{"id":"https://openalex.org/W2583638795","doi":"https://doi.org/10.1109/bigdata.2016.7840652","title":"Multi-step threshold algorithm for efficient feature-based query processing in large-scale multimedia databases","display_name":"Multi-step threshold algorithm for efficient feature-based query processing in large-scale multimedia databases","publication_year":2016,"publication_date":"2016-12-01","ids":{"openalex":"https://openalex.org/W2583638795","doi":"https://doi.org/10.1109/bigdata.2016.7840652","mag":"2583638795"},"language":"en","primary_location":{"id":"doi:10.1109/bigdata.2016.7840652","is_oa":false,"landing_page_url":"https://doi.org/10.1109/bigdata.2016.7840652","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2016 IEEE International Conference on Big Data (Big Data)","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/A5039809923","display_name":"Christian Beecks","orcid":"https://orcid.org/0009-0000-9028-629X"},"institutions":[{"id":"https://openalex.org/I887968799","display_name":"RWTH Aachen University","ror":"https://ror.org/04xfq0f34","country_code":"DE","type":"education","lineage":["https://openalex.org/I887968799"]}],"countries":["DE"],"is_corresponding":true,"raw_author_name":"Christian Beecks","raw_affiliation_strings":["RWTH Aachen University, Germany"],"affiliations":[{"raw_affiliation_string":"RWTH Aachen University, Germany","institution_ids":["https://openalex.org/I887968799"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5012505213","display_name":"Alexander Gras","orcid":null},"institutions":[{"id":"https://openalex.org/I887968799","display_name":"RWTH Aachen University","ror":"https://ror.org/04xfq0f34","country_code":"DE","type":"education","lineage":["https://openalex.org/I887968799"]}],"countries":["DE"],"is_corresponding":false,"raw_author_name":"Alexander Gras","raw_affiliation_strings":["RWTH Aachen University, Germany"],"affiliations":[{"raw_affiliation_string":"RWTH Aachen University, Germany","institution_ids":["https://openalex.org/I887968799"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5039809923"],"corresponding_institution_ids":["https://openalex.org/I887968799"],"apc_list":null,"apc_paid":null,"fwci":0.501,"has_fulltext":false,"cited_by_count":3,"citation_normalized_percentile":{"value":0.74496115,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":96},"biblio":{"volume":null,"issue":null,"first_page":"596","last_page":"605"},"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":0.9975000023841858,"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":0.9975000023841858,"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/T11106","display_name":"Data Management and Algorithms","score":0.9911999702453613,"subfield":{"id":"https://openalex.org/subfields/1711","display_name":"Signal Processing"},"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":0.9908999800682068,"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":0.8944542407989502},{"id":"https://openalex.org/keywords/multimedia-database","display_name":"Multimedia database","score":0.7906163930892944},{"id":"https://openalex.org/keywords/scalability","display_name":"Scalability","score":0.7550473213195801},{"id":"https://openalex.org/keywords/database","display_name":"Database","score":0.621720552444458},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.5605836510658264},{"id":"https://openalex.org/keywords/information-retrieval","display_name":"Information retrieval","score":0.5312478542327881},{"id":"https://openalex.org/keywords/multimedia","display_name":"Multimedia","score":0.43095284700393677},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.32624632120132446}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8944542407989502},{"id":"https://openalex.org/C2779061030","wikidata":"https://www.wikidata.org/wiki/Q12038942","display_name":"Multimedia database","level":2,"score":0.7906163930892944},{"id":"https://openalex.org/C48044578","wikidata":"https://www.wikidata.org/wiki/Q727490","display_name":"Scalability","level":2,"score":0.7550473213195801},{"id":"https://openalex.org/C77088390","wikidata":"https://www.wikidata.org/wiki/Q8513","display_name":"Database","level":1,"score":0.621720552444458},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.5605836510658264},{"id":"https://openalex.org/C23123220","wikidata":"https://www.wikidata.org/wiki/Q816826","display_name":"Information retrieval","level":1,"score":0.5312478542327881},{"id":"https://openalex.org/C49774154","wikidata":"https://www.wikidata.org/wiki/Q131765","display_name":"Multimedia","level":1,"score":0.43095284700393677},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.32624632120132446},{"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}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/bigdata.2016.7840652","is_oa":false,"landing_page_url":"https://doi.org/10.1109/bigdata.2016.7840652","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2016 IEEE International Conference on Big Data (Big Data)","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":0,"referenced_works":[],"related_works":["https://openalex.org/W2389214306","https://openalex.org/W2290518917","https://openalex.org/W1574746021","https://openalex.org/W1510239124","https://openalex.org/W150639994","https://openalex.org/W615792041","https://openalex.org/W2983732983","https://openalex.org/W263774331","https://openalex.org/W2613278502","https://openalex.org/W1556501646"],"abstract_inverted_index":{"Accessing":[0],"very":[1],"large":[2],"multimedia":[3,18,46,61,79,109,131,169],"databases":[4,80],"in":[5,16,122,126,171],"a":[6,96,123,167,174],"content-based":[7,36,76],"way":[8],"has":[9],"become":[10],"one":[11],"of":[12,27,33,53,59,83,98],"the":[13,25,30,49,57,89,101,106,130,141],"major":[14],"challenges":[15],"todays'":[17],"analysis":[19,155],"and":[20,29,43,64,74],"retrieval":[21],"applications.":[22],"Accompanied":[23],"by":[24,115],"heterogeneity":[26],"data":[28,85],"continuous":[31],"change":[32],"user":[34],"requirements,":[35],"approaches":[37],"are":[38],"supposed":[39],"to":[40,71,104,128,135,140,162],"efficiently":[41],"retrieve":[42,105],"analyze":[44],"query-like":[45],"objects":[47,110],"with":[48,111],"highest":[50],"possible":[51],"degree":[52],"efficacy,":[54],"which":[55],"makes":[56],"utilization":[58],"complex":[60,84],"object":[62],"representations":[63],"adaptive":[65],"similarity":[66],"measures":[67],"inevitable.":[68],"In":[69,138],"order":[70,127],"facilitate":[72],"efficient":[73],"flexible":[75],"access":[77],"into":[78],"comprising":[81],"millions":[82],"objects,":[86],"we":[87,143],"propose":[88,144],"Multi-step":[90],"Threshold":[91],"Algorithm":[92],"(MTA).":[93],"Based":[94],"on":[95,166,173],"set":[97],"query":[99,151],"features,":[100],"MTA":[102],"aims":[103],"most":[107],"similar":[108],"minimal":[112],"I/O":[113],"cost":[114],"incrementally":[116],"traversing":[117],"an":[118],"in-memory":[119],"index":[120],"structure":[121],"feature-by-feature":[124],"manner":[125],"approximate":[129],"objects'":[132],"similarities":[133],"prior":[134],"database":[136,170],"access.":[137],"addition":[139],"MTA,":[142],"different":[145],"enhancements":[146],"that":[147,157],"ensure":[148],"scalable":[149],"feature-based":[150,164],"processing.":[152],"Our":[153],"performance":[154],"evidences":[156],"our":[158],"proposal":[159],"is":[160],"able":[161],"process":[163],"queries":[165],"million-scale":[168],"milliseconds":[172],"single":[175],"CPU.":[176]},"counts_by_year":[{"year":2019,"cited_by_count":1},{"year":2018,"cited_by_count":2}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
