{"id":"https://openalex.org/W2122829955","doi":"https://doi.org/10.1109/cvpr.2009.5206518","title":"A revisit of Generative Model for Automatic Image Annotation using Markov Random Fields","display_name":"A revisit of Generative Model for Automatic Image Annotation using Markov Random Fields","publication_year":2009,"publication_date":"2009-06-01","ids":{"openalex":"https://openalex.org/W2122829955","doi":"https://doi.org/10.1109/cvpr.2009.5206518","mag":"2122829955"},"language":"en","primary_location":{"id":"doi:10.1109/cvpr.2009.5206518","is_oa":false,"landing_page_url":"https://doi.org/10.1109/cvpr.2009.5206518","pdf_url":null,"source":{"id":"https://openalex.org/S4363607795","display_name":"2009 IEEE Conference on Computer Vision and Pattern Recognition","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2009 IEEE Conference on Computer Vision and Pattern Recognition","raw_type":"proceedings-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://ink.library.smu.edu.sg/cgi/viewcontent.cgi?article=7603&context=sis_research","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5101595454","display_name":"Yu Xiang","orcid":"https://orcid.org/0000-0002-2891-9153"},"institutions":[{"id":"https://openalex.org/I24943067","display_name":"Fudan University","ror":"https://ror.org/013q1eq08","country_code":"CN","type":"education","lineage":["https://openalex.org/I24943067"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Yu Xiang","raw_affiliation_strings":["Chong-Wah Ngo, Shanghai, China","Fudan Unviersity, Shanghai, China"],"affiliations":[{"raw_affiliation_string":"Chong-Wah Ngo, Shanghai, China","institution_ids":[]},{"raw_affiliation_string":"Fudan Unviersity, Shanghai, China","institution_ids":["https://openalex.org/I24943067"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5090238197","display_name":"Xiangdong Zhou","orcid":"https://orcid.org/0000-0002-4451-5327"},"institutions":[{"id":"https://openalex.org/I24943067","display_name":"Fudan University","ror":"https://ror.org/013q1eq08","country_code":"CN","type":"education","lineage":["https://openalex.org/I24943067"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xiangdong Zhou","raw_affiliation_strings":["Chong-Wah Ngo, Shanghai, China","Fudan Unviersity, Shanghai, China"],"affiliations":[{"raw_affiliation_string":"Chong-Wah Ngo, Shanghai, China","institution_ids":[]},{"raw_affiliation_string":"Fudan Unviersity, Shanghai, China","institution_ids":["https://openalex.org/I24943067"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5089404640","display_name":"Tat\u2010Seng Chua","orcid":"https://orcid.org/0000-0001-6097-7807"},"institutions":[{"id":"https://openalex.org/I165932596","display_name":"National University of Singapore","ror":"https://ror.org/01tgyzw49","country_code":"SG","type":"education","lineage":["https://openalex.org/I165932596"]}],"countries":["SG"],"is_corresponding":false,"raw_author_name":"Tat-Seng Chua","raw_affiliation_strings":["National University, Singapore","National University Singapore; Singapore"],"affiliations":[{"raw_affiliation_string":"National University, Singapore","institution_ids":["https://openalex.org/I165932596"]},{"raw_affiliation_string":"National University Singapore; Singapore","institution_ids":["https://openalex.org/I165932596"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5010722442","display_name":"Chong\u2010Wah Ngo","orcid":"https://orcid.org/0000-0003-4182-8261"},"institutions":[{"id":"https://openalex.org/I168719708","display_name":"City University of Hong Kong","ror":"https://ror.org/03q8dnn23","country_code":"HK","type":"education","lineage":["https://openalex.org/I168719708"]}],"countries":["HK"],"is_corresponding":false,"raw_author_name":"Chong-Wah Ngo","raw_affiliation_strings":["City University, Hong Kong, China","City University, Hong Kong, China#TAB#"],"affiliations":[{"raw_affiliation_string":"City University, Hong Kong, China","institution_ids":["https://openalex.org/I168719708"]},{"raw_affiliation_string":"City University, Hong Kong, China#TAB#","institution_ids":["https://openalex.org/I168719708"]}]}],"institutions":[],"countries_distinct_count":3,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5101595454"],"corresponding_institution_ids":["https://openalex.org/I24943067"],"apc_list":null,"apc_paid":null,"fwci":2.8598,"has_fulltext":false,"cited_by_count":78,"citation_normalized_percentile":{"value":0.92579294,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":90,"max":100},"biblio":{"volume":null,"issue":null,"first_page":"1153","last_page":"1160"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10824","display_name":"Image Retrieval and Classification Techniques","score":0.9998999834060669,"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":0.9998999834060669,"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":0.9995999932289124,"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":0.982699990272522,"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/leverage","display_name":"Leverage (statistics)","score":0.7735867500305176},{"id":"https://openalex.org/keywords/inference","display_name":"Inference","score":0.7564796805381775},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7184879183769226},{"id":"https://openalex.org/keywords/generative-model","display_name":"Generative model","score":0.6869403123855591},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6571216583251953},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.6010672450065613},{"id":"https://openalex.org/keywords/generative-grammar","display_name":"Generative grammar","score":0.5516031980514526},{"id":"https://openalex.org/keywords/annotation","display_name":"Annotation","score":0.5013172626495361},{"id":"https://openalex.org/keywords/context","display_name":"Context (archaeology)","score":0.47583794593811035},{"id":"https://openalex.org/keywords/hidden-markov-model","display_name":"Hidden Markov model","score":0.43781858682632446},{"id":"https://openalex.org/keywords/markov-random-field","display_name":"Markov random field","score":0.42014577984809875},{"id":"https://openalex.org/keywords/markov-chain","display_name":"Markov chain","score":0.4175349473953247},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.364329993724823},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.3323451280593872},{"id":"https://openalex.org/keywords/image-segmentation","display_name":"Image segmentation","score":0.14278754591941833}],"concepts":[{"id":"https://openalex.org/C153083717","wikidata":"https://www.wikidata.org/wiki/Q6535263","display_name":"Leverage (statistics)","level":2,"score":0.7735867500305176},{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.7564796805381775},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7184879183769226},{"id":"https://openalex.org/C167966045","wikidata":"https://www.wikidata.org/wiki/Q5532625","display_name":"Generative model","level":3,"score":0.6869403123855591},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6571216583251953},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.6010672450065613},{"id":"https://openalex.org/C39890363","wikidata":"https://www.wikidata.org/wiki/Q36108","display_name":"Generative grammar","level":2,"score":0.5516031980514526},{"id":"https://openalex.org/C2776321320","wikidata":"https://www.wikidata.org/wiki/Q857525","display_name":"Annotation","level":2,"score":0.5013172626495361},{"id":"https://openalex.org/C2779343474","wikidata":"https://www.wikidata.org/wiki/Q3109175","display_name":"Context (archaeology)","level":2,"score":0.47583794593811035},{"id":"https://openalex.org/C23224414","wikidata":"https://www.wikidata.org/wiki/Q176769","display_name":"Hidden Markov model","level":2,"score":0.43781858682632446},{"id":"https://openalex.org/C2778045648","wikidata":"https://www.wikidata.org/wiki/Q176827","display_name":"Markov random field","level":4,"score":0.42014577984809875},{"id":"https://openalex.org/C98763669","wikidata":"https://www.wikidata.org/wiki/Q176645","display_name":"Markov chain","level":2,"score":0.4175349473953247},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.364329993724823},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.3323451280593872},{"id":"https://openalex.org/C124504099","wikidata":"https://www.wikidata.org/wiki/Q56933","display_name":"Image segmentation","level":3,"score":0.14278754591941833},{"id":"https://openalex.org/C151730666","wikidata":"https://www.wikidata.org/wiki/Q7205","display_name":"Paleontology","level":1,"score":0.0},{"id":"https://openalex.org/C86803240","wikidata":"https://www.wikidata.org/wiki/Q420","display_name":"Biology","level":0,"score":0.0}],"mesh":[],"locations_count":3,"locations":[{"id":"doi:10.1109/cvpr.2009.5206518","is_oa":false,"landing_page_url":"https://doi.org/10.1109/cvpr.2009.5206518","pdf_url":null,"source":{"id":"https://openalex.org/S4363607795","display_name":"2009 IEEE Conference on Computer Vision and Pattern Recognition","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2009 IEEE Conference on Computer Vision and Pattern Recognition","raw_type":"proceedings-article"},{"id":"pmh:oai:ink.library.smu.edu.sg:sis_research-7603","is_oa":true,"landing_page_url":"https://ink.library.smu.edu.sg/cgi/viewcontent.cgi?article=7603&context=sis_research","pdf_url":null,"source":{"id":"https://openalex.org/S4377196871","display_name":"Institutional Knowledge (InK) - Institutional Knowledge at Singapore Management University (Singapore Management University)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I79891267","host_organization_name":"Singapore Management University","host_organization_lineage":["https://openalex.org/I79891267"],"host_organization_lineage_names":[],"type":"repository"},"license":"other-oa","license_id":"https://openalex.org/licenses/other-oa","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"https://doi.org/10.1109/CVPRW.2009.5206518","raw_type":"Conference Proceeding Article"},{"id":"pmh:oai:CiteSeerX.psu:10.1.1.321.2591","is_oa":false,"landing_page_url":"http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.321.2591","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"http://vireo.cs.cityu.edu.hk/papers/cvpr09-xiang.pdf","raw_type":"text"}],"best_oa_location":{"id":"pmh:oai:ink.library.smu.edu.sg:sis_research-7603","is_oa":true,"landing_page_url":"https://ink.library.smu.edu.sg/cgi/viewcontent.cgi?article=7603&context=sis_research","pdf_url":null,"source":{"id":"https://openalex.org/S4377196871","display_name":"Institutional Knowledge (InK) - Institutional Knowledge at Singapore Management University (Singapore Management University)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I79891267","host_organization_name":"Singapore Management University","host_organization_lineage":["https://openalex.org/I79891267"],"host_organization_lineage_names":[],"type":"repository"},"license":"other-oa","license_id":"https://openalex.org/licenses/other-oa","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"https://doi.org/10.1109/CVPRW.2009.5206518","raw_type":"Conference Proceeding Article"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":23,"referenced_works":["https://openalex.org/W1507028917","https://openalex.org/W1666447063","https://openalex.org/W2043196868","https://openalex.org/W2104252248","https://openalex.org/W2115517344","https://openalex.org/W2119967926","https://openalex.org/W2125238156","https://openalex.org/W2127411609","https://openalex.org/W2131279070","https://openalex.org/W2133510502","https://openalex.org/W2137569015","https://openalex.org/W2137918516","https://openalex.org/W2138454757","https://openalex.org/W2140302574","https://openalex.org/W2143854982","https://openalex.org/W2154107515","https://openalex.org/W2156336347","https://openalex.org/W2283195891","https://openalex.org/W6637249095","https://openalex.org/W6677204712","https://openalex.org/W6678852649","https://openalex.org/W6679895941","https://openalex.org/W6680263565"],"related_works":["https://openalex.org/W4365211920","https://openalex.org/W3014948380","https://openalex.org/W4380551139","https://openalex.org/W4317695495","https://openalex.org/W4287117424","https://openalex.org/W4387506531","https://openalex.org/W4238433571","https://openalex.org/W2967848559","https://openalex.org/W4299831724","https://openalex.org/W4283803360"],"abstract_inverted_index":{"Much":[0],"research":[1],"effort":[2],"on":[3,11,80,128,154,173],"Automatic":[4],"Image":[5],"Annotation":[6],"(AIA)":[7],"has":[8],"been":[9],"focused":[10],"Generative":[12],"Model,":[13],"due":[14,50],"to":[15,51,109],"its":[16],"well":[17,27],"formed":[18],"theory":[19],"and":[20,29,57,90,105,131,142,166,170],"competitive":[21],"performance":[22],"as":[23],"compared":[24],"with":[25],"many":[26],"designed":[28],"sophisticated":[30],"methods.":[31],"However,":[32],"when":[33],"considering":[34],"semantic":[35,64,87],"context":[36,65,88],"for":[37,61,86,124,135],"annotation,":[38],"the":[39,43,52,63,67,101,111,183,188],"model":[40,106,130,143],"suffers":[41],"from":[42,93],"weak":[44],"learning":[45,59,112],"ability.":[46],"This":[47,176],"is":[48,145,177],"mainly":[49],"lack":[53],"of":[54,114,187],"parameter":[55,103,140],"setting":[56],"appropriate":[58],"strategy":[60],"characterizing":[62],"in":[66,147,168],"traditional":[68,115],"generative":[69,116,129],"model.":[70,117],"In":[71],"this":[72],"paper,":[73],"we":[74,99,119,163],"present":[75],"a":[76,178],"new":[77,121],"approach":[78],"based":[79,127],"Multiple":[81],"Markov":[82],"Random":[83],"Fields":[84],"(MRF)":[85],"modeling":[89,126],"learning.":[91],"Differing":[92],"previous":[94],"MRF":[95],"related":[96],"AIA":[97],"approach,":[98],"explore":[100],"optimal":[102,149],"estimation":[104,141],"inference":[107,144],"systematically":[108],"leverage":[110],"power":[113],"Specifically,":[118],"propose":[120],"potential":[122],"function":[123],"site":[125],"build":[132],"local":[133,148],"graphs":[134],"each":[136],"annotation":[137],"keyword.":[138],"The":[139],"performed":[146],"sense.":[150],"We":[151],"conduct":[152],"experiments":[153],"commonly":[155],"used":[156],"benchmarks.":[157],"On":[158],"Corel":[159],"5000":[160],"images":[161],"[3],":[162],"achieved":[164],"0.36":[165],"0.31":[167],"recall":[169],"precision":[171],"respectively":[172],"263":[174],"keywords.":[175],"very":[179],"significant":[180],"improvement":[181],"over":[182],"best":[184],"reported":[185],"result":[186],"current":[189],"state-of-the-art":[190],"approaches.":[191]},"counts_by_year":[{"year":2024,"cited_by_count":1},{"year":2023,"cited_by_count":3},{"year":2022,"cited_by_count":2},{"year":2021,"cited_by_count":2},{"year":2020,"cited_by_count":3},{"year":2018,"cited_by_count":4},{"year":2017,"cited_by_count":7},{"year":2016,"cited_by_count":2},{"year":2015,"cited_by_count":6},{"year":2014,"cited_by_count":18},{"year":2013,"cited_by_count":7},{"year":2012,"cited_by_count":12}],"updated_date":"2026-04-04T16:13:02.066488","created_date":"2025-10-10T00:00:00"}