{"id":"https://openalex.org/W2103956678","doi":"https://doi.org/10.1109/cvpr.2012.6247995","title":"Learning object relationships via graph-based context model","display_name":"Learning object relationships via graph-based context model","publication_year":2012,"publication_date":"2012-06-01","ids":{"openalex":"https://openalex.org/W2103956678","doi":"https://doi.org/10.1109/cvpr.2012.6247995","mag":"2103956678"},"language":"en","primary_location":{"id":"doi:10.1109/cvpr.2012.6247995","is_oa":false,"landing_page_url":"https://doi.org/10.1109/cvpr.2012.6247995","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2012 IEEE Conference on Computer Vision and Pattern Recognition","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/A5023870894","display_name":"Heesoo Myeong","orcid":null},"institutions":[{"id":"https://openalex.org/I139264467","display_name":"Seoul National University","ror":"https://ror.org/04h9pn542","country_code":"KR","type":"education","lineage":["https://openalex.org/I139264467"]}],"countries":["KR"],"is_corresponding":true,"raw_author_name":"Heesoo Myeong","raw_affiliation_strings":["Department of EECS, ASRI, Seoul National University, Seoul, South Korea"],"affiliations":[{"raw_affiliation_string":"Department of EECS, ASRI, Seoul National University, Seoul, South Korea","institution_ids":["https://openalex.org/I139264467"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5073032930","display_name":"Ju Yong Chang","orcid":"https://orcid.org/0000-0003-3710-7314"},"institutions":[{"id":"https://openalex.org/I142401562","display_name":"Electronics and Telecommunications Research Institute","ror":"https://ror.org/03ysstz10","country_code":"KR","type":"facility","lineage":["https://openalex.org/I142401562","https://openalex.org/I2801339556","https://openalex.org/I4210144908","https://openalex.org/I4387152098"]},{"id":"https://openalex.org/I139264467","display_name":"Seoul National University","ror":"https://ror.org/04h9pn542","country_code":"KR","type":"education","lineage":["https://openalex.org/I139264467"]}],"countries":["KR"],"is_corresponding":false,"raw_author_name":"Ju Yong Chang","raw_affiliation_strings":["Department of EECS, ASRI, Seoul National University, Seoul, South Korea","Electronics and Telecommunications Research Institute, South Korea"],"affiliations":[{"raw_affiliation_string":"Department of EECS, ASRI, Seoul National University, Seoul, South Korea","institution_ids":["https://openalex.org/I139264467"]},{"raw_affiliation_string":"Electronics and Telecommunications Research Institute, South Korea","institution_ids":["https://openalex.org/I142401562"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5046504049","display_name":"Kyoung Mu Lee","orcid":"https://orcid.org/0000-0001-7210-1036"},"institutions":[{"id":"https://openalex.org/I139264467","display_name":"Seoul National University","ror":"https://ror.org/04h9pn542","country_code":"KR","type":"education","lineage":["https://openalex.org/I139264467"]}],"countries":["KR"],"is_corresponding":false,"raw_author_name":"Kyoung Mu Lee","raw_affiliation_strings":["Department of EECS, ASRI, Seoul National University, Seoul, South Korea"],"affiliations":[{"raw_affiliation_string":"Department of EECS, ASRI, Seoul National University, Seoul, South Korea","institution_ids":["https://openalex.org/I139264467"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5023870894"],"corresponding_institution_ids":["https://openalex.org/I139264467"],"apc_list":null,"apc_paid":null,"fwci":5.8318,"has_fulltext":false,"cited_by_count":39,"citation_normalized_percentile":{"value":0.96785792,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":89,"max":99},"biblio":{"volume":null,"issue":null,"first_page":"2727","last_page":"2734"},"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":0.9991999864578247,"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/T11307","display_name":"Domain Adaptation and Few-Shot Learning","score":0.9958999752998352,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"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/pairwise-comparison","display_name":"Pairwise comparison","score":0.7776240706443787},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7670751214027405},{"id":"https://openalex.org/keywords/markov-random-field","display_name":"Markov random field","score":0.7591484785079956},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6228433847427368},{"id":"https://openalex.org/keywords/context-model","display_name":"Context model","score":0.5848885774612427},{"id":"https://openalex.org/keywords/graph","display_name":"Graph","score":0.5604922771453857},{"id":"https://openalex.org/keywords/segmentation","display_name":"Segmentation","score":0.5407580733299255},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.5143864750862122},{"id":"https://openalex.org/keywords/context","display_name":"Context (archaeology)","score":0.447343111038208},{"id":"https://openalex.org/keywords/image-segmentation","display_name":"Image segmentation","score":0.437588095664978},{"id":"https://openalex.org/keywords/markov-chain","display_name":"Markov chain","score":0.42436888813972473},{"id":"https://openalex.org/keywords/spatial-contextual-awareness","display_name":"Spatial contextual awareness","score":0.420148104429245},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.3723897337913513},{"id":"https://openalex.org/keywords/object","display_name":"Object (grammar)","score":0.35623055696487427},{"id":"https://openalex.org/keywords/theoretical-computer-science","display_name":"Theoretical computer science","score":0.345261812210083}],"concepts":[{"id":"https://openalex.org/C184898388","wikidata":"https://www.wikidata.org/wiki/Q1435712","display_name":"Pairwise comparison","level":2,"score":0.7776240706443787},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7670751214027405},{"id":"https://openalex.org/C2778045648","wikidata":"https://www.wikidata.org/wiki/Q176827","display_name":"Markov random field","level":4,"score":0.7591484785079956},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6228433847427368},{"id":"https://openalex.org/C183322885","wikidata":"https://www.wikidata.org/wiki/Q17007702","display_name":"Context model","level":3,"score":0.5848885774612427},{"id":"https://openalex.org/C132525143","wikidata":"https://www.wikidata.org/wiki/Q141488","display_name":"Graph","level":2,"score":0.5604922771453857},{"id":"https://openalex.org/C89600930","wikidata":"https://www.wikidata.org/wiki/Q1423946","display_name":"Segmentation","level":2,"score":0.5407580733299255},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.5143864750862122},{"id":"https://openalex.org/C2779343474","wikidata":"https://www.wikidata.org/wiki/Q3109175","display_name":"Context (archaeology)","level":2,"score":0.447343111038208},{"id":"https://openalex.org/C124504099","wikidata":"https://www.wikidata.org/wiki/Q56933","display_name":"Image segmentation","level":3,"score":0.437588095664978},{"id":"https://openalex.org/C98763669","wikidata":"https://www.wikidata.org/wiki/Q176645","display_name":"Markov chain","level":2,"score":0.42436888813972473},{"id":"https://openalex.org/C64754055","wikidata":"https://www.wikidata.org/wiki/Q7574053","display_name":"Spatial contextual awareness","level":2,"score":0.420148104429245},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3723897337913513},{"id":"https://openalex.org/C2781238097","wikidata":"https://www.wikidata.org/wiki/Q175026","display_name":"Object (grammar)","level":2,"score":0.35623055696487427},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.345261812210083},{"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}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1109/cvpr.2012.6247995","is_oa":false,"landing_page_url":"https://doi.org/10.1109/cvpr.2012.6247995","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2012 IEEE Conference on Computer Vision and Pattern Recognition","raw_type":"proceedings-article"},{"id":"pmh:oai:CiteSeerX.psu:10.1.1.701.6455","is_oa":false,"landing_page_url":"http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.701.6455","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"http://cv.snu.ac.kr/publication/conf/2012/LORGCM_CVPR2012.pdf","raw_type":"text"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":41,"referenced_works":["https://openalex.org/W1516887802","https://openalex.org/W1532257412","https://openalex.org/W1542723449","https://openalex.org/W1748750709","https://openalex.org/W1800471361","https://openalex.org/W1866372755","https://openalex.org/W1999478155","https://openalex.org/W2011404628","https://openalex.org/W2017313218","https://openalex.org/W2074078071","https://openalex.org/W2077493928","https://openalex.org/W2081293863","https://openalex.org/W2101309634","https://openalex.org/W2107640784","https://openalex.org/W2110764733","https://openalex.org/W2113137767","https://openalex.org/W2121043743","https://openalex.org/W2123126017","https://openalex.org/W2125310925","https://openalex.org/W2125849446","https://openalex.org/W2128962821","https://openalex.org/W2137117160","https://openalex.org/W2140435402","https://openalex.org/W2145607950","https://openalex.org/W2154455818","https://openalex.org/W2157035885","https://openalex.org/W2160254296","https://openalex.org/W2160419739","https://openalex.org/W2162915993","https://openalex.org/W2536208356","https://openalex.org/W6630825005","https://openalex.org/W6632547051","https://openalex.org/W6637730771","https://openalex.org/W6638538433","https://openalex.org/W6676328599","https://openalex.org/W6677865154","https://openalex.org/W6678684981","https://openalex.org/W6680382358","https://openalex.org/W6682494755","https://openalex.org/W6682855966","https://openalex.org/W6683656881"],"related_works":["https://openalex.org/W4233585817","https://openalex.org/W2016045932","https://openalex.org/W1675950995","https://openalex.org/W2188882668","https://openalex.org/W2004379491","https://openalex.org/W2088323302","https://openalex.org/W1998269854","https://openalex.org/W2083140487","https://openalex.org/W2140435402","https://openalex.org/W2103956678"],"abstract_inverted_index":{"In":[0],"this":[1],"paper,":[2],"we":[3],"propose":[4],"a":[5,35,46,54,58,92],"novel":[6],"framework":[7,97],"for":[8,28],"modeling":[9],"image-dependent":[10],"contextual":[11,25,40],"relationships":[12,26],"using":[13],"graph-based":[14,123],"context":[15,36,55,72,89,124],"model.":[16],"This":[17],"approach":[18],"enables":[19],"us":[20],"to":[21,68],"selectively":[22],"utilize":[23],"the":[24,43,70,81,87,99,106,121,127],"suitable":[27],"an":[29],"input":[30,82],"query":[31],"image.":[32,83],"We":[33],"introduce":[34],"link":[37,56],"view":[38],"of":[39,48,61,74,77,101],"knowledge,":[41],"where":[42],"relationship":[44],"between":[45],"pair":[47],"annotated":[49],"regions":[50,79],"is":[51],"represented":[52],"as":[53],"on":[57,115],"similarity":[59],"graph":[60],"regions.":[62],"Link":[63],"analysis":[64],"techniques":[65],"are":[66],"used":[67],"estimate":[69],"pairwise":[71,102],"scores":[73,90],"all":[75],"pairs":[76],"unlabeled":[78],"in":[80,98],"Our":[84],"system":[85],"integrates":[86],"learned":[88],"into":[91],"Markov":[93],"Random":[94],"Field":[95],"(MRF)":[96],"form":[100],"cost":[103],"and":[104],"infers":[105],"semantic":[107],"segmentation":[108,118],"result":[109],"by":[110],"MRF":[111],"optimization.":[112],"Experimental":[113],"results":[114],"object":[116],"class":[117],"show":[119],"that":[120],"proposed":[122],"model":[125],"outperforms":[126],"current":[128],"state-of-the-art":[129],"methods.":[130]},"counts_by_year":[{"year":2022,"cited_by_count":1},{"year":2021,"cited_by_count":2},{"year":2020,"cited_by_count":1},{"year":2019,"cited_by_count":1},{"year":2018,"cited_by_count":3},{"year":2017,"cited_by_count":2},{"year":2016,"cited_by_count":8},{"year":2015,"cited_by_count":9},{"year":2014,"cited_by_count":5},{"year":2013,"cited_by_count":7}],"updated_date":"2026-04-04T16:13:02.066488","created_date":"2025-10-10T00:00:00"}