{"id":"https://openalex.org/W2011890309","doi":"https://doi.org/10.1109/cvpr.2010.5540015","title":"Semantic context modeling with maximal margin Conditional Random Fields for automatic image annotation","display_name":"Semantic context modeling with maximal margin Conditional Random Fields for automatic image annotation","publication_year":2010,"publication_date":"2010-06-01","ids":{"openalex":"https://openalex.org/W2011890309","doi":"https://doi.org/10.1109/cvpr.2010.5540015","mag":"2011890309"},"language":"en","primary_location":{"id":"doi:10.1109/cvpr.2010.5540015","is_oa":false,"landing_page_url":"https://doi.org/10.1109/cvpr.2010.5540015","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2010 IEEE Computer Society 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=7604&amp;amp;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":["Fudan University, Shanghai, China","Fudan Unviersity, Shanghai, China"],"affiliations":[{"raw_affiliation_string":"Fudan University, Shanghai, China","institution_ids":["https://openalex.org/I24943067"]},{"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":["Fudan University, Shanghai, China","Fudan Unviersity, Shanghai, China"],"affiliations":[{"raw_affiliation_string":"Fudan University, Shanghai, China","institution_ids":["https://openalex.org/I24943067"]},{"raw_affiliation_string":"Fudan Unviersity, Shanghai, China","institution_ids":["https://openalex.org/I24943067"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5010021609","display_name":"Zuotao Liu","orcid":null},"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":"Zuotao Liu","raw_affiliation_strings":["Fudan University, Shanghai, China","Fudan Unviersity, Shanghai, China"],"affiliations":[{"raw_affiliation_string":"Fudan University, Shanghai, China","institution_ids":["https://openalex.org/I24943067"]},{"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 United University (NUU), Singapore","National Univ., Singapore"],"affiliations":[{"raw_affiliation_string":"National United University (NUU), Singapore","institution_ids":[]},{"raw_affiliation_string":"National Univ., 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, HongKong, China"],"affiliations":[{"raw_affiliation_string":"City University, Hong Kong, China","institution_ids":["https://openalex.org/I168719708"]},{"raw_affiliation_string":"City University, HongKong, China","institution_ids":["https://openalex.org/I168719708"]}]}],"institutions":[],"countries_distinct_count":3,"institutions_distinct_count":5,"corresponding_author_ids":["https://openalex.org/A5101595454"],"corresponding_institution_ids":["https://openalex.org/I24943067"],"apc_list":null,"apc_paid":null,"fwci":5.4957,"has_fulltext":false,"cited_by_count":35,"citation_normalized_percentile":{"value":0.96236372,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":90,"max":99},"biblio":{"volume":null,"issue":null,"first_page":"3368","last_page":"3375"},"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":0.9998999834060669,"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/T10627","display_name":"Advanced Image and Video Retrieval 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/T10824","display_name":"Image Retrieval and Classification Techniques","score":0.9994999766349792,"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/T10689","display_name":"Remote-Sensing Image Classification","score":0.9975000023841858,"subfield":{"id":"https://openalex.org/subfields/2214","display_name":"Media Technology"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/conditional-random-field","display_name":"Conditional random field","score":0.7969779968261719},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7857597470283508},{"id":"https://openalex.org/keywords/discriminative-model","display_name":"Discriminative model","score":0.7766937017440796},{"id":"https://openalex.org/keywords/margin","display_name":"Margin (machine learning)","score":0.6089429259300232},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6016125082969666},{"id":"https://openalex.org/keywords/hinge-loss","display_name":"Hinge loss","score":0.5315827131271362},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.47698694467544556},{"id":"https://openalex.org/keywords/context","display_name":"Context (archaeology)","score":0.4740947186946869},{"id":"https://openalex.org/keywords/scalability","display_name":"Scalability","score":0.4727918803691864},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.464263916015625},{"id":"https://openalex.org/keywords/annotation","display_name":"Annotation","score":0.42277786135673523},{"id":"https://openalex.org/keywords/context-model","display_name":"Context model","score":0.4147694408893585},{"id":"https://openalex.org/keywords/natural-language-processing","display_name":"Natural language processing","score":0.32952719926834106},{"id":"https://openalex.org/keywords/support-vector-machine","display_name":"Support vector machine","score":0.23730707168579102}],"concepts":[{"id":"https://openalex.org/C152565575","wikidata":"https://www.wikidata.org/wiki/Q1124538","display_name":"Conditional random field","level":2,"score":0.7969779968261719},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7857597470283508},{"id":"https://openalex.org/C97931131","wikidata":"https://www.wikidata.org/wiki/Q5282087","display_name":"Discriminative model","level":2,"score":0.7766937017440796},{"id":"https://openalex.org/C774472","wikidata":"https://www.wikidata.org/wiki/Q6760393","display_name":"Margin (machine learning)","level":2,"score":0.6089429259300232},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6016125082969666},{"id":"https://openalex.org/C39891107","wikidata":"https://www.wikidata.org/wiki/Q5767098","display_name":"Hinge loss","level":3,"score":0.5315827131271362},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.47698694467544556},{"id":"https://openalex.org/C2779343474","wikidata":"https://www.wikidata.org/wiki/Q3109175","display_name":"Context (archaeology)","level":2,"score":0.4740947186946869},{"id":"https://openalex.org/C48044578","wikidata":"https://www.wikidata.org/wiki/Q727490","display_name":"Scalability","level":2,"score":0.4727918803691864},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.464263916015625},{"id":"https://openalex.org/C2776321320","wikidata":"https://www.wikidata.org/wiki/Q857525","display_name":"Annotation","level":2,"score":0.42277786135673523},{"id":"https://openalex.org/C183322885","wikidata":"https://www.wikidata.org/wiki/Q17007702","display_name":"Context model","level":3,"score":0.4147694408893585},{"id":"https://openalex.org/C204321447","wikidata":"https://www.wikidata.org/wiki/Q30642","display_name":"Natural language processing","level":1,"score":0.32952719926834106},{"id":"https://openalex.org/C12267149","wikidata":"https://www.wikidata.org/wiki/Q282453","display_name":"Support vector machine","level":2,"score":0.23730707168579102},{"id":"https://openalex.org/C77088390","wikidata":"https://www.wikidata.org/wiki/Q8513","display_name":"Database","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},{"id":"https://openalex.org/C151730666","wikidata":"https://www.wikidata.org/wiki/Q7205","display_name":"Paleontology","level":1,"score":0.0},{"id":"https://openalex.org/C2781238097","wikidata":"https://www.wikidata.org/wiki/Q175026","display_name":"Object (grammar)","level":2,"score":0.0}],"mesh":[],"locations_count":3,"locations":[{"id":"doi:10.1109/cvpr.2010.5540015","is_oa":false,"landing_page_url":"https://doi.org/10.1109/cvpr.2010.5540015","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition","raw_type":"proceedings-article"},{"id":"pmh:oai:ink.library.smu.edu.sg:sis_research-7604","is_oa":true,"landing_page_url":"https://ink.library.smu.edu.sg/cgi/viewcontent.cgi?article=7604&amp;amp;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/CVPR.2010.5540015","raw_type":"Conference Proceeding Article"},{"id":"pmh:oai:scholarbank.nus.edu.sg:10635/40446","is_oa":false,"landing_page_url":"http://scholarbank.nus.edu.sg/handle/10635/40446","pdf_url":null,"source":{"id":"https://openalex.org/S7407052290","display_name":"National University of Singapore","issn_l":null,"issn":[],"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":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"Scopus","raw_type":"Conference Paper"}],"best_oa_location":{"id":"pmh:oai:ink.library.smu.edu.sg:sis_research-7604","is_oa":true,"landing_page_url":"https://ink.library.smu.edu.sg/cgi/viewcontent.cgi?article=7604&amp;amp;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/CVPR.2010.5540015","raw_type":"Conference Proceeding Article"},"sustainable_development_goals":[{"score":0.7300000190734863,"display_name":"Reduced inequalities","id":"https://metadata.un.org/sdg/10"}],"awards":[],"funders":[{"id":"https://openalex.org/F4320321001","display_name":"National Natural Science Foundation of China","ror":"https://ror.org/01h0zpd94"}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":33,"referenced_works":["https://openalex.org/W1507028917","https://openalex.org/W1554544485","https://openalex.org/W1566135517","https://openalex.org/W1666447063","https://openalex.org/W1986482242","https://openalex.org/W2037407504","https://openalex.org/W2081293863","https://openalex.org/W2096071754","https://openalex.org/W2105644991","https://openalex.org/W2106962004","https://openalex.org/W2107034620","https://openalex.org/W2110306668","https://openalex.org/W2115517344","https://openalex.org/W2122829955","https://openalex.org/W2125238156","https://openalex.org/W2127411609","https://openalex.org/W2137918516","https://openalex.org/W2140804075","https://openalex.org/W2143854982","https://openalex.org/W2147880316","https://openalex.org/W2156336347","https://openalex.org/W2156909104","https://openalex.org/W2159564241","https://openalex.org/W2161914416","https://openalex.org/W2283195891","https://openalex.org/W2429914308","https://openalex.org/W2536305071","https://openalex.org/W6637249095","https://openalex.org/W6674626981","https://openalex.org/W6675760969","https://openalex.org/W6677204712","https://openalex.org/W6678852649","https://openalex.org/W6683825394"],"related_works":["https://openalex.org/W2361861616","https://openalex.org/W4389116644","https://openalex.org/W2153315159","https://openalex.org/W2356597680","https://openalex.org/W3103844505","https://openalex.org/W2163278254","https://openalex.org/W155708904","https://openalex.org/W1574213390","https://openalex.org/W4389523750","https://openalex.org/W2145850538"],"abstract_inverted_index":{"Context":[0],"modeling":[1,74],"for":[2,71,145,181],"Vision":[3],"Recognition":[4],"and":[5,21,30,83,105,177],"Automatic":[6],"Image":[7],"Annotation":[8],"(AIA)":[9],"has":[10,25],"attracted":[11],"increasing":[12],"attentions":[13],"in":[14,28,75,108],"recent":[15],"years.":[16],"For":[17],"various":[18],"contextual":[19,119,166],"information":[20],"resources,":[22],"semantic":[23,72,81,99,103,122],"context":[24,73],"been":[26],"exploited":[27],"AIA":[29],"brings":[31],"promising":[32],"results.":[33],"However,":[34],"previous":[35],"works":[36],"either":[37],"casted":[38],"the":[39,51,96,152,190],"problem":[40],"into":[41],"structural":[42],"classification":[43],"or":[44,55],"adopted":[45],"multi-layer":[46],"modeling,":[47],"which":[48,77,134],"suffer":[49],"from":[50,101],"problems":[52,162],"of":[53,158],"scalability":[54],"model":[56,70,94,118,153],"efficiency.":[57],"In":[58],"this":[59],"paper,":[60],"we":[61,113],"propose":[62,138],"a":[63,88,139,156],"novel":[64,140],"discriminative":[65,132],"Conditional":[66],"Random":[67],"Field":[68],"(CRF)":[69],"AIA,":[76],"is":[78],"built":[79],"over":[80],"concepts":[82,100],"treats":[84],"an":[85,109],"image":[86],"as":[87],"whole":[89],"observation":[90],"without":[91],"segmentation.":[92],"Our":[93],"captures":[95],"interactions":[97],"between":[98,121],"both":[102],"level":[104,107],"visual":[106],"integrated":[110],"manner.":[111],"Specifically,":[112],"employ":[114],"graph":[115],"structure":[116],"to":[117,137],"relationships":[120],"concepts.":[123],"The":[124,168,183],"potential":[125],"functions":[126],"are":[127,170],"designed":[128],"based":[129],"on":[130,172,198],"linear":[131],"models,":[133],"enables":[135],"us":[136],"decoupled":[141],"hinge":[142],"loss":[143],"function":[144],"maximal":[146],"margin":[147],"parameter":[148],"estimation.":[149],"We":[150],"train":[151],"by":[154],"solving":[155],"set":[157],"independent":[159],"quadratic":[160],"programming":[161],"with":[163,189],"our":[164,193],"derived":[165],"kernel.":[167],"experiments":[169],"conducted":[171],"commonly":[173],"used":[174],"benchmarks:":[175],"Corel":[176],"TRECVID":[178],"data":[179],"sets":[180],"evaluation.":[182],"experimental":[184],"results":[185],"show":[186],"that":[187],"compared":[188],"state-of-the-art":[191],"methods,":[192],"method":[194],"achieves":[195],"significant":[196],"improvement":[197],"annotation":[199],"performance.":[200]},"counts_by_year":[{"year":2019,"cited_by_count":1},{"year":2018,"cited_by_count":3},{"year":2017,"cited_by_count":1},{"year":2016,"cited_by_count":4},{"year":2015,"cited_by_count":4},{"year":2014,"cited_by_count":5},{"year":2013,"cited_by_count":6},{"year":2012,"cited_by_count":5}],"updated_date":"2026-04-05T17:49:38.594831","created_date":"2025-10-10T00:00:00"}
