{"id":"https://openalex.org/W2295017146","doi":"https://doi.org/10.1109/icip.2015.7351354","title":"Multi-level action detection via learning latent structure","display_name":"Multi-level action detection via learning latent structure","publication_year":2015,"publication_date":"2015-09-01","ids":{"openalex":"https://openalex.org/W2295017146","doi":"https://doi.org/10.1109/icip.2015.7351354","mag":"2295017146"},"language":"en","primary_location":{"id":"doi:10.1109/icip.2015.7351354","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icip.2015.7351354","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 Image Processing (ICIP)","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/A5058274549","display_name":"Behzad Bozorgtabar","orcid":"https://orcid.org/0000-0002-5759-4896"},"institutions":[{"id":"https://openalex.org/I188329596","display_name":"University of Canberra","ror":"https://ror.org/04s1nv328","country_code":"AU","type":"education","lineage":["https://openalex.org/I188329596"]}],"countries":["AU"],"is_corresponding":false,"raw_author_name":"Behzad Bozorgtabar","raw_affiliation_strings":["University of Canberra, Canberra, ACT, AU"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of Canberra, Canberra, ACT, AU","institution_ids":["https://openalex.org/I188329596"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5046144886","display_name":"Roland Goecke","orcid":"https://orcid.org/0000-0003-2279-7041"},"institutions":[{"id":"https://openalex.org/I118347636","display_name":"Australian National University","ror":"https://ror.org/019wvm592","country_code":"AU","type":"education","lineage":["https://openalex.org/I118347636"]},{"id":"https://openalex.org/I188329596","display_name":"University of Canberra","ror":"https://ror.org/04s1nv328","country_code":"AU","type":"education","lineage":["https://openalex.org/I188329596"]}],"countries":["AU"],"is_corresponding":false,"raw_author_name":"Roland Goecke","raw_affiliation_strings":["IHCC, RSCS, CECS, Australian National University","Vision & Sensing, HCC Lab, ESTeM University of Canberra"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"IHCC, RSCS, CECS, Australian National University","institution_ids":["https://openalex.org/I118347636"]},{"raw_affiliation_string":"Vision & Sensing, HCC Lab, ESTeM University of Canberra","institution_ids":["https://openalex.org/I188329596"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.20103198,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":"2","issue":null,"first_page":"3004","last_page":"3008"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10812","display_name":"Human Pose and Action Recognition","score":1.0,"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/T10812","display_name":"Human Pose and Action Recognition","score":1.0,"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/T11439","display_name":"Video Analysis and Summarization","score":0.995199978351593,"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/T10331","display_name":"Video Surveillance and Tracking Methods","score":0.9944000244140625,"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/discriminative-model","display_name":"Discriminative model","score":0.8127255439758301},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.723414421081543},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7189178466796875},{"id":"https://openalex.org/keywords/pooling","display_name":"Pooling","score":0.678032636642456},{"id":"https://openalex.org/keywords/classifier","display_name":"Classifier (UML)","score":0.6505596041679382},{"id":"https://openalex.org/keywords/action-recognition","display_name":"Action recognition","score":0.5802094340324402},{"id":"https://openalex.org/keywords/feature-learning","display_name":"Feature learning","score":0.5086768269538879},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.49835991859436035},{"id":"https://openalex.org/keywords/graph","display_name":"Graph","score":0.4851754307746887},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.48095837235450745},{"id":"https://openalex.org/keywords/feature-vector","display_name":"Feature vector","score":0.44714483618736267},{"id":"https://openalex.org/keywords/bag-of-words-model","display_name":"Bag-of-words model","score":0.4158877432346344},{"id":"https://openalex.org/keywords/class","display_name":"Class (philosophy)","score":0.23466536402702332},{"id":"https://openalex.org/keywords/theoretical-computer-science","display_name":"Theoretical computer science","score":0.10402581095695496}],"concepts":[{"id":"https://openalex.org/C97931131","wikidata":"https://www.wikidata.org/wiki/Q5282087","display_name":"Discriminative model","level":2,"score":0.8127255439758301},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.723414421081543},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7189178466796875},{"id":"https://openalex.org/C70437156","wikidata":"https://www.wikidata.org/wiki/Q7228652","display_name":"Pooling","level":2,"score":0.678032636642456},{"id":"https://openalex.org/C95623464","wikidata":"https://www.wikidata.org/wiki/Q1096149","display_name":"Classifier (UML)","level":2,"score":0.6505596041679382},{"id":"https://openalex.org/C2987834672","wikidata":"https://www.wikidata.org/wiki/Q4677630","display_name":"Action recognition","level":3,"score":0.5802094340324402},{"id":"https://openalex.org/C59404180","wikidata":"https://www.wikidata.org/wiki/Q17013334","display_name":"Feature learning","level":2,"score":0.5086768269538879},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.49835991859436035},{"id":"https://openalex.org/C132525143","wikidata":"https://www.wikidata.org/wiki/Q141488","display_name":"Graph","level":2,"score":0.4851754307746887},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.48095837235450745},{"id":"https://openalex.org/C83665646","wikidata":"https://www.wikidata.org/wiki/Q42139305","display_name":"Feature vector","level":2,"score":0.44714483618736267},{"id":"https://openalex.org/C13672336","wikidata":"https://www.wikidata.org/wiki/Q3460803","display_name":"Bag-of-words model","level":2,"score":0.4158877432346344},{"id":"https://openalex.org/C2777212361","wikidata":"https://www.wikidata.org/wiki/Q5127848","display_name":"Class (philosophy)","level":2,"score":0.23466536402702332},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.10402581095695496}],"mesh":[],"locations_count":3,"locations":[{"id":"doi:10.1109/icip.2015.7351354","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icip.2015.7351354","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 Image Processing (ICIP)","raw_type":"proceedings-article"},{"id":"pmh:oai:CiteSeerX.psu:10.1.1.721.2608","is_oa":false,"landing_page_url":"http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.721.2608","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"http://staff.estem-uc.edu.au/roland/files/2009/05/Bozorgtabar_Goecke_ICIP2015_Multi-LevelActionDetectionViaLearningLatentStructure.pdf","raw_type":"text"},{"id":"pmh:tle:f3c21822-0037-4be6-a4c7-90b2e771fe73:1f071524-0ffb-45dd-b0fd-e2ac8222ffc2:1","is_oa":false,"landing_page_url":"http://www.canberra.edu.au/researchrepository/items/f3c21822-0037-4be6-a4c7-90b2e771fe73/1/","pdf_url":null,"source":{"id":"https://openalex.org/S7407050591","display_name":"University of Canberra Research Portal","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"","raw_type":"Conference Paper"}],"best_oa_location":null,"sustainable_development_goals":[{"display_name":"Reduced inequalities","id":"https://metadata.un.org/sdg/10","score":0.7099999785423279}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":19,"referenced_works":["https://openalex.org/W1984630281","https://openalex.org/W2003543060","https://openalex.org/W2034328688","https://openalex.org/W2055753778","https://openalex.org/W2063153269","https://openalex.org/W2068611653","https://openalex.org/W2095661305","https://openalex.org/W2097342496","https://openalex.org/W2105101328","https://openalex.org/W2113321810","https://openalex.org/W2121947440","https://openalex.org/W2123745357","https://openalex.org/W2131311058","https://openalex.org/W2139893168","https://openalex.org/W2147102238","https://openalex.org/W2164918853","https://openalex.org/W4249279051","https://openalex.org/W6678853083","https://openalex.org/W6681785522"],"related_works":["https://openalex.org/W3032932973","https://openalex.org/W2144899925","https://openalex.org/W2786094008","https://openalex.org/W3131501806","https://openalex.org/W2304061952","https://openalex.org/W2799683370","https://openalex.org/W2807745940","https://openalex.org/W2417477528","https://openalex.org/W2367773024","https://openalex.org/W3037610354"],"abstract_inverted_index":{"Detecting":[0],"actions":[1],"in":[2,27,49,94,202],"videos":[3],"is":[4],"still":[5],"a":[6,24,38,67,77,144],"demanding":[7],"task":[8],"due":[9],"to":[10,65,123,139],"large":[11],"intra-class":[12],"variation":[13],"caused":[14],"by":[15,36],"varying":[16],"pose,":[17],"motion":[18,32,46,128],"and":[19,112,130,183,188],"scales.":[20],"Conventional":[21],"approaches":[22],"use":[23],"Bag-of-Words":[25],"model":[26,68,147,159,168],"the":[28,42,53,70,99,106,116,125,153,161,166,171,192,195],"form":[29],"of":[30,52,69,74,105,118,194],"space-time":[31,84],"feature":[33],"pooling":[34],"followed":[35],"learning":[37,98],"classifier.":[39],"However,":[40],"since":[41],"informative":[43],"body":[44,90,135],"parts":[45,91,136],"only":[47],"appear":[48],"specific":[50],"regions":[51,73],"body,":[54],"these":[55,119],"methods":[56,201],"have":[57],"limited":[58],"capability.":[59],"In":[60,121],"this":[61,203],"paper,":[62],"we":[63,108,142],"seek":[64],"learn":[66],"interaction":[71],"among":[72],"interest":[75],"via":[76,97],"graph":[78,101],"structure.":[79],"We":[80],"first":[81],"discover":[82],"several":[83],"video":[85,163],"segments":[86],"representing":[87],"persistent":[88],"moving":[89],"observed":[92],"sparsely":[93],"video.":[95],"Then,":[96],"hidden":[100],"structure":[102],"(a":[103],"subset":[104],"graph),":[107],"identify":[109],"both":[110],"spatial":[111],"temporal":[113],"relations":[114],"between":[115,134],"subsets":[117],"segments.":[120],"order":[122],"seize":[124],"more":[126],"discriminative":[127],"patterns":[129],"handle":[131],"different":[132],"interactions":[133],"from":[137],"simple":[138],"composite":[140],"action,":[141],"present":[143],"multi-level":[145],"action":[146,151,158,167],"representation.":[148],"Consequently,":[149],"for":[150],"classification,":[152],"classifier":[154],"learned":[155],"through":[156],"each":[157],"labels":[160],"test":[162],"based":[164],"on":[165,176],"that":[169,198],"gives":[170],"highest":[172],"probability":[173],"score.":[174],"Experiments":[175],"challenging":[177],"datasets,":[178],"such":[179],"as":[180],"MSR":[181],"II":[182],"UCF-Sports":[184],"including":[185],"complex":[186],"motions":[187],"dynamic":[189],"backgrounds,":[190],"demonstrate":[191],"effectiveness":[193],"proposed":[196],"approach":[197],"outperforms":[199],"state-of-the-art":[200],"context.":[204]},"counts_by_year":[],"updated_date":"2026-07-29T14:22:42.915294","created_date":"2025-10-10T00:00:00"}
