{"id":"https://openalex.org/W2060777673","doi":"https://doi.org/10.1145/2324796.2324839","title":"Multi-graph multi-instance learning for object-based image and video retrieval","display_name":"Multi-graph multi-instance learning for object-based image and video retrieval","publication_year":2012,"publication_date":"2012-06-05","ids":{"openalex":"https://openalex.org/W2060777673","doi":"https://doi.org/10.1145/2324796.2324839","mag":"2060777673"},"language":"en","primary_location":{"id":"doi:10.1145/2324796.2324839","is_oa":false,"landing_page_url":"https://doi.org/10.1145/2324796.2324839","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 2nd ACM International Conference on Multimedia Retrieval","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/A5100325771","display_name":"Fei Li","orcid":"https://orcid.org/0000-0001-7652-0473"},"institutions":[{"id":"https://openalex.org/I4210159607","display_name":"Fujitsu (China)","ror":"https://ror.org/04w4yzw62","country_code":"CN","type":"company","lineage":["https://openalex.org/I4210159607"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Fei Li","raw_affiliation_strings":["Fujitsu Research &amp; Development Center, Beijing, China"],"affiliations":[{"raw_affiliation_string":"Fujitsu Research &amp; Development Center, Beijing, China","institution_ids":["https://openalex.org/I4210159607"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5044324493","display_name":"Rujie Liu","orcid":"https://orcid.org/0000-0002-4992-7312"},"institutions":[{"id":"https://openalex.org/I4210159607","display_name":"Fujitsu (China)","ror":"https://ror.org/04w4yzw62","country_code":"CN","type":"company","lineage":["https://openalex.org/I4210159607"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Rujie Liu","raw_affiliation_strings":["Fujitsu Research &amp; Development Center, Beijing, China"],"affiliations":[{"raw_affiliation_string":"Fujitsu Research &amp; Development Center, Beijing, China","institution_ids":["https://openalex.org/I4210159607"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5100325771"],"corresponding_institution_ids":["https://openalex.org/I4210159607"],"apc_list":null,"apc_paid":null,"fwci":1.0982,"has_fulltext":false,"cited_by_count":10,"citation_normalized_percentile":{"value":0.79861773,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":96},"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"8"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10824","display_name":"Image Retrieval and Classification Techniques","score":0.9998000264167786,"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/T10824","display_name":"Image Retrieval and Classification Techniques","score":0.9998000264167786,"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/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/T11439","display_name":"Video Analysis and Summarization","score":0.9930999875068665,"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":0.8037001490592957},{"id":"https://openalex.org/keywords/image-retrieval","display_name":"Image retrieval","score":0.6249033212661743},{"id":"https://openalex.org/keywords/graph","display_name":"Graph","score":0.5766549110412598},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5581094622612},{"id":"https://openalex.org/keywords/benchmark","display_name":"Benchmark (surveying)","score":0.4924856126308441},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.4441404640674591},{"id":"https://openalex.org/keywords/set","display_name":"Set (abstract data type)","score":0.4284785985946655},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.39632323384284973},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.3499910533428192},{"id":"https://openalex.org/keywords/information-retrieval","display_name":"Information retrieval","score":0.3390832543373108},{"id":"https://openalex.org/keywords/theoretical-computer-science","display_name":"Theoretical computer science","score":0.13166767358779907}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8037001490592957},{"id":"https://openalex.org/C1667742","wikidata":"https://www.wikidata.org/wiki/Q10927554","display_name":"Image retrieval","level":3,"score":0.6249033212661743},{"id":"https://openalex.org/C132525143","wikidata":"https://www.wikidata.org/wiki/Q141488","display_name":"Graph","level":2,"score":0.5766549110412598},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5581094622612},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.4924856126308441},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.4441404640674591},{"id":"https://openalex.org/C177264268","wikidata":"https://www.wikidata.org/wiki/Q1514741","display_name":"Set (abstract data type)","level":2,"score":0.4284785985946655},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.39632323384284973},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.3499910533428192},{"id":"https://openalex.org/C23123220","wikidata":"https://www.wikidata.org/wiki/Q816826","display_name":"Information retrieval","level":1,"score":0.3390832543373108},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.13166767358779907},{"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/C205649164","wikidata":"https://www.wikidata.org/wiki/Q1071","display_name":"Geography","level":0,"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":1,"locations":[{"id":"doi:10.1145/2324796.2324839","is_oa":false,"landing_page_url":"https://doi.org/10.1145/2324796.2324839","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 2nd ACM International Conference on Multimedia Retrieval","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"score":0.5199999809265137,"display_name":"Quality Education","id":"https://metadata.un.org/sdg/4"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":41,"referenced_works":["https://openalex.org/W65207188","https://openalex.org/W1625255723","https://openalex.org/W1963929959","https://openalex.org/W1972352301","https://openalex.org/W1974477236","https://openalex.org/W1975692209","https://openalex.org/W1980511015","https://openalex.org/W1982713233","https://openalex.org/W2026486243","https://openalex.org/W2027922120","https://openalex.org/W2028017042","https://openalex.org/W2039118116","https://openalex.org/W2072263195","https://openalex.org/W2075460102","https://openalex.org/W2076752034","https://openalex.org/W2082453965","https://openalex.org/W2092485668","https://openalex.org/W2093990122","https://openalex.org/W2097092269","https://openalex.org/W2098166271","https://openalex.org/W2098239572","https://openalex.org/W2107034620","https://openalex.org/W2110119381","https://openalex.org/W2111993661","https://openalex.org/W2115517344","https://openalex.org/W2123073303","https://openalex.org/W2130660124","https://openalex.org/W2134791408","https://openalex.org/W2136595724","https://openalex.org/W2138395288","https://openalex.org/W2147069236","https://openalex.org/W2148488151","https://openalex.org/W2150691697","https://openalex.org/W2154455818","https://openalex.org/W2161967063","https://openalex.org/W2172191903","https://openalex.org/W2215815030","https://openalex.org/W2999905431","https://openalex.org/W6636494156","https://openalex.org/W6682494755","https://openalex.org/W6688645734"],"related_works":["https://openalex.org/W3208409104","https://openalex.org/W2392326565","https://openalex.org/W2005185696","https://openalex.org/W1536471031","https://openalex.org/W4288373736","https://openalex.org/W1806283415","https://openalex.org/W2161229648","https://openalex.org/W2235753890","https://openalex.org/W2993674027","https://openalex.org/W2116278160"],"abstract_inverted_index":{"Object-based":[0],"image":[1,47,90,142],"retrieval":[2],"has":[3,32],"been":[4],"an":[5,98],"active":[6],"research":[7],"topic":[8],"in":[9,12,19,22,46,50,54,82,134],"recent":[10],"years,":[11],"which":[13],"a":[14,68],"user":[15],"is":[16,76,95,104],"only":[17,132],"interested":[18],"some":[20],"object":[21],"the":[23,65,86,112,126,140,145,150],"images.":[24],"As":[25],"one":[26,43],"promising":[27],"approach,":[28],"graph-based":[29],"multi-instance":[30,74],"learning":[31,41,75],"attracted":[33],"many":[34],"researchers.":[35],"The":[36],"existing":[37],"methods":[38],"often":[39],"conduct":[40],"on":[42,72,139],"graph,":[44],"either":[45],"level":[48],"or":[49],"region":[51],"level.":[52,136],"While":[53],"this":[55],"paper,":[56],"by":[57],"considering":[58],"both":[59],"image-":[60],"and":[61,85,91,119,144],"region-level":[62],"information":[63],"at":[64],"same":[66],"time,":[67],"novel":[69],"method":[70,103],"based":[71],"multi-graph":[73],"proposed.":[77],"Two":[78],"graphs":[79],"are":[80,131],"constructed":[81],"our":[83,102,153],"method,":[84],"relationship":[87],"between":[88,114],"each":[89],"its":[92],"segmented":[93,120],"regions":[94],"introduced":[96],"into":[97],"optimization":[99],"framework.":[100],"Moreover,":[101],"further":[105],"extended":[106],"to":[107],"video":[108,115,147],"retrieval.":[109],"By":[110],"exploring":[111],"relationships":[113],"shots,":[116],"representative":[117],"images,":[118],"regions,":[121],"it":[122],"can":[123],"deal":[124],"with":[125],"case":[127],"when":[128],"training":[129],"labels":[130],"assigned":[133],"shot":[135],"Experimental":[137],"results":[138],"SIVAL":[141],"benchmark":[143],"TRECVID":[146],"set":[148],"demonstrate":[149],"effectiveness":[151],"of":[152],"proposal.":[154]},"counts_by_year":[{"year":2024,"cited_by_count":1},{"year":2021,"cited_by_count":1},{"year":2020,"cited_by_count":1},{"year":2017,"cited_by_count":2},{"year":2016,"cited_by_count":1},{"year":2015,"cited_by_count":2},{"year":2013,"cited_by_count":2}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
