{"id":"https://openalex.org/W2131351121","doi":"https://doi.org/10.1109/cvpr.2015.7299137","title":"Space-time tree ensemble for action recognition","display_name":"Space-time tree ensemble for action recognition","publication_year":2015,"publication_date":"2015-06-01","ids":{"openalex":"https://openalex.org/W2131351121","doi":"https://doi.org/10.1109/cvpr.2015.7299137","mag":"2131351121"},"language":"en","primary_location":{"id":"doi:10.1109/cvpr.2015.7299137","is_oa":false,"landing_page_url":"https://doi.org/10.1109/cvpr.2015.7299137","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)","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/A5032460666","display_name":"Shugao Ma","orcid":"https://orcid.org/0000-0002-4986-2221"},"institutions":[{"id":"https://openalex.org/I111088046","display_name":"Boston University","ror":"https://ror.org/05qwgg493","country_code":"US","type":"education","lineage":["https://openalex.org/I111088046"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Shugao Ma","raw_affiliation_strings":["Boston University","[Boston University, USA]"],"affiliations":[{"raw_affiliation_string":"Boston University","institution_ids":[]},{"raw_affiliation_string":"[Boston University, USA]","institution_ids":["https://openalex.org/I111088046"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5053011888","display_name":"Leonid Sigal","orcid":"https://orcid.org/0000-0002-3942-2804"},"institutions":[{"id":"https://openalex.org/I4210142140","display_name":"Walt Disney (United States)","ror":"https://ror.org/04eg47h42","country_code":"US","type":"company","lineage":["https://openalex.org/I4210142140"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Leonid Sigal","raw_affiliation_strings":["Disney Research","Disney Research USA"],"affiliations":[{"raw_affiliation_string":"Disney Research","institution_ids":["https://openalex.org/I4210142140"]},{"raw_affiliation_string":"Disney Research USA","institution_ids":["https://openalex.org/I4210142140"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5042663603","display_name":"Stan Sclaroff","orcid":"https://orcid.org/0000-0002-0711-4313"},"institutions":[{"id":"https://openalex.org/I111088046","display_name":"Boston University","ror":"https://ror.org/05qwgg493","country_code":"US","type":"education","lineage":["https://openalex.org/I111088046"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Stan Sclaroff","raw_affiliation_strings":["Bostron University","[Boston University, USA]"],"affiliations":[{"raw_affiliation_string":"Bostron University","institution_ids":[]},{"raw_affiliation_string":"[Boston University, USA]","institution_ids":["https://openalex.org/I111088046"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5032460666"],"corresponding_institution_ids":["https://openalex.org/I111088046"],"apc_list":null,"apc_paid":null,"fwci":6.6274,"has_fulltext":false,"cited_by_count":45,"citation_normalized_percentile":{"value":0.97817494,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":89,"max":100},"biblio":{"volume":null,"issue":null,"first_page":"5024","last_page":"5032"},"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/T11512","display_name":"Anomaly Detection Techniques and Applications","score":0.9936000108718872,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"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":0.9878000020980835,"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":0.833622932434082},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.722449004650116},{"id":"https://openalex.org/keywords/tree","display_name":"Tree (set theory)","score":0.6578010320663452},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6377590298652649},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.5820860266685486},{"id":"https://openalex.org/keywords/tree-structure","display_name":"Tree structure","score":0.508735716342926},{"id":"https://openalex.org/keywords/cluster-analysis","display_name":"Cluster analysis","score":0.5078151822090149},{"id":"https://openalex.org/keywords/vocabulary","display_name":"Vocabulary","score":0.48952019214630127},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.4783287048339844},{"id":"https://openalex.org/keywords/generality","display_name":"Generality","score":0.46657928824424744},{"id":"https://openalex.org/keywords/hierarchical-clustering","display_name":"Hierarchical clustering","score":0.4552716910839081},{"id":"https://openalex.org/keywords/set","display_name":"Set (abstract data type)","score":0.425595223903656},{"id":"https://openalex.org/keywords/ranking","display_name":"Ranking (information retrieval)","score":0.41317492723464966},{"id":"https://openalex.org/keywords/data-structure","display_name":"Data structure","score":0.18317362666130066},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.15944835543632507}],"concepts":[{"id":"https://openalex.org/C97931131","wikidata":"https://www.wikidata.org/wiki/Q5282087","display_name":"Discriminative model","level":2,"score":0.833622932434082},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.722449004650116},{"id":"https://openalex.org/C113174947","wikidata":"https://www.wikidata.org/wiki/Q2859736","display_name":"Tree (set theory)","level":2,"score":0.6578010320663452},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6377590298652649},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.5820860266685486},{"id":"https://openalex.org/C163797641","wikidata":"https://www.wikidata.org/wiki/Q2067937","display_name":"Tree structure","level":3,"score":0.508735716342926},{"id":"https://openalex.org/C73555534","wikidata":"https://www.wikidata.org/wiki/Q622825","display_name":"Cluster analysis","level":2,"score":0.5078151822090149},{"id":"https://openalex.org/C2777601683","wikidata":"https://www.wikidata.org/wiki/Q6499736","display_name":"Vocabulary","level":2,"score":0.48952019214630127},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.4783287048339844},{"id":"https://openalex.org/C2780767217","wikidata":"https://www.wikidata.org/wiki/Q5532421","display_name":"Generality","level":2,"score":0.46657928824424744},{"id":"https://openalex.org/C92835128","wikidata":"https://www.wikidata.org/wiki/Q1277447","display_name":"Hierarchical clustering","level":3,"score":0.4552716910839081},{"id":"https://openalex.org/C177264268","wikidata":"https://www.wikidata.org/wiki/Q1514741","display_name":"Set (abstract data type)","level":2,"score":0.425595223903656},{"id":"https://openalex.org/C189430467","wikidata":"https://www.wikidata.org/wiki/Q7293293","display_name":"Ranking (information retrieval)","level":2,"score":0.41317492723464966},{"id":"https://openalex.org/C162319229","wikidata":"https://www.wikidata.org/wiki/Q175263","display_name":"Data structure","level":2,"score":0.18317362666130066},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.15944835543632507},{"id":"https://openalex.org/C542102704","wikidata":"https://www.wikidata.org/wiki/Q183257","display_name":"Psychotherapist","level":1,"score":0.0},{"id":"https://openalex.org/C41895202","wikidata":"https://www.wikidata.org/wiki/Q8162","display_name":"Linguistics","level":1,"score":0.0},{"id":"https://openalex.org/C138885662","wikidata":"https://www.wikidata.org/wiki/Q5891","display_name":"Philosophy","level":0,"score":0.0},{"id":"https://openalex.org/C134306372","wikidata":"https://www.wikidata.org/wiki/Q7754","display_name":"Mathematical analysis","level":1,"score":0.0},{"id":"https://openalex.org/C199360897","wikidata":"https://www.wikidata.org/wiki/Q9143","display_name":"Programming language","level":1,"score":0.0},{"id":"https://openalex.org/C15744967","wikidata":"https://www.wikidata.org/wiki/Q9418","display_name":"Psychology","level":0,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/cvpr.2015.7299137","is_oa":false,"landing_page_url":"https://doi.org/10.1109/cvpr.2015.7299137","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/10","display_name":"Reduced inequalities","score":0.7300000190734863}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":46,"referenced_works":["https://openalex.org/W68234866","https://openalex.org/W337346652","https://openalex.org/W410625161","https://openalex.org/W1596155328","https://openalex.org/W1884141828","https://openalex.org/W1970259423","https://openalex.org/W1979545636","https://openalex.org/W1986254198","https://openalex.org/W1992208818","https://openalex.org/W2003088946","https://openalex.org/W2016387968","https://openalex.org/W2019725986","https://openalex.org/W2025508903","https://openalex.org/W2055753778","https://openalex.org/W2063153269","https://openalex.org/W2068611653","https://openalex.org/W2080282247","https://openalex.org/W2094016507","https://openalex.org/W2095661305","https://openalex.org/W2099806072","https://openalex.org/W2101194540","https://openalex.org/W2105101328","https://openalex.org/W2111885008","https://openalex.org/W2113321810","https://openalex.org/W2114623790","https://openalex.org/W2118585731","https://openalex.org/W2131042978","https://openalex.org/W2131311058","https://openalex.org/W2133728753","https://openalex.org/W2134988438","https://openalex.org/W2137275576","https://openalex.org/W2140978750","https://openalex.org/W2142194269","https://openalex.org/W2157791002","https://openalex.org/W2162670331","https://openalex.org/W2165232124","https://openalex.org/W2166070055","https://openalex.org/W2168311572","https://openalex.org/W6611559973","https://openalex.org/W6614097952","https://openalex.org/W6635744257","https://openalex.org/W6642633109","https://openalex.org/W6677656871","https://openalex.org/W6679761776","https://openalex.org/W6680050993","https://openalex.org/W6680698335"],"related_works":["https://openalex.org/W2045049461","https://openalex.org/W1978893398","https://openalex.org/W2201908702","https://openalex.org/W4381094582","https://openalex.org/W2369625323","https://openalex.org/W2364579609","https://openalex.org/W1977906818","https://openalex.org/W2898752095","https://openalex.org/W2079000291","https://openalex.org/W3035714756"],"abstract_inverted_index":{"Human":[0],"actions":[1],"are,":[2],"inherently,":[3],"structured":[4],"patterns":[5,111],"of":[6,12,41,94,151],"body":[7],"movements.":[8],"We":[9,61,99],"explore":[10],"ensembles":[11],"hierarchical":[13,29],"spatio-temporal":[14,30],"trees,":[15],"discovered":[16],"directly":[17],"from":[18],"training":[19],"data,":[20],"to":[21,50,89],"model":[22],"these":[23,102],"structures":[24,46],"for":[25,37,148],"action":[26,69,76,138],"recognition.":[27],"The":[28],"trees":[31,129,153],"provide":[32],"a":[33,67,91,141],"robust":[34],"mid-level":[35],"representation":[36],"actions.":[38],"However,":[39],"discovery":[40],"frequent":[42],"and":[43,87,114,126],"discriminative":[44,72,96],"tree":[45,81,85,97,103],"is":[47],"challenging":[48,122],"due":[49],"the":[51,75,146,152],"exponential":[52],"search":[53],"space,":[54],"particularly":[55],"if":[56],"one":[57],"allows":[58],"partial":[59],"matching.":[60],"address":[62],"this":[63],"by":[64],"first":[65],"building":[66],"concise":[68],"vocabulary":[70,77],"via":[71],"clustering.":[73],"Using":[74],"we":[78],"then":[79],"utilize":[80],"mining":[82],"with":[83,109],"subsequent":[84],"clustering":[86],"ranking":[88],"select":[90],"compact":[92],"set":[93],"highly":[95],"patterns.":[98],"show":[100],"that":[101],"patterns,":[104],"alone,":[105],"or":[106],"in":[107,135,140],"combination":[108],"shorter":[110],"(action":[112],"words":[113],"pairwise":[115],"patterns)":[116],"achieve":[117],"state-of-the-art":[118],"performance":[119],"on":[120,131],"two":[121,137],"datasets:":[123],"UCF":[124],"Sports":[125],"HighFive.":[127],"Moreover,":[128],"learned":[130],"HighFive":[132],"are":[133],"used":[134],"recognizing":[136],"classes":[139],"different":[142],"dataset,":[143],"Hollywood3D,":[144],"demonstrating":[145],"potential":[147],"cross-dataset":[149],"generality":[150],"our":[154],"approach":[155],"discovers.":[156]},"counts_by_year":[{"year":2024,"cited_by_count":1},{"year":2022,"cited_by_count":1},{"year":2021,"cited_by_count":2},{"year":2020,"cited_by_count":1},{"year":2019,"cited_by_count":4},{"year":2018,"cited_by_count":8},{"year":2017,"cited_by_count":13},{"year":2016,"cited_by_count":12},{"year":2015,"cited_by_count":3}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}