{"id":"https://openalex.org/W1996828489","doi":"https://doi.org/10.4018/jmdem.2010111203","title":"Weighted Association Rule Mining for Video Semantic Detection","display_name":"Weighted Association Rule Mining for Video Semantic Detection","publication_year":2010,"publication_date":"2010-01-01","ids":{"openalex":"https://openalex.org/W1996828489","doi":"https://doi.org/10.4018/jmdem.2010111203","mag":"1996828489"},"language":"en","primary_location":{"id":"doi:10.4018/jmdem.2010111203","is_oa":false,"landing_page_url":"https://doi.org/10.4018/jmdem.2010111203","pdf_url":null,"source":{"id":"https://openalex.org/S87630009","display_name":"International Journal of Multimedia Data Engineering and Management","issn_l":"1947-8534","issn":["1947-8534","1947-8542"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310320424","host_organization_name":"IGI Global","host_organization_lineage":["https://openalex.org/P4310320424"],"host_organization_lineage_names":["IGI Global"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"International Journal of Multimedia Data Engineering and Management","raw_type":"journal-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/A5100342078","display_name":"Lin Lin","orcid":"https://orcid.org/0000-0003-3397-6002"},"institutions":[{"id":"https://openalex.org/I145608581","display_name":"University of Miami","ror":"https://ror.org/02dgjyy92","country_code":"US","type":"education","lineage":["https://openalex.org/I145608581"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Lin Lin","raw_affiliation_strings":["University of Miami, USA","(University of Miami, USA)"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of Miami, USA","institution_ids":["https://openalex.org/I145608581"]},{"raw_affiliation_string":"(University of Miami, USA)","institution_ids":["https://openalex.org/I145608581"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5036221136","display_name":"Mei\u2010Ling Shyu","orcid":"https://orcid.org/0000-0003-0902-0844"},"institutions":[{"id":"https://openalex.org/I145608581","display_name":"University of Miami","ror":"https://ror.org/02dgjyy92","country_code":"US","type":"education","lineage":["https://openalex.org/I145608581"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Mei-Ling Shyu","raw_affiliation_strings":["University of Miami, USA","(University of Miami, USA)"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of Miami, USA","institution_ids":["https://openalex.org/I145608581"]},{"raw_affiliation_string":"(University of Miami, USA)","institution_ids":["https://openalex.org/I145608581"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I145608581"],"apc_list":null,"apc_paid":null,"fwci":4.1258,"has_fulltext":false,"cited_by_count":49,"citation_normalized_percentile":{"value":0.94346467,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":89,"max":99},"biblio":{"volume":"1","issue":"1","first_page":"37","last_page":"54"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10627","display_name":"Advanced Image and Video Retrieval Techniques","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"}},"topics":[{"id":"https://openalex.org/T10627","display_name":"Advanced Image and Video Retrieval Techniques","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/T10824","display_name":"Image Retrieval and Classification Techniques","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/T11439","display_name":"Video Analysis and Summarization","score":0.9991000294685364,"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":0.8275748491287231},{"id":"https://openalex.org/keywords/association-rule-learning","display_name":"Association rule learning","score":0.8077565431594849},{"id":"https://openalex.org/keywords/classifier","display_name":"Classifier (UML)","score":0.6322260499000549},{"id":"https://openalex.org/keywords/benchmark","display_name":"Benchmark (surveying)","score":0.5757776498794556},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.5391083359718323},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.5369612574577332},{"id":"https://openalex.org/keywords/semantic-space","display_name":"Semantic space","score":0.4667523205280304},{"id":"https://openalex.org/keywords/semantic-feature","display_name":"Semantic feature","score":0.46256929636001587},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.445676326751709},{"id":"https://openalex.org/keywords/association","display_name":"Association (psychology)","score":0.4442991614341736},{"id":"https://openalex.org/keywords/information-retrieval","display_name":"Information retrieval","score":0.3586561679840088},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.35542285442352295},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.3279959559440613}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8275748491287231},{"id":"https://openalex.org/C193524817","wikidata":"https://www.wikidata.org/wiki/Q386780","display_name":"Association rule learning","level":2,"score":0.8077565431594849},{"id":"https://openalex.org/C95623464","wikidata":"https://www.wikidata.org/wiki/Q1096149","display_name":"Classifier (UML)","level":2,"score":0.6322260499000549},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.5757776498794556},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.5391083359718323},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.5369612574577332},{"id":"https://openalex.org/C2986420190","wikidata":"https://www.wikidata.org/wiki/Q39045939","display_name":"Semantic space","level":2,"score":0.4667523205280304},{"id":"https://openalex.org/C2781122975","wikidata":"https://www.wikidata.org/wiki/Q16928266","display_name":"Semantic feature","level":2,"score":0.46256929636001587},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.445676326751709},{"id":"https://openalex.org/C142853389","wikidata":"https://www.wikidata.org/wiki/Q744778","display_name":"Association (psychology)","level":2,"score":0.4442991614341736},{"id":"https://openalex.org/C23123220","wikidata":"https://www.wikidata.org/wiki/Q816826","display_name":"Information retrieval","level":1,"score":0.3586561679840088},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.35542285442352295},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.3279959559440613},{"id":"https://openalex.org/C138885662","wikidata":"https://www.wikidata.org/wiki/Q5891","display_name":"Philosophy","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/C205649164","wikidata":"https://www.wikidata.org/wiki/Q1071","display_name":"Geography","level":0,"score":0.0},{"id":"https://openalex.org/C111472728","wikidata":"https://www.wikidata.org/wiki/Q9471","display_name":"Epistemology","level":1,"score":0.0},{"id":"https://openalex.org/C13280743","wikidata":"https://www.wikidata.org/wiki/Q131089","display_name":"Geodesy","level":1,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.4018/jmdem.2010111203","is_oa":false,"landing_page_url":"https://doi.org/10.4018/jmdem.2010111203","pdf_url":null,"source":{"id":"https://openalex.org/S87630009","display_name":"International Journal of Multimedia Data Engineering and Management","issn_l":"1947-8534","issn":["1947-8534","1947-8542"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310320424","host_organization_name":"IGI Global","host_organization_lineage":["https://openalex.org/P4310320424"],"host_organization_lineage_names":["IGI Global"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"International Journal of Multimedia Data Engineering and Management","raw_type":"journal-article"},{"id":"pmh:oai:RePEc:igg:jmdem0:v:1:y:2010:i:1:p:37-54","is_oa":false,"landing_page_url":"http://services.igi-global.com/resolvedoi/resolve.aspx?doi=10.4018/jmdem.2010111203","pdf_url":null,"source":{"id":"https://openalex.org/S4306401271","display_name":"RePEc: Research Papers in Economics","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I77793887","host_organization_name":"Federal Reserve Bank of St. Louis","host_organization_lineage":["https://openalex.org/I77793887"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":36,"referenced_works":["https://openalex.org/W643606810","https://openalex.org/W1506285740","https://openalex.org/W1570448133","https://openalex.org/W1570713908","https://openalex.org/W1777378786","https://openalex.org/W1912852371","https://openalex.org/W2022111853","https://openalex.org/W2031577530","https://openalex.org/W2032427901","https://openalex.org/W2045664231","https://openalex.org/W2049826046","https://openalex.org/W2057858164","https://openalex.org/W2062903088","https://openalex.org/W2094770144","https://openalex.org/W2098268836","https://openalex.org/W2103396520","https://openalex.org/W2107255621","https://openalex.org/W2107618751","https://openalex.org/W2108838415","https://openalex.org/W2111993661","https://openalex.org/W2117489914","https://openalex.org/W2134330816","https://openalex.org/W2135333448","https://openalex.org/W2137117795","https://openalex.org/W2140763925","https://openalex.org/W2143663038","https://openalex.org/W2147069236","https://openalex.org/W2159743937","https://openalex.org/W2161373797","https://openalex.org/W2171128071","https://openalex.org/W2171880211","https://openalex.org/W2234068274","https://openalex.org/W2293888039","https://openalex.org/W2966207845","https://openalex.org/W4249215961","https://openalex.org/W4296262844"],"related_works":["https://openalex.org/W2772475062","https://openalex.org/W2965069339","https://openalex.org/W4323520214","https://openalex.org/W2811154137","https://openalex.org/W2116924158","https://openalex.org/W2047760401","https://openalex.org/W2560583282","https://openalex.org/W3135619263","https://openalex.org/W4376648022","https://openalex.org/W2356539390"],"abstract_inverted_index":{"Semantic":[0],"knowledge":[1],"detection":[2,39,58,181],"of":[3,48,90,177],"multimedia":[4,34],"content":[5],"has":[6,22],"become":[7],"a":[8,54,65,122],"very":[9],"popular":[10],"research":[11],"topic":[12],"in":[13,40,75,95,215],"recent":[14],"years.":[15],"The":[16,198],"association":[17,50,68,98,156,166],"rule":[18,51,69],"mining":[19,52,70],"(ARM)":[20],"technique":[21],"been":[23],"shown":[24],"to":[25,84,115,147,153],"be":[26],"an":[27],"efficient":[28],"and":[29,36,119,127,134,141,152,194,208],"accurate":[30],"approach":[31],"for":[32,100],"content-based":[33],"retrieval":[35],"semantic":[37,56,102,179],"concept":[38,57,103,180],"many":[41],"applications.":[42],"To":[43,168],"further":[44],"improve":[45],"the":[46,86,91,97,117,129,145,149,155,187,192,216],"performance":[47,176,207],"traditional":[49],"technique,":[53],"video":[55,101,178],"framework":[59,108,204],"whose":[60],"classifier":[61],"is":[62,73,82],"built":[63],"upon":[64],"new":[66,123],"weighted":[67,165],"(WARM)":[71],"algorithm":[72,81],"proposed":[74,79,106],"this":[76],"article.":[77],"Our":[78,105],"WARM":[80],"able":[83],"capture":[85],"different":[87],"significance":[88],"degrees":[89],"items":[92],"(feature-value":[93],"pairs)":[94],"generating":[96],"rules":[99],"detection.":[104],"WARM-based":[107,171,203],"first":[109],"applies":[110],"multiple":[111],"correspondence":[112],"analysis":[113],"(MCA)":[114],"project":[116],"features":[118],"classes":[120],"into":[121],"principle":[124],"component":[125],"space":[126],"discover":[128],"correlation":[130,140],"between":[131],"feature-value":[132,150],"pairs":[133,151],"classes.":[135],"Next,":[136],"it":[137,159],"considers":[138],"both":[139],"percentage":[142],"information":[143],"as":[144],"measurement":[146],"weight":[148],"generate":[154],"rules.":[157,167],"Finally,":[158],"performs":[160,209],"classification":[161],"by":[162],"using":[163,186],"these":[164],"evaluate":[169],"our":[170,202],"framework,":[172],"we":[173],"compare":[174],"its":[175],"with":[182],"several":[183],"well-known":[184],"classifiers":[185,214],"benchmark":[188],"data":[189],"available":[190],"from":[191],"2007":[193],"2008":[195],"TRECVID":[196],"projects.":[197],"results":[199],"demonstrate":[200],"that":[201],"achieves":[205],"promising":[206],"significantly":[210],"better":[211],"than":[212],"those":[213],"comparison.":[217]},"counts_by_year":[{"year":2025,"cited_by_count":1},{"year":2021,"cited_by_count":1},{"year":2020,"cited_by_count":3},{"year":2019,"cited_by_count":3},{"year":2018,"cited_by_count":4},{"year":2017,"cited_by_count":7},{"year":2016,"cited_by_count":6},{"year":2015,"cited_by_count":7},{"year":2014,"cited_by_count":3},{"year":2013,"cited_by_count":3},{"year":2012,"cited_by_count":5}],"updated_date":"2026-06-11T06:19:23.411458","created_date":"2025-10-10T00:00:00"}