{"id":"https://openalex.org/W2168305139","doi":"https://doi.org/10.1109/cvpr.2012.6248033","title":"Image search results refinement via outlier detection using deep contexts","display_name":"Image search results refinement via outlier detection using deep contexts","publication_year":2012,"publication_date":"2012-06-01","ids":{"openalex":"https://openalex.org/W2168305139","doi":"https://doi.org/10.1109/cvpr.2012.6248033","mag":"2168305139"},"language":"en","primary_location":{"id":"doi:10.1109/cvpr.2012.6248033","is_oa":false,"landing_page_url":"https://doi.org/10.1109/cvpr.2012.6248033","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/A5102164667","display_name":"Junyang Lu","orcid":null},"institutions":[{"id":"https://openalex.org/I20231570","display_name":"Peking University","ror":"https://ror.org/02v51f717","country_code":"CN","type":"education","lineage":["https://openalex.org/I20231570"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Junyang Lu","raw_affiliation_strings":["Peking University, China"],"affiliations":[{"raw_affiliation_string":"Peking University, China","institution_ids":["https://openalex.org/I20231570"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101631491","display_name":"Jiazhen Zhou","orcid":"https://orcid.org/0000-0001-9061-6586"},"institutions":[{"id":"https://openalex.org/I20231570","display_name":"Peking University","ror":"https://ror.org/02v51f717","country_code":"CN","type":"education","lineage":["https://openalex.org/I20231570"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Jiazhen Zhou","raw_affiliation_strings":["Peking University, China"],"affiliations":[{"raw_affiliation_string":"Peking University, China","institution_ids":["https://openalex.org/I20231570"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5075880303","display_name":"Jingdong Wang","orcid":"https://orcid.org/0000-0002-4888-4445"},"institutions":[{"id":"https://openalex.org/I4210113369","display_name":"Microsoft Research Asia (China)","ror":"https://ror.org/0300m5276","country_code":"CN","type":"company","lineage":["https://openalex.org/I1290206253","https://openalex.org/I4210113369"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Jingdong Wang","raw_affiliation_strings":["Microsoft Research Asia, China"],"affiliations":[{"raw_affiliation_string":"Microsoft Research Asia, China","institution_ids":["https://openalex.org/I4210113369"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5017597537","display_name":"Tao Mei","orcid":"https://orcid.org/0000-0003-2497-7732"},"institutions":[{"id":"https://openalex.org/I4210113369","display_name":"Microsoft Research Asia (China)","ror":"https://ror.org/0300m5276","country_code":"CN","type":"company","lineage":["https://openalex.org/I1290206253","https://openalex.org/I4210113369"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Tao Mei","raw_affiliation_strings":["Microsoft Research Asia, China"],"affiliations":[{"raw_affiliation_string":"Microsoft Research Asia, China","institution_ids":["https://openalex.org/I4210113369"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5024965898","display_name":"Xian\u2010Sheng Hua","orcid":"https://orcid.org/0000-0002-8232-5049"},"institutions":[{"id":"https://openalex.org/I1290206253","display_name":"Microsoft (United States)","ror":"https://ror.org/00d0nc645","country_code":"US","type":"company","lineage":["https://openalex.org/I1290206253"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Xian-Sheng Hua","raw_affiliation_strings":["Microsoft Bing, USA"],"affiliations":[{"raw_affiliation_string":"Microsoft Bing, USA","institution_ids":["https://openalex.org/I1290206253"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5100321089","display_name":"Shipeng Li","orcid":"https://orcid.org/0000-0001-5368-4256"},"institutions":[{"id":"https://openalex.org/I4210113369","display_name":"Microsoft Research Asia (China)","ror":"https://ror.org/0300m5276","country_code":"CN","type":"company","lineage":["https://openalex.org/I1290206253","https://openalex.org/I4210113369"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Shipeng Li","raw_affiliation_strings":["Microsoft Research Asia"],"affiliations":[{"raw_affiliation_string":"Microsoft Research Asia","institution_ids":["https://openalex.org/I4210113369"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":6,"corresponding_author_ids":["https://openalex.org/A5102164667"],"corresponding_institution_ids":["https://openalex.org/I20231570"],"apc_list":null,"apc_paid":null,"fwci":3.0532,"has_fulltext":false,"cited_by_count":18,"citation_normalized_percentile":{"value":0.927428,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":90,"max":99},"biblio":{"volume":"30","issue":null,"first_page":"3029","last_page":"3036"},"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":0.9998000264167786,"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":0.9998000264167786,"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/T11714","display_name":"Multimodal Machine Learning Applications","score":0.9988999962806702,"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":0.9966999888420105,"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/computer-science","display_name":"Computer science","score":0.8038636445999146},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5998521447181702},{"id":"https://openalex.org/keywords/ranking","display_name":"Ranking (information retrieval)","score":0.5965874195098877},{"id":"https://openalex.org/keywords/markov-random-field","display_name":"Markov random field","score":0.5613422989845276},{"id":"https://openalex.org/keywords/context","display_name":"Context (archaeology)","score":0.5534508228302002},{"id":"https://openalex.org/keywords/outlier","display_name":"Outlier","score":0.5530487298965454},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.525498628616333},{"id":"https://openalex.org/keywords/graph","display_name":"Graph","score":0.499004602432251},{"id":"https://openalex.org/keywords/exploit","display_name":"Exploit","score":0.4861152172088623},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.44448307156562805},{"id":"https://openalex.org/keywords/property","display_name":"Property (philosophy)","score":0.43564146757125854},{"id":"https://openalex.org/keywords/filter","display_name":"Filter (signal processing)","score":0.4264400899410248},{"id":"https://openalex.org/keywords/anomaly-detection","display_name":"Anomaly detection","score":0.4129929542541504},{"id":"https://openalex.org/keywords/information-retrieval","display_name":"Information retrieval","score":0.3998816907405853},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.3502349257469177},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.29155081510543823},{"id":"https://openalex.org/keywords/image-segmentation","display_name":"Image segmentation","score":0.20346060395240784},{"id":"https://openalex.org/keywords/theoretical-computer-science","display_name":"Theoretical computer science","score":0.17609387636184692}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8038636445999146},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5998521447181702},{"id":"https://openalex.org/C189430467","wikidata":"https://www.wikidata.org/wiki/Q7293293","display_name":"Ranking (information retrieval)","level":2,"score":0.5965874195098877},{"id":"https://openalex.org/C2778045648","wikidata":"https://www.wikidata.org/wiki/Q176827","display_name":"Markov random field","level":4,"score":0.5613422989845276},{"id":"https://openalex.org/C2779343474","wikidata":"https://www.wikidata.org/wiki/Q3109175","display_name":"Context (archaeology)","level":2,"score":0.5534508228302002},{"id":"https://openalex.org/C79337645","wikidata":"https://www.wikidata.org/wiki/Q779824","display_name":"Outlier","level":2,"score":0.5530487298965454},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.525498628616333},{"id":"https://openalex.org/C132525143","wikidata":"https://www.wikidata.org/wiki/Q141488","display_name":"Graph","level":2,"score":0.499004602432251},{"id":"https://openalex.org/C165696696","wikidata":"https://www.wikidata.org/wiki/Q11287","display_name":"Exploit","level":2,"score":0.4861152172088623},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.44448307156562805},{"id":"https://openalex.org/C189950617","wikidata":"https://www.wikidata.org/wiki/Q937228","display_name":"Property (philosophy)","level":2,"score":0.43564146757125854},{"id":"https://openalex.org/C106131492","wikidata":"https://www.wikidata.org/wiki/Q3072260","display_name":"Filter (signal processing)","level":2,"score":0.4264400899410248},{"id":"https://openalex.org/C739882","wikidata":"https://www.wikidata.org/wiki/Q3560506","display_name":"Anomaly detection","level":2,"score":0.4129929542541504},{"id":"https://openalex.org/C23123220","wikidata":"https://www.wikidata.org/wiki/Q816826","display_name":"Information retrieval","level":1,"score":0.3998816907405853},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.3502349257469177},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.29155081510543823},{"id":"https://openalex.org/C124504099","wikidata":"https://www.wikidata.org/wiki/Q56933","display_name":"Image segmentation","level":3,"score":0.20346060395240784},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.17609387636184692},{"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/C38652104","wikidata":"https://www.wikidata.org/wiki/Q3510521","display_name":"Computer security","level":1,"score":0.0},{"id":"https://openalex.org/C111472728","wikidata":"https://www.wikidata.org/wiki/Q9471","display_name":"Epistemology","level":1,"score":0.0},{"id":"https://openalex.org/C138885662","wikidata":"https://www.wikidata.org/wiki/Q5891","display_name":"Philosophy","level":0,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/cvpr.2012.6248033","is_oa":false,"landing_page_url":"https://doi.org/10.1109/cvpr.2012.6248033","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"}],"best_oa_location":null,"sustainable_development_goals":[{"display_name":"Quality Education","id":"https://metadata.un.org/sdg/4","score":0.6299999952316284}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":28,"referenced_works":["https://openalex.org/W1565630510","https://openalex.org/W1578226009","https://openalex.org/W1969926026","https://openalex.org/W2003310214","https://openalex.org/W2035821475","https://openalex.org/W2048318462","https://openalex.org/W2066941820","https://openalex.org/W2081580037","https://openalex.org/W2096765155","https://openalex.org/W2101309634","https://openalex.org/W2107658650","https://openalex.org/W2110009404","https://openalex.org/W2111861094","https://openalex.org/W2117876453","https://openalex.org/W2118509786","https://openalex.org/W2119482054","https://openalex.org/W2120477910","https://openalex.org/W2124958362","https://openalex.org/W2148675068","https://openalex.org/W2161149720","https://openalex.org/W2162867699","https://openalex.org/W2162915993","https://openalex.org/W2172191903","https://openalex.org/W4244761728","https://openalex.org/W6634576794","https://openalex.org/W6677441152","https://openalex.org/W6677675441","https://openalex.org/W6826294982"],"related_works":["https://openalex.org/W17155033","https://openalex.org/W3207760230","https://openalex.org/W1496222301","https://openalex.org/W4312814274","https://openalex.org/W2499612753","https://openalex.org/W3111802945","https://openalex.org/W2946096271","https://openalex.org/W2295423552","https://openalex.org/W1598471830","https://openalex.org/W3107369729"],"abstract_inverted_index":{"Visual":[0],"reranking":[1,234],"has":[2],"become":[3],"a":[4,58,174],"widely-accepted":[5],"method":[6,251],"to":[7,18,32,78,146,157,172,221,238],"improve":[8],"traditional":[9],"text-based":[10],"image":[11,108,136],"search":[12,101,140],"results.":[13,102],"The":[14,103,185],"main":[15],"principle":[16],"is":[17,90,95,164,179,187,207,219],"exploit":[19],"the":[20,44,81,91,99,120,124,132,138,143,148,152,158,168,195,210,216,244],"visual":[21,59],"aggregation":[22,60],"property":[23,61],"of":[24,36,110,112,127,134],"relevant":[25,37,46,198],"images":[26,85,113,159,199],"among":[27,52,203],"top":[28,204],"results":[29,141,205,237,242],"so":[30],"as":[31],"boost":[33],"ranking":[34,50],"scores":[35,51,154],"images,":[38,47],"by":[39,117,123,182],"explicitly":[40],"or":[41],"implicitly":[42],"detecting":[43],"confident":[45],"and":[48,66,142,206],"propagating":[49],"visually":[53,201],"similar":[54],"images.":[55],"However,":[56],"such":[57],"does":[62,191],"not":[63,96,192],"always":[64],"hold,":[65],"thus":[67],"these":[68],"schemes":[69,170],"may":[70],"fail.":[71],"In":[72],"this":[73,128,135],"paper,":[74],"we":[75,231],"instead":[76],"propose":[77],"filter":[79],"out":[80],"most":[82],"probable":[83],"irrelevant":[84],"using":[86,119],"deep":[87,104,144],"contexts,":[88],"which":[89,178],"extra":[92],"information":[93],"that":[94,114,188,197,212,248],"limited":[97],"in":[98,137],"current":[100,139,217],"contexts":[105,145],"for":[106],"each":[107],"consist":[109],"sets":[111],"are":[115,155,200],"returned":[116],"searches":[118],"queries":[121],"formed":[122],"textual":[125,162],"context":[126,163],"image.":[129],"We":[130,166],"compare":[131],"popularity":[133],"check":[147],"irrelevance":[149,153],"score.":[150],"Then":[151],"propagated":[156],"whose":[160],"useful":[161],"missed.":[165],"formulate":[167],"two":[169],"together":[171],"reach":[173],"Markov":[175],"random":[176],"field,":[177],"effectively":[180],"solved":[181],"graph":[183,233],"cuts.":[184],"key":[186],"our":[189,249],"scheme":[190],"rely":[193],"on":[194,209,243],"assumption":[196],"aggregated":[202],"based":[208],"observation":[211],"an":[213],"outlier":[214],"under":[215,225],"query":[218],"likely":[220],"be":[222],"more":[223],"popular":[224],"some":[226],"other":[227],"query.":[228],"After":[229],"that,":[230],"perform":[232],"over":[235,255],"filtered":[236],"reorder":[239],"them.":[240],"Experimental":[241],"INRIA":[245],"dataset":[246],"show":[247],"proposed":[250],"achieves":[252],"significant":[253],"improvements":[254],"previous":[256],"approaches.":[257]},"counts_by_year":[{"year":2020,"cited_by_count":2},{"year":2019,"cited_by_count":1},{"year":2017,"cited_by_count":1},{"year":2016,"cited_by_count":3},{"year":2014,"cited_by_count":5},{"year":2013,"cited_by_count":6}],"updated_date":"2026-04-05T17:49:38.594831","created_date":"2025-10-10T00:00:00"}