{"id":"https://openalex.org/W2735393225","doi":"https://doi.org/10.1109/jstsp.2017.2726977","title":"Query Adaptive Fusion for Graph-Based Visual Reranking","display_name":"Query Adaptive Fusion for Graph-Based Visual Reranking","publication_year":2017,"publication_date":"2017-07-13","ids":{"openalex":"https://openalex.org/W2735393225","doi":"https://doi.org/10.1109/jstsp.2017.2726977","mag":"2735393225"},"language":"en","primary_location":{"id":"doi:10.1109/jstsp.2017.2726977","is_oa":false,"landing_page_url":"https://doi.org/10.1109/jstsp.2017.2726977","pdf_url":null,"source":{"id":"https://openalex.org/S42167783","display_name":"IEEE Journal of Selected Topics in Signal Processing","issn_l":"1932-4553","issn":["1932-4553","1941-0484"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Journal of Selected Topics in Signal Processing","raw_type":"journal-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/A5091822876","display_name":"Muyuan Fang","orcid":null},"institutions":[{"id":"https://openalex.org/I99065089","display_name":"Tsinghua University","ror":"https://ror.org/03cve4549","country_code":"CN","type":"education","lineage":["https://openalex.org/I99065089"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Muyuan Fang","raw_affiliation_strings":["Department of Electronic Engineering, Tsinghua University, Beijing, China","Department of Electronic, Engineering, Tsinghua University, Beijing, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Electronic Engineering, Tsinghua University, Beijing, China","institution_ids":["https://openalex.org/I99065089"]},{"raw_affiliation_string":"Department of Electronic, Engineering, Tsinghua University, Beijing, China","institution_ids":["https://openalex.org/I99065089"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5100684572","display_name":"Yujin Zhang","orcid":"https://orcid.org/0000-0003-1225-4334"},"institutions":[{"id":"https://openalex.org/I99065089","display_name":"Tsinghua University","ror":"https://ror.org/03cve4549","country_code":"CN","type":"education","lineage":["https://openalex.org/I99065089"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yu-Jin Zhang","raw_affiliation_strings":["Department of Electronic Engineering, Tsinghua University, Beijing, China","Department of Electronic, Engineering, Tsinghua University, Beijing, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Electronic Engineering, Tsinghua University, Beijing, China","institution_ids":["https://openalex.org/I99065089"]},{"raw_affiliation_string":"Department of Electronic, Engineering, Tsinghua University, Beijing, China","institution_ids":["https://openalex.org/I99065089"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.1847,"has_fulltext":false,"cited_by_count":3,"citation_normalized_percentile":{"value":0.56572654,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":94},"biblio":{"volume":"11","issue":"6","first_page":"908","last_page":"917"},"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":1.0,"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":1.0,"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":0.9976999759674072,"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/T11605","display_name":"Visual Attention and Saliency Detection","score":0.9976000189781189,"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/computer-science","display_name":"Computer science","score":0.7106869220733643},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.633499026298523},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.6262721419334412},{"id":"https://openalex.org/keywords/scale-invariant-feature-transform","display_name":"Scale-invariant feature transform","score":0.5199722051620483},{"id":"https://openalex.org/keywords/graph","display_name":"Graph","score":0.5118443369865417},{"id":"https://openalex.org/keywords/fusion","display_name":"Fusion","score":0.5079782605171204},{"id":"https://openalex.org/keywords/hue","display_name":"Hue","score":0.4911019802093506},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.45654913783073425},{"id":"https://openalex.org/keywords/feature-extraction","display_name":"Feature extraction","score":0.36269891262054443}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7106869220733643},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.633499026298523},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.6262721419334412},{"id":"https://openalex.org/C61265191","wikidata":"https://www.wikidata.org/wiki/Q767770","display_name":"Scale-invariant feature transform","level":3,"score":0.5199722051620483},{"id":"https://openalex.org/C132525143","wikidata":"https://www.wikidata.org/wiki/Q141488","display_name":"Graph","level":2,"score":0.5118443369865417},{"id":"https://openalex.org/C158525013","wikidata":"https://www.wikidata.org/wiki/Q2593739","display_name":"Fusion","level":2,"score":0.5079782605171204},{"id":"https://openalex.org/C126537357","wikidata":"https://www.wikidata.org/wiki/Q372948","display_name":"Hue","level":2,"score":0.4911019802093506},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.45654913783073425},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.36269891262054443},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","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},{"id":"https://openalex.org/C41895202","wikidata":"https://www.wikidata.org/wiki/Q8162","display_name":"Linguistics","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/jstsp.2017.2726977","is_oa":false,"landing_page_url":"https://doi.org/10.1109/jstsp.2017.2726977","pdf_url":null,"source":{"id":"https://openalex.org/S42167783","display_name":"IEEE Journal of Selected Topics in Signal Processing","issn_l":"1932-4553","issn":["1932-4553","1941-0484"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Journal of Selected Topics in Signal Processing","raw_type":"journal-article"}],"best_oa_location":null,"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/16","display_name":"Peace, Justice and strong institutions","score":0.5600000023841858},{"id":"https://metadata.un.org/sdg/10","display_name":"Reduced inequalities","score":0.44999998807907104}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":40,"referenced_works":["https://openalex.org/W1479136381","https://openalex.org/W1556531089","https://openalex.org/W1686810756","https://openalex.org/W1820015093","https://openalex.org/W1935207225","https://openalex.org/W1979931042","https://openalex.org/W1996169591","https://openalex.org/W1997813293","https://openalex.org/W2011027234","https://openalex.org/W2018057603","https://openalex.org/W2031332477","https://openalex.org/W2044284589","https://openalex.org/W2050697589","https://openalex.org/W2062118960","https://openalex.org/W2065296697","https://openalex.org/W2066477856","https://openalex.org/W2076188996","https://openalex.org/W2109032227","https://openalex.org/W2118509786","https://openalex.org/W2119939615","https://openalex.org/W2122557169","https://openalex.org/W2123229215","https://openalex.org/W2128017662","https://openalex.org/W2128489591","https://openalex.org/W2135364649","https://openalex.org/W2141362318","https://openalex.org/W2148809531","https://openalex.org/W2151103935","https://openalex.org/W2155893237","https://openalex.org/W2171139251","https://openalex.org/W2247935935","https://openalex.org/W2294587618","https://openalex.org/W2412709527","https://openalex.org/W2582830023","https://openalex.org/W2603858568","https://openalex.org/W2618530766","https://openalex.org/W6633472159","https://openalex.org/W6637373629","https://openalex.org/W6666665630","https://openalex.org/W6666888491"],"related_works":["https://openalex.org/W3034955165","https://openalex.org/W4320518079","https://openalex.org/W2094920358","https://openalex.org/W2041448692","https://openalex.org/W2247121321","https://openalex.org/W2391926582","https://openalex.org/W2087391438","https://openalex.org/W1966831329","https://openalex.org/W2316074893","https://openalex.org/W2049930962"],"abstract_inverted_index":{"Developing":[0],"effective":[1],"fusion":[2,101],"schemes":[3],"for":[4,26,50,75],"multiple":[5],"feature":[6,88,134],"types":[7],"has":[8],"always":[9],"been":[10],"a":[11,23,90],"hot":[12],"issue":[13],"in":[14,35,39,62],"content-based":[15],"image":[16,51],"retrieval.":[17],"In":[18],"this":[19],"paper,":[20],"we":[21,80],"propose":[22,81],"novel":[24],"method":[25,46,124],"graph-based":[27],"visual":[28],"reranking,":[29],"which":[30,155],"addresses":[31],"two":[32],"major":[33],"limitations":[34],"existing":[36],"methods.":[37,162],"First,":[38],"the":[40,55,63,73,84,96,115,149,152,160],"phase":[41,64],"of":[42,65,86,151],"graph":[43,66],"construction,":[44],"our":[45],"introduces":[47],"fine-grained":[48],"measurements":[49],"relations,":[52],"by":[53,121,131],"assigning":[54],"edge":[56],"weights":[57],"using":[58],"normalized":[59],"similarity.":[60],"Furthermore,":[61],"fusion,":[67],"rather":[68],"than":[69,159],"summing":[70],"up":[71],"all":[72],"graphs":[74,98],"different":[76],"single":[77,97],"features":[78],"indiscriminately,":[79],"to":[82],"estimate":[83],"reliability":[85],"each":[87],"through":[89],"statistical":[91],"model,":[92],"and":[93,109,114,119,138],"selectively":[94],"fuse":[95],"via":[99],"query-adaptive":[100],"weights.":[102],"Fusion":[103],"methods":[104],"with":[105],"either":[106],"labeled":[107],"data":[108,111],"unlabeled":[110],"are":[112,117],"proposed":[113,153],"performance":[116],"evaluated":[118,126],"compared":[120],"experiments.":[122],"Our":[123],"is":[125],"on":[127],"five":[128],"public":[129],"datasets,":[130],"fusing":[132],"scale-invariant":[133],"transform":[135],"(SIFT),":[136],"CNN,":[137],"hue,":[139],"saturation,":[140],"hue":[141],"(HSV),":[142],"three":[143],"complementary":[144],"features.":[145],"Experimental":[146],"results":[147,158],"demonstrate":[148],"effectiveness":[150],"method,":[154],"yields":[156],"superior":[157],"competing":[161]},"counts_by_year":[{"year":2023,"cited_by_count":1},{"year":2020,"cited_by_count":1},{"year":2018,"cited_by_count":1}],"updated_date":"2026-06-11T09:08:48.828518","created_date":"2025-10-10T00:00:00"}
