{"id":"https://openalex.org/W2118778308","doi":"https://doi.org/10.1109/cvprw.2008.4563047","title":"Discovery of social relationships in consumer photo collections using Markov Logic","display_name":"Discovery of social relationships in consumer photo collections using Markov Logic","publication_year":2008,"publication_date":"2008-06-01","ids":{"openalex":"https://openalex.org/W2118778308","doi":"https://doi.org/10.1109/cvprw.2008.4563047","mag":"2118778308"},"language":"en","primary_location":{"id":"doi:10.1109/cvprw.2008.4563047","is_oa":false,"landing_page_url":"https://doi.org/10.1109/cvprw.2008.4563047","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2008 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops","raw_type":"proceedings-article"},"type":"conference-paper","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/A5103677687","display_name":"Parag Singla","orcid":"https://orcid.org/0009-0000-9190-9794"},"institutions":[{"id":"https://openalex.org/I201448701","display_name":"University of Washington","ror":"https://ror.org/00cvxb145","country_code":"US","type":"education","lineage":["https://openalex.org/I201448701"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Parag Singla","raw_affiliation_strings":["University of Washington, USA","Univ. of Washington, Washington DC#TAB#"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of Washington, USA","institution_ids":["https://openalex.org/I201448701"]},{"raw_affiliation_string":"Univ. of Washington, Washington DC#TAB#","institution_ids":["https://openalex.org/I201448701"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5105508446","display_name":"Henry Kautz","orcid":"https://orcid.org/0000-0001-5219-2970"},"institutions":[{"id":"https://openalex.org/I5388228","display_name":"University of Rochester","ror":"https://ror.org/022kthw22","country_code":"US","type":"education","lineage":["https://openalex.org/I5388228"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Henry Kautz","raw_affiliation_strings":["University of Rochester, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of Rochester, USA","institution_ids":["https://openalex.org/I5388228"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5055469774","display_name":"Jiebo Luo","orcid":"https://orcid.org/0000-0002-4516-9729"},"institutions":[{"id":"https://openalex.org/I4210094723","display_name":"Kodak (Japan)","ror":"https://ror.org/00tee0349","country_code":"JP","type":"company","lineage":["https://openalex.org/I4210094723","https://openalex.org/I4210159451"]},{"id":"https://openalex.org/I4210159451","display_name":"Kodak (United States)","ror":"https://ror.org/04rn3ph18","country_code":"US","type":"company","lineage":["https://openalex.org/I4210159451"]}],"countries":["JP","US"],"is_corresponding":false,"raw_author_name":"Jiebo Luo","raw_affiliation_strings":["Eastman Kodak (Japan) Limited, USA","Eastman Kodak Company/USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Eastman Kodak (Japan) Limited, USA","institution_ids":["https://openalex.org/I4210094723"]},{"raw_affiliation_string":"Eastman Kodak Company/USA","institution_ids":["https://openalex.org/I4210159451"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5031269302","display_name":"Andrew Gallagher","orcid":"https://orcid.org/0000-0001-8090-2239"},"institutions":[{"id":"https://openalex.org/I4210094723","display_name":"Kodak (Japan)","ror":"https://ror.org/00tee0349","country_code":"JP","type":"company","lineage":["https://openalex.org/I4210094723","https://openalex.org/I4210159451"]},{"id":"https://openalex.org/I4210159451","display_name":"Kodak (United States)","ror":"https://ror.org/04rn3ph18","country_code":"US","type":"company","lineage":["https://openalex.org/I4210159451"]}],"countries":["JP","US"],"is_corresponding":false,"raw_author_name":"Andrew Gallagher","raw_affiliation_strings":["Eastman Kodak (Japan) Limited, USA","Eastman Kodak Company/USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Eastman Kodak (Japan) Limited, USA","institution_ids":["https://openalex.org/I4210094723"]},{"raw_affiliation_string":"Eastman Kodak Company/USA","institution_ids":["https://openalex.org/I4210159451"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":4,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":45,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"7"},"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":0.9923999905586243,"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":0.9923999905586243,"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":0.9828000068664551,"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/T10757","display_name":"Geographic Information Systems Studies","score":0.9786999821662903,"subfield":{"id":"https://openalex.org/subfields/3305","display_name":"Geography, Planning and Development"},"field":{"id":"https://openalex.org/fields/33","display_name":"Social Sciences"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/markov-chain","display_name":"Markov chain","score":0.7078247666358948},{"id":"https://openalex.org/keywords/inference","display_name":"Inference","score":0.7000706195831299},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.690253496170044},{"id":"https://openalex.org/keywords/set","display_name":"Set (abstract data type)","score":0.6208631992340088},{"id":"https://openalex.org/keywords/statistical-inference","display_name":"Statistical inference","score":0.5485353469848633},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5078149437904358},{"id":"https://openalex.org/keywords/hidden-markov-model","display_name":"Hidden Markov model","score":0.4727017879486084},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.4570167362689972},{"id":"https://openalex.org/keywords/markov-model","display_name":"Markov model","score":0.44211307168006897},{"id":"https://openalex.org/keywords/domain","display_name":"Domain (mathematical analysis)","score":0.4400934875011444},{"id":"https://openalex.org/keywords/data-science","display_name":"Data science","score":0.4395514130592346},{"id":"https://openalex.org/keywords/markov-process","display_name":"Markov process","score":0.42409518361091614},{"id":"https://openalex.org/keywords/natural-language-processing","display_name":"Natural language processing","score":0.3528785705566406},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.16007104516029358},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.14047184586524963}],"concepts":[{"id":"https://openalex.org/C98763669","wikidata":"https://www.wikidata.org/wiki/Q176645","display_name":"Markov chain","level":2,"score":0.7078247666358948},{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.7000706195831299},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.690253496170044},{"id":"https://openalex.org/C177264268","wikidata":"https://www.wikidata.org/wiki/Q1514741","display_name":"Set (abstract data type)","level":2,"score":0.6208631992340088},{"id":"https://openalex.org/C134261354","wikidata":"https://www.wikidata.org/wiki/Q938438","display_name":"Statistical inference","level":2,"score":0.5485353469848633},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5078149437904358},{"id":"https://openalex.org/C23224414","wikidata":"https://www.wikidata.org/wiki/Q176769","display_name":"Hidden Markov model","level":2,"score":0.4727017879486084},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.4570167362689972},{"id":"https://openalex.org/C163836022","wikidata":"https://www.wikidata.org/wiki/Q6771326","display_name":"Markov model","level":3,"score":0.44211307168006897},{"id":"https://openalex.org/C36503486","wikidata":"https://www.wikidata.org/wiki/Q11235244","display_name":"Domain (mathematical analysis)","level":2,"score":0.4400934875011444},{"id":"https://openalex.org/C2522767166","wikidata":"https://www.wikidata.org/wiki/Q2374463","display_name":"Data science","level":1,"score":0.4395514130592346},{"id":"https://openalex.org/C159886148","wikidata":"https://www.wikidata.org/wiki/Q176645","display_name":"Markov process","level":2,"score":0.42409518361091614},{"id":"https://openalex.org/C204321447","wikidata":"https://www.wikidata.org/wiki/Q30642","display_name":"Natural language processing","level":1,"score":0.3528785705566406},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.16007104516029358},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.14047184586524963},{"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/C134306372","wikidata":"https://www.wikidata.org/wiki/Q7754","display_name":"Mathematical analysis","level":1,"score":0.0}],"mesh":[],"locations_count":3,"locations":[{"id":"doi:10.1109/cvprw.2008.4563047","is_oa":false,"landing_page_url":"https://doi.org/10.1109/cvprw.2008.4563047","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2008 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops","raw_type":"proceedings-article"},{"id":"pmh:oai:CiteSeerX.psu:10.1.1.143.8424","is_oa":false,"landing_page_url":"http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.143.8424","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"http://www.cs.washington.edu/homes/parag/papers/dscpc-cvprslam08.pdf.pdf","raw_type":"text"},{"id":"pmh:oai:CiteSeerX.psu:10.1.1.420.1838","is_oa":false,"landing_page_url":"http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.420.1838","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"http://mplab.ucsd.edu/wp-content/uploads/CVPR2008/WorkShops/data/papers/098.pdf","raw_type":"text"}],"best_oa_location":null,"sustainable_development_goals":[{"display_name":"Reduced inequalities","id":"https://metadata.un.org/sdg/10","score":0.4399999976158142}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":20,"referenced_works":["https://openalex.org/W28766783","https://openalex.org/W47392883","https://openalex.org/W203049729","https://openalex.org/W1535439311","https://openalex.org/W1585529040","https://openalex.org/W1966382345","https://openalex.org/W1977970897","https://openalex.org/W2005915018","https://openalex.org/W2039140324","https://openalex.org/W2095650959","https://openalex.org/W2097394871","https://openalex.org/W2114740362","https://openalex.org/W2117736950","https://openalex.org/W2121075864","https://openalex.org/W2147625498","https://openalex.org/W2912206496","https://openalex.org/W2962735828","https://openalex.org/W6631974464","https://openalex.org/W6661236838","https://openalex.org/W6674532778"],"related_works":["https://openalex.org/W1510894296","https://openalex.org/W2134386692","https://openalex.org/W2379651310","https://openalex.org/W2082284720","https://openalex.org/W2113019827","https://openalex.org/W1541249122","https://openalex.org/W2084326697","https://openalex.org/W2194396582","https://openalex.org/W2027903142","https://openalex.org/W2116722627"],"abstract_inverted_index":{"We":[0,121,172],"identify":[1],"the":[2,43,57,86,101,105,115,143,150],"social":[3],"relationships":[4,32],"between":[5],"individuals":[6],"in":[7,85,104],"consumer":[8,38],"photos.":[9],"Consumer":[10],"photos":[11,134],"generally":[12],"do":[13],"not":[14],"contain":[15],"a":[16,46,52,96,129,159],"random":[17],"gathering":[18],"of":[19,24,79,107,131,145,161],"strangers":[20],"but":[21],"rather":[22],"groups":[23],"friends":[25],"and":[26,29,91],"families.":[27],"Detecting":[28],"identifying":[30],"these":[31],"are":[33,93],"important":[34],"steps":[35],"towards":[36],"understanding":[37],"image":[39],"collections.":[40],"Similar":[41],"to":[42,55,63,72],"approach":[44,147],"that":[45],"human":[47],"might":[48],"use,":[49],"we":[50,113],"use":[51,114],"rule-based":[53],"system":[54],"quantify":[56],"domain":[58],"knowledge":[59],"(e.g.":[60],"children":[61],"tend":[62,71],"be":[64],"photographed":[65],"more":[66],"often":[67],"than":[68],"adults;":[69],"parents":[70],"appear":[73],"with":[74,169,174],"their":[75],"kids).":[76],"The":[77],"weight":[78],"each":[80],"rule":[81],"reflects":[82],"its":[83],"importance":[84],"overall":[87],"prediction":[88,165],"model.":[89],"Learning":[90],"inference":[92],"based":[94],"on":[95,128],"sound":[97],"mathematical":[98],"formulation":[99],"using":[100,125],"theory":[102],"developed":[103],"area":[106],"statistical":[108,156],"relational":[109],"models.":[110],"In":[111],"particular,":[112],"language":[116],"called":[117],"Markov":[118],"Logic":[119],"[14].":[120],"evaluate":[122],"our":[123,146],"model":[124],"cross":[126],"validation":[127],"set":[130,160],"about":[132],"4500":[133],"collected":[135],"from":[136],"13":[137],"different":[138,163,170],"users.":[139],"Our":[140],"experiments":[141],"show":[142],"potential":[144],"by":[148],"improving":[149],"accuracy":[151],"(as":[152],"well":[153],"as":[154],"other":[155],"measures)":[157],"over":[158],"two":[162],"relationship":[164],"tasks":[166],"when":[167],"compared":[168],"baselines.":[171],"conclude":[173],"directions":[175],"for":[176],"future":[177],"work.":[178]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2025,"cited_by_count":1},{"year":2022,"cited_by_count":1},{"year":2021,"cited_by_count":2},{"year":2019,"cited_by_count":2},{"year":2017,"cited_by_count":3},{"year":2016,"cited_by_count":6},{"year":2015,"cited_by_count":6},{"year":2014,"cited_by_count":2},{"year":2013,"cited_by_count":9},{"year":2012,"cited_by_count":5}],"updated_date":"2026-07-14T23:27:15.235271","created_date":"2025-10-10T00:00:00"}