{"id":"https://openalex.org/W2114930007","doi":null,"title":"Scene Segmentation with CRFs Learned from Partially Labeled Images","display_name":"Scene Segmentation with CRFs Learned from Partially Labeled Images","publication_year":2007,"publication_date":"2007-12-03","ids":{"openalex":"https://openalex.org/W2114930007","mag":"2114930007"},"language":"en","primary_location":{"id":"pmh:oai:CiteSeerX.psu:10.1.1.143.7767","doi":null,"is_oa":false,"landing_page_url":"http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.143.7767","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"http://books.nips.cc/papers/files/nips20/NIPS2007_0243.pdf","raw_type":"text"},"volume":null,"type":"article","is_traditional":true,"indexed_in":[],"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/A5062363302","display_name":"Bill Triggs","orcid":"https://orcid.org/0000-0003-4116-6296","observed_orcids":["https://orcid.org/0000-0003-4116-6296"]},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Bill Triggs","raw_affiliation_strings":["Learning and recognition in vision"],"raw_orcid":"https://orcid.org/0000-0003-4116-6296","affiliations":[{"raw_affiliation_string":"Learning and recognition in vision","institution_ids":[]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5040312210","display_name":"Jakob Jozef Verbeek","orcid":"https://orcid.org/0000-0003-1419-1816","observed_orcids":["https://orcid.org/0000-0003-1419-1816"]},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Jakob Verbeek","raw_affiliation_strings":["Learning and recognition in vision"],"raw_orcid":"https://orcid.org/0000-0003-1419-1816","affiliations":[{"raw_affiliation_string":"Learning and recognition in vision","institution_ids":[]}]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":0,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":13.6267,"has_fulltext":false,"cited_by_count":142,"citation_normalized_percentile":{"value":0.98952754,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":88,"max":100},"biblio":{"volume":"20","issue":null,"first_page":"1553","last_page":"1560"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10036","display_name":"Advanced Neural Network Applications","score":0.4846000075340271,"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/T10036","display_name":"Advanced Neural Network Applications","score":0.4846000075340271,"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/T11303","display_name":"Bayesian Modeling and Causal Inference","score":0.09040000289678574,"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"}},{"id":"https://openalex.org/T10627","display_name":"Advanced Image and Video Retrieval Techniques","score":0.08810000121593475,"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/scene-segmentation","display_name":"scene segmentation","score":0.843999981880188},{"id":"https://openalex.org/keywords/conditional-random-fields","display_name":"conditional random fields","score":0.9599999785423279},{"id":"https://openalex.org/keywords/semi-supervised-learning","display_name":"semi-supervised learning","score":0.7110000252723694},{"id":"https://openalex.org/keywords/loopy-belief-propagation","display_name":"loopy belief propagation","score":0.7940000295639038},{"id":"https://openalex.org/keywords/bethe-free-energy","display_name":"Bethe free energy","score":0.9350000023841858},{"id":"https://openalex.org/keywords/gradient-ascent","display_name":"gradient ascent","score":0.5},{"id":"https://openalex.org/keywords/image-annotation","display_name":"image annotation","score":0.5740000009536743},{"id":"https://openalex.org/keywords/log-likelihood-maximization","display_name":"log-likelihood maximization","score":0.7289999723434448}],"concepts":[{"id":"https://openalex.org/C2775953691","wikidata":"https://www.wikidata.org/wiki/Q5013874","display_name":"CRFS","level":3,"score":0.9832909107208252},{"id":"https://openalex.org/C152565575","wikidata":"https://www.wikidata.org/wiki/Q1124538","display_name":"Conditional random field","level":2,"score":0.9120545387268066},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7197588682174683},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6748304963111877},{"id":"https://openalex.org/C89600930","wikidata":"https://www.wikidata.org/wiki/Q1423946","display_name":"Segmentation","level":2,"score":0.634716272354126},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.5922647714614868},{"id":"https://openalex.org/C2779343474","wikidata":"https://www.wikidata.org/wiki/Q3109175","display_name":"Context (archaeology)","level":2,"score":0.5447486639022827},{"id":"https://openalex.org/C124504099","wikidata":"https://www.wikidata.org/wiki/Q56933","display_name":"Image segmentation","level":3,"score":0.4575125575065613},{"id":"https://openalex.org/C152948882","wikidata":"https://www.wikidata.org/wiki/Q4060686","display_name":"Belief propagation","level":3,"score":0.45009782910346985},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.4256765842437744},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.10106861591339111},{"id":"https://openalex.org/C151730666","wikidata":"https://www.wikidata.org/wiki/Q7205","display_name":"Paleontology","level":1,"score":0.0},{"id":"https://openalex.org/C57273362","wikidata":"https://www.wikidata.org/wiki/Q576722","display_name":"Decoding methods","level":2,"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":"pmh:oai:CiteSeerX.psu:10.1.1.143.7767","doi":null,"is_oa":false,"landing_page_url":"http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.143.7767","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"http://books.nips.cc/papers/files/nips20/NIPS2007_0243.pdf","raw_type":"text"},{"id":"mag:2114930007","doi":null,"is_oa":false,"landing_page_url":"https://hal.archives-ouvertes.fr/docs/00/58/49/77/PDF/verbeek08nips.pdf","pdf_url":null,"source":{"id":"https://openalex.org/S4363606243","display_name":"neural information processing systems","issn_l":"1049-5258","issn":["1049-5258"],"is_oa":false,"is_in_doaj":false,"is_core":true,"listed_in":["cwts-core","norway-2"],"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"conference"},"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":false,"raw_source_name":"Neural Information Processing Systems","raw_type":null},{"id":"mag:2562510222","doi":null,"is_oa":false,"landing_page_url":"http://class.inrialpes.fr/pub/219-verbeek-nips07.pdf","pdf_url":null,"source":{"id":"https://openalex.org/S4363606243","display_name":"neural information processing systems","issn_l":"1049-5258","issn":["1049-5258"],"is_oa":false,"is_in_doaj":false,"is_core":true,"listed_in":["cwts-core","norway-2"],"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"conference"},"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":false,"raw_source_name":"Neural Information Processing Systems","raw_type":null}],"best_oa_location":null,"sustainable_development_goals":[],"study_designs":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":16,"referenced_works":["https://openalex.org/W1506851152","https://openalex.org/W1528789833","https://openalex.org/W1597084354","https://openalex.org/W2019599312","https://openalex.org/W2020999234","https://openalex.org/W2095844239","https://openalex.org/W2100158203","https://openalex.org/W2120294885","https://openalex.org/W2123023145","https://openalex.org/W2124351162","https://openalex.org/W2128962821","https://openalex.org/W2141111254","https://openalex.org/W2147880316","https://openalex.org/W2151103935","https://openalex.org/W2151322733","https://openalex.org/W2167090521"],"related_works":["https://openalex.org/W2562510222","https://openalex.org/W7097933438","https://openalex.org/W108893130","https://openalex.org/W1976323859","https://openalex.org/W2078319884","https://openalex.org/W1897240504","https://openalex.org/W1653341947","https://openalex.org/W7096269714","https://openalex.org/W2007695859","https://openalex.org/W2089374915"],"abstract_inverted_index":{"Conditional":[0],"Random":[1],"Fields":[2],"(CRFs)":[3],"are":[4,190],"an":[5],"effective":[6,169],"tool":[7],"for":[8,104,143],"a":[9,61,102],"variety":[10],"of":[11,51,82,123],"different":[12,197],"data":[13],"segmentation":[14],"and":[15,32,72,96,146,149,177],"labeling":[16,42,65],"tasks":[17],"including":[18],"visual":[19],"scene":[20,64],"interpretation,":[21],"which":[22,92],"seeks":[23],"to":[24,37,46,98,138,154,159,192],"partition":[25],"images":[26],"into":[27],"their":[28],"constituent":[29],"semantic-level":[30],"regions":[31],"assign":[33],"appropriate":[34],"class":[35],"labels":[36,118],"each":[38],"region.":[39],"For":[40],"accurate":[41],"it":[43],"is":[44,136,157],"important":[45],"capture":[47],"the":[48,52,76,116,121,124,140,144,150,155,161,185,193],"global":[49],"context":[50],"image":[53,78,198],"as":[54,56],"well":[55],"local":[57,70],"information.":[58],"We":[59,100],"introduce":[60,101],"CRF":[62,86],"based":[63],"model":[66],"that":[67,120,168,178],"incorporates":[68],"both":[69],"features":[71,73,182],"aggregated":[74],"over":[75],"whole":[77],"or":[79],"large":[80],"sections":[81],"it.":[83],"Secondly,":[84],"traditional":[85],"learning":[87,105],"requires":[88],"fully":[89],"labeled":[90],"datasets":[91,108],"can":[93,127,171],"be":[94,128,172],"costly":[95],"troublesome":[97],"produce.":[99],"method":[103],"CRFs":[106],"from":[107,174],"with":[109],"many":[110],"unlabeled":[111],"nodes":[112],"by":[113,130],"marginalizing":[114],"out":[115],"unknown":[117],"so":[119],"log-likelihood":[122,147,156],"known":[125],"ones":[126],"maximized":[129],"gradient":[131,145],"ascent.":[132],"Loopy":[133],"Belief":[134],"Propagation":[135],"used":[137],"approximate":[139],"marginals":[141],"needed":[142],"calculations":[148],"Bethe":[151],"free-energy":[152],"approximation":[153],"monitored":[158],"control":[160],"step":[162],"size.":[163],"Our":[164],"experimental":[165],"results":[166],"show":[167],"models":[170],"learned":[173],"fragmentary":[175],"labelings":[176],"incorporating":[179],"top-down":[180],"aggregate":[181],"significantly":[183],"improves":[184],"segmentations.":[186],"The":[187],"resulting":[188],"segmentations":[189],"compared":[191],"state-of-the-art":[194],"on":[195],"three":[196],"datasets.":[199],"1":[200]},"counts_by_year":[{"year":2024,"cited_by_count":1},{"year":2022,"cited_by_count":4},{"year":2021,"cited_by_count":5},{"year":2020,"cited_by_count":8},{"year":2019,"cited_by_count":5},{"year":2018,"cited_by_count":7},{"year":2017,"cited_by_count":7},{"year":2016,"cited_by_count":14},{"year":2015,"cited_by_count":11},{"year":2014,"cited_by_count":8},{"year":2013,"cited_by_count":18},{"year":2012,"cited_by_count":6}],"updated_date":"2026-11-08T12:12:28.789561","created_date":"2016-06-24T00:00:00"}