{"id":"https://openalex.org/W2549692186","doi":"https://doi.org/10.1109/spcom.2016.7746695","title":"Accelerated learning of discriminative spatio-temporal features for action recognition","display_name":"Accelerated learning of discriminative spatio-temporal features for action recognition","publication_year":2016,"publication_date":"2016-06-01","ids":{"openalex":"https://openalex.org/W2549692186","doi":"https://doi.org/10.1109/spcom.2016.7746695","mag":"2549692186"},"language":"en","primary_location":{"id":"doi:10.1109/spcom.2016.7746695","is_oa":false,"landing_page_url":"https://doi.org/10.1109/spcom.2016.7746695","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2016 International Conference on Signal Processing and Communications (SPCOM)","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/A5036559623","display_name":"Munender Varshney","orcid":"https://orcid.org/0000-0002-3061-5757"},"institutions":[{"id":"https://openalex.org/I9579091","display_name":"Indian Institute of Technology Mandi","ror":"https://ror.org/05r9r2f34","country_code":"IN","type":"education","lineage":["https://openalex.org/I9579091"]}],"countries":["IN"],"is_corresponding":true,"raw_author_name":"Munender Varshney","raw_affiliation_strings":["School of Computing and Electrical Engineering, Indian Institute of Technology, Mandi, Himachal Pradesh, India"],"affiliations":[{"raw_affiliation_string":"School of Computing and Electrical Engineering, Indian Institute of Technology, Mandi, Himachal Pradesh, India","institution_ids":["https://openalex.org/I9579091"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5050363496","display_name":"Renu Rameshan","orcid":"https://orcid.org/0000-0002-7623-0510"},"institutions":[{"id":"https://openalex.org/I9579091","display_name":"Indian Institute of Technology Mandi","ror":"https://ror.org/05r9r2f34","country_code":"IN","type":"education","lineage":["https://openalex.org/I9579091"]}],"countries":["IN"],"is_corresponding":false,"raw_author_name":"Renu Rameshan","raw_affiliation_strings":["School of Computing and Electrical Engineering, Indian Institute of Technology, Mandi, Himachal Pradesh, India"],"affiliations":[{"raw_affiliation_string":"School of Computing and Electrical Engineering, Indian Institute of Technology, Mandi, Himachal Pradesh, India","institution_ids":["https://openalex.org/I9579091"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5036559623"],"corresponding_institution_ids":["https://openalex.org/I9579091"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.11812979,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"5"},"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":0.9991999864578247,"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/T10331","display_name":"Video Surveillance and Tracking Methods","score":0.998199999332428,"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/computer-science","display_name":"Computer science","score":0.8363455533981323},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6751409769058228},{"id":"https://openalex.org/keywords/feature-extraction","display_name":"Feature extraction","score":0.6474053859710693},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.6042048335075378},{"id":"https://openalex.org/keywords/disjoint-sets","display_name":"Disjoint sets","score":0.5694030523300171},{"id":"https://openalex.org/keywords/convolution","display_name":"Convolution (computer science)","score":0.5668574571609497},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.5316427946090698},{"id":"https://openalex.org/keywords/discriminative-model","display_name":"Discriminative model","score":0.5035633444786072},{"id":"https://openalex.org/keywords/feature-learning","display_name":"Feature learning","score":0.5022592544555664},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.49898195266723633},{"id":"https://openalex.org/keywords/subspace-topology","display_name":"Subspace topology","score":0.44318264722824097},{"id":"https://openalex.org/keywords/speedup","display_name":"Speedup","score":0.42762988805770874},{"id":"https://openalex.org/keywords/scalability","display_name":"Scalability","score":0.4104287922382355},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.40469491481781006},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.15759235620498657}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8363455533981323},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6751409769058228},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.6474053859710693},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.6042048335075378},{"id":"https://openalex.org/C45340560","wikidata":"https://www.wikidata.org/wiki/Q215382","display_name":"Disjoint sets","level":2,"score":0.5694030523300171},{"id":"https://openalex.org/C45347329","wikidata":"https://www.wikidata.org/wiki/Q5166604","display_name":"Convolution (computer science)","level":3,"score":0.5668574571609497},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.5316427946090698},{"id":"https://openalex.org/C97931131","wikidata":"https://www.wikidata.org/wiki/Q5282087","display_name":"Discriminative model","level":2,"score":0.5035633444786072},{"id":"https://openalex.org/C59404180","wikidata":"https://www.wikidata.org/wiki/Q17013334","display_name":"Feature learning","level":2,"score":0.5022592544555664},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.49898195266723633},{"id":"https://openalex.org/C32834561","wikidata":"https://www.wikidata.org/wiki/Q660730","display_name":"Subspace topology","level":2,"score":0.44318264722824097},{"id":"https://openalex.org/C68339613","wikidata":"https://www.wikidata.org/wiki/Q1549489","display_name":"Speedup","level":2,"score":0.42762988805770874},{"id":"https://openalex.org/C48044578","wikidata":"https://www.wikidata.org/wiki/Q727490","display_name":"Scalability","level":2,"score":0.4104287922382355},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.40469491481781006},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.15759235620498657},{"id":"https://openalex.org/C77088390","wikidata":"https://www.wikidata.org/wiki/Q8513","display_name":"Database","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/C111919701","wikidata":"https://www.wikidata.org/wiki/Q9135","display_name":"Operating system","level":1,"score":0.0},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"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/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/spcom.2016.7746695","is_oa":false,"landing_page_url":"https://doi.org/10.1109/spcom.2016.7746695","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2016 International Conference on Signal Processing and Communications (SPCOM)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"score":0.7200000286102295,"display_name":"Reduced inequalities","id":"https://metadata.un.org/sdg/10"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":32,"referenced_works":["https://openalex.org/W854053868","https://openalex.org/W1586730761","https://openalex.org/W1738124305","https://openalex.org/W1983364832","https://openalex.org/W1993229407","https://openalex.org/W1999192586","https://openalex.org/W2015861736","https://openalex.org/W2016053056","https://openalex.org/W2026942141","https://openalex.org/W2034328688","https://openalex.org/W2091662199","https://openalex.org/W2096544401","https://openalex.org/W2096691069","https://openalex.org/W2124486835","https://openalex.org/W2126715624","https://openalex.org/W2131975293","https://openalex.org/W2136922672","https://openalex.org/W2138791376","https://openalex.org/W2146634731","https://openalex.org/W2156303437","https://openalex.org/W2163292664","https://openalex.org/W2168231600","https://openalex.org/W2173213060","https://openalex.org/W2179463219","https://openalex.org/W2189465200","https://openalex.org/W2482213519","https://openalex.org/W2533739470","https://openalex.org/W4249279051","https://openalex.org/W6623766018","https://openalex.org/W6682864246","https://openalex.org/W6684859321","https://openalex.org/W6687322159"],"related_works":["https://openalex.org/W2058965144","https://openalex.org/W2164382479","https://openalex.org/W2146343568","https://openalex.org/W98480971","https://openalex.org/W3208297503","https://openalex.org/W3119773509","https://openalex.org/W2889153461","https://openalex.org/W2964117661","https://openalex.org/W4388405611","https://openalex.org/W2619127353"],"abstract_inverted_index":{"Recently,":[0],"paradigm":[1],"has":[2],"shifted":[3],"from":[4,17,132],"hand-designed":[5],"local":[6],"feature":[7,73,92,182],"learning":[8,11,29,74,93],"to":[9,14,36,59,104,128],"unsupervised":[10],"in":[12,195],"order":[13],"extract":[15,129],"features":[16],"raw":[18],"data.":[19],"In":[20],"action":[21],"recognition,":[22],"good":[23],"results":[24],"are":[25],"achieved":[26,156,185],"using":[27,75,123],"deep":[28],"techniques":[30],"such":[31],"as":[32,150],"stacking":[33],"and":[34,63,155,176,193,197],"convolution":[35],"extend":[37],"the":[38,85,99,139,144,151,163,178],"idea":[39],"of":[40,53,90,153,173,191],"independent":[41],"subspace":[42],"analysis":[43],"(ISA).":[44],"Albeit":[45],"performance":[46],"is":[47,94,121],"good,":[48],"it":[49],"takes":[50],"significant":[51],"amount":[52],"time":[54],"on":[55,161],"big":[56],"datasets":[57,110,164],"due":[58],"high":[60],"computational":[61],"complexity":[62],"sequential":[64],"implementation.":[65],"We":[66,77,147,168,184],"propose":[67,79],"two":[68],"methods":[69],"for":[70,118,181],"speeding":[71],"up":[72,187],"ISA.":[76],"also":[78,142,169],"input":[80,149],"data":[81],"modification":[82],"which":[83],"increases":[84],"classification":[86,145,159],"performance.":[87],"One":[88],"method":[89,117,199],"faster":[91],"parallelization":[95],"-":[96],"we":[97],"use":[98],"scalable":[100],"programming":[101],"model,":[102],"MapReduce":[103],"parametrize":[105],"ISA":[106,179],"algorithm":[107],"by":[108,122,188],"distributing":[109],"into":[111],"equal":[112],"disjoint":[113],"sets.":[114],"The":[115,134],"second":[116,198],"increasing":[119],"speed":[120,140,186],"spatio-temporal":[124],"interest":[125],"point":[126],"detectors":[127],"\u201cimportant\u201d":[130],"blocks":[131],"video.":[133],"latter":[135],"not":[136],"only":[137],"enhances":[138],"but":[141],"improves":[143],"accuracy.":[146],"modified":[148],"gradient":[152],"video":[154],"a":[157,171,189],"better":[158],"accuracy":[160],"all":[162],"that":[165],"were":[166],"tested.":[167],"created":[170],"dataset":[172],"water":[174],"activities":[175],"used":[177],"network":[180],"extraction.":[183],"factor":[190],"4":[192],"2.4":[194],"first":[196],"respectively.":[200]},"counts_by_year":[],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
