Organic chemistry","level":1,"score":0.0},{"id":"https://openalex.org/C54355233","wikidata":"https://www.wikidata.org/wiki/Q7162","display_name":"Genetics","level":1,"score":0.0},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.0},{"id":"https://openalex.org/C185592680","wikidata":"https://www.wikidata.org/wiki/Q2329","display_name":"Chemistry","level":0,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/icip.2003.1246755","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icip.2003.1246755","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings 2003 International Conference on Image Processing (Cat. No.03CH37429)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":17,"referenced_works":["https://openalex.org/W175821084","https://openalex.org/W1523411603","https://openalex.org/W1585440265","https://openalex.org/W1636244751","https://openalex.org/W1986082156","https://openalex.org/W2102188949","https://openalex.org/W2110575115","https://openalex.org/W2115439269","https://openalex.org/W2138043589","https://openalex.org/W2145176096","https://openalex.org/W2150674727","https://openalex.org/W2155172503","https://openalex.org/W2158708335","https://openalex.org/W6675348527","https://openalex.org/W6676404962","https://openalex.org/W6676852178","https://openalex.org/W6682275800"],"related_works":["https://openalex.org/W2017210410","https://openalex.org/W1993009522","https://openalex.org/W1995792634","https://openalex.org/W2135672910","https://openalex.org/W2578973671","https://openalex.org/W2215058820","https://openalex.org/W2945000716","https://openalex.org/W2097663773","https://openalex.org/W1602184117","https://openalex.org/W2511198839"],"abstract_inverted_index":{"A":[0],"dynamic":[1],"Bayesian":[2],"network":[3],"(DBN)":[4],"based":[5,82,109,114],"framework":[6,81],"to":[7,57,61],"model":[8,92],"video":[9,14,20,28,41,74,79,98,102,135],"sequences":[10,15,21,29,42],"is":[11,69,94],"proposed.":[12],"The":[13,64,120],"of":[16,36,40,66,86,118,131],"interest":[17],"include":[18],"single-shot":[19,101],"containing":[22,30],"only":[23],"one":[24,84],"event":[25],"and":[26,43,111,129],"multishot":[27,73,97],"various":[31],"events.":[32],"By":[33],"taking":[34],"advantage":[35],"the":[37,55,88,127],"temporal":[38],"continuity":[39],"assuming":[44],"Markovian":[45],"property":[46],"between":[47],"successive":[48],"image":[49],"frames,":[50],"we":[51,104],"propose":[52],"DBNs":[53,133],"as":[54],"tool":[56],"map":[58],"low-level":[59],"features":[60],"high-level":[62],"concepts.":[63],"feasibility":[65],"DBN":[67],"modeling":[68],"tested":[70],"on":[71,83,115,126],"single-and":[72],"sequences.":[75,99],"Specifically,":[76],"a":[77,106],"coarse-grained":[78],"interpretation":[80,110],"kind":[85],"DBN,":[87],"hierarchical":[89],"hidden":[90],"Markov":[91],"(HHMM),":[93],"proposed":[95],"for":[96,134],"For":[100],"sequences,":[103],"present":[105],"fine-grained":[107],"object":[108],"classification":[112],"system":[113],"another":[116],"version":[117],"DBNs.":[119],"preliminary":[121],"simulations":[122],"show":[123],"great":[124],"promise":[125],"efficiency":[128],"flexibility":[130],"using":[132],"sequence":[136],"modeling.":[137]},"counts_by_year":[{"year":2012,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}