{"id":"https://openalex.org/W2895716796","doi":"https://doi.org/10.1109/tip.2018.2872831","title":"A Probabilistic Approach to Cross-Region Matching-Based Image Retrieval","display_name":"A Probabilistic Approach to Cross-Region Matching-Based Image Retrieval","publication_year":2018,"publication_date":"2018-10-01","ids":{"openalex":"https://openalex.org/W2895716796","doi":"https://doi.org/10.1109/tip.2018.2872831","mag":"2895716796","pmid":"https://pubmed.ncbi.nlm.nih.gov/30281450"},"language":"en","primary_location":{"id":"doi:10.1109/tip.2018.2872831","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tip.2018.2872831","pdf_url":null,"source":{"id":"https://openalex.org/S4210173141","display_name":"IEEE Transactions on Image Processing","issn_l":"1057-7149","issn":["1057-7149","1941-0042"],"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 Transactions on Image Processing","raw_type":"journal-article"},"type":"article","indexed_in":["crossref","pubmed"],"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/A5066142993","display_name":"Zhimin Gao","orcid":"https://orcid.org/0000-0002-2450-492X"},"institutions":[{"id":"https://openalex.org/I38877650","display_name":"Zhengzhou University","ror":"https://ror.org/04ypx8c21","country_code":"CN","type":"education","lineage":["https://openalex.org/I38877650"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Zhimin Gao","raw_affiliation_strings":["School of Information Engineering, Zhengzhou University, Zhengzhou, China"],"affiliations":[{"raw_affiliation_string":"School of Information Engineering, Zhengzhou University, Zhengzhou, China","institution_ids":["https://openalex.org/I38877650"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100435848","display_name":"Lei Wang","orcid":"https://orcid.org/0000-0002-0961-0441"},"institutions":[{"id":"https://openalex.org/I204824540","display_name":"University of Wollongong","ror":"https://ror.org/00jtmb277","country_code":"AU","type":"education","lineage":["https://openalex.org/I204824540"]}],"countries":["AU"],"is_corresponding":false,"raw_author_name":"Lei Wang","raw_affiliation_strings":["School of Computing and Information Technology, University of Wollongong, Wollongong, NSW, Australia"],"affiliations":[{"raw_affiliation_string":"School of Computing and Information Technology, University of Wollongong, Wollongong, NSW, Australia","institution_ids":["https://openalex.org/I204824540"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5100643784","display_name":"Luping Zhou","orcid":"https://orcid.org/0000-0001-8762-2424"},"institutions":[{"id":"https://openalex.org/I129604602","display_name":"The University of Sydney","ror":"https://ror.org/0384j8v12","country_code":"AU","type":"education","lineage":["https://openalex.org/I129604602"]}],"countries":["AU"],"is_corresponding":false,"raw_author_name":"Luping Zhou","raw_affiliation_strings":["School of Electrical and Information Engineering, The University of Sydney, Sydney, NSW, Australia"],"affiliations":[{"raw_affiliation_string":"School of Electrical and Information Engineering, The University of Sydney, Sydney, NSW, Australia","institution_ids":["https://openalex.org/I129604602"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5066142993"],"corresponding_institution_ids":["https://openalex.org/I38877650"],"apc_list":null,"apc_paid":null,"fwci":0.851,"has_fulltext":false,"cited_by_count":11,"citation_normalized_percentile":{"value":0.79477218,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":91,"max":97},"biblio":{"volume":"28","issue":"3","first_page":"1191","last_page":"1204"},"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":0.9994999766349792,"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":0.9909999966621399,"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/artificial-intelligence","display_name":"Artificial intelligence","score":0.7065010070800781},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6488438844680786},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.6189662218093872},{"id":"https://openalex.org/keywords/probabilistic-logic","display_name":"Probabilistic logic","score":0.6015498638153076},{"id":"https://openalex.org/keywords/image-retrieval","display_name":"Image retrieval","score":0.5479757785797119},{"id":"https://openalex.org/keywords/matching","display_name":"Matching (statistics)","score":0.5281344056129456},{"id":"https://openalex.org/keywords/parametric-statistics","display_name":"Parametric statistics","score":0.5223785042762756},{"id":"https://openalex.org/keywords/similarity","display_name":"Similarity (geometry)","score":0.508955717086792},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.47833678126335144},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.4488910138607025},{"id":"https://openalex.org/keywords/contextual-image-classification","display_name":"Contextual image classification","score":0.43896299600601196},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.2838839590549469},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.1115117073059082}],"concepts":[{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7065010070800781},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6488438844680786},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.6189662218093872},{"id":"https://openalex.org/C49937458","wikidata":"https://www.wikidata.org/wiki/Q2599292","display_name":"Probabilistic logic","level":2,"score":0.6015498638153076},{"id":"https://openalex.org/C1667742","wikidata":"https://www.wikidata.org/wiki/Q10927554","display_name":"Image retrieval","level":3,"score":0.5479757785797119},{"id":"https://openalex.org/C165064840","wikidata":"https://www.wikidata.org/wiki/Q1321061","display_name":"Matching (statistics)","level":2,"score":0.5281344056129456},{"id":"https://openalex.org/C117251300","wikidata":"https://www.wikidata.org/wiki/Q1849855","display_name":"Parametric statistics","level":2,"score":0.5223785042762756},{"id":"https://openalex.org/C103278499","wikidata":"https://www.wikidata.org/wiki/Q254465","display_name":"Similarity (geometry)","level":3,"score":0.508955717086792},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.47833678126335144},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.4488910138607025},{"id":"https://openalex.org/C75294576","wikidata":"https://www.wikidata.org/wiki/Q5165192","display_name":"Contextual image classification","level":3,"score":0.43896299600601196},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.2838839590549469},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.1115117073059082}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1109/tip.2018.2872831","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tip.2018.2872831","pdf_url":null,"source":{"id":"https://openalex.org/S4210173141","display_name":"IEEE Transactions on Image Processing","issn_l":"1057-7149","issn":["1057-7149","1941-0042"],"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 Transactions on Image Processing","raw_type":"journal-article"},{"id":"pmid:30281450","is_oa":false,"landing_page_url":"https://pubmed.ncbi.nlm.nih.gov/30281450","pdf_url":null,"source":{"id":"https://openalex.org/S4306525036","display_name":"PubMed","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I1299303238","host_organization_name":"National Institutes of Health","host_organization_lineage":["https://openalex.org/I1299303238"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE transactions on image processing : a publication of the IEEE Signal Processing Society","raw_type":null}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G3064199919","display_name":null,"funder_award_id":"ARC DE160100241","funder_id":"https://openalex.org/F4320334704","funder_display_name":"Australian Research Council"}],"funders":[{"id":"https://openalex.org/F4320312169","display_name":"National Computational Infrastructure","ror":"https://ror.org/04yx6dh41"},{"id":"https://openalex.org/F4320315885","display_name":"Australian Government","ror":"https://ror.org/0314h5y94"},{"id":"https://openalex.org/F4320334704","display_name":"Australian Research Council","ror":"https://ror.org/05mmh0f86"}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":69,"referenced_works":["https://openalex.org/W4952878","https://openalex.org/W204268067","https://openalex.org/W1491541693","https://openalex.org/W1524680991","https://openalex.org/W1532325895","https://openalex.org/W1544060217","https://openalex.org/W1556531089","https://openalex.org/W1679894842","https://openalex.org/W1686810756","https://openalex.org/W1820015093","https://openalex.org/W1903029394","https://openalex.org/W1922773808","https://openalex.org/W1963882359","https://openalex.org/W1976794880","https://openalex.org/W1984309565","https://openalex.org/W2003115311","https://openalex.org/W2023991840","https://openalex.org/W2040002737","https://openalex.org/W2045143396","https://openalex.org/W2062118960","https://openalex.org/W2071027807","https://openalex.org/W2076188996","https://openalex.org/W2086504823","https://openalex.org/W2097117768","https://openalex.org/W2099482512","https://openalex.org/W2100398441","https://openalex.org/W2102605133","https://openalex.org/W2109868644","https://openalex.org/W2111993661","https://openalex.org/W2118132750","https://openalex.org/W2128017662","https://openalex.org/W2130660124","https://openalex.org/W2131846894","https://openalex.org/W2132204530","https://openalex.org/W2141362318","https://openalex.org/W2145287260","https://openalex.org/W2147238549","https://openalex.org/W2148809531","https://openalex.org/W2149991777","https://openalex.org/W2151103935","https://openalex.org/W2163605009","https://openalex.org/W2164022341","https://openalex.org/W2166742463","https://openalex.org/W2174726731","https://openalex.org/W2200092826","https://openalex.org/W2204975001","https://openalex.org/W2294444065","https://openalex.org/W2295537791","https://openalex.org/W2336302573","https://openalex.org/W2340690086","https://openalex.org/W2499468060","https://openalex.org/W2593864460","https://openalex.org/W2620629206","https://openalex.org/W2799148064","https://openalex.org/W2951019013","https://openalex.org/W2952239967","https://openalex.org/W2962835968","https://openalex.org/W2963125676","https://openalex.org/W2963129433","https://openalex.org/W4213009331","https://openalex.org/W4299585995","https://openalex.org/W6608313692","https://openalex.org/W6631498818","https://openalex.org/W6637373629","https://openalex.org/W6683949359","https://openalex.org/W6684191040","https://openalex.org/W6685522000","https://openalex.org/W6697161373","https://openalex.org/W6704508018"],"related_works":["https://openalex.org/W2952813363","https://openalex.org/W4378678253","https://openalex.org/W2911497689","https://openalex.org/W4360783045","https://openalex.org/W3176438653","https://openalex.org/W2770149305","https://openalex.org/W2972076240","https://openalex.org/W3167930666","https://openalex.org/W3014952856","https://openalex.org/W4312465310"],"abstract_inverted_index":{"With":[0],"deep":[1,111],"convolutional":[2,112],"features,":[3,113],"cross-region":[4],"matching":[5],"(CRM)":[6],"has":[7],"recently":[8],"shown":[9],"superior":[10,179],"performance":[11,75,180],"on":[12,172],"image":[13,17,21,43,80,95,127,153],"retrieval.":[14],"It":[15,123],"evaluates":[16],"similarity":[18,99],"by":[19,51,65],"comparing":[20,78],"regions":[22,128],"at":[23],"different":[24],"locations":[25],"and":[26,28,138,157,188],"scales,":[27],"is,":[29],"therefore,":[30],"more":[31,197],"robust":[32],"to":[33,45,59,119,141,186],"geometric":[34],"variance":[35],"of":[36,76,87,105,110,145,181,196],"objects.":[37],"This":[38],"paper":[39,115],"first":[40],"scrutinizes":[41],"CRM-based":[42],"retrieval":[44],"provide":[46],"a":[47,70,130],"rigorous":[48],"probabilistic":[49,167],"interpretation":[50,68],"following":[52],"the":[53,61,74,85,88,98,106,134,143,146,178,182,193],"probability":[54],"ranking":[55],"principle.":[56],"In":[57],"addition":[58],"manifesting":[60],"assumptions":[62],"implicitly":[63],"taken":[64],"CRM,":[66],"our":[67],"highlights":[69],"fundamental":[71],"issue":[72],"hindering":[73],"CRM-when":[77],"two":[79],"regions,":[81],"CRM":[82,187],"ignores":[83],"modeling":[84],"distribution":[86,144],"visual":[89,136,147],"concept":[90,137,148],"class":[91,132,149],"associated":[92,150],"with":[93,151,165,192],"an":[94,152],"region,":[96],"making":[97],"comparison":[100],"less":[101],"precise.":[102],"Taking":[103],"advantage":[104],"unprecedented":[107],"representation":[108],"capability":[109],"this":[114,163],"proposes":[116],"one":[117],"approach":[118,185],"tackle":[120],"that":[121],"issue.":[122],"treats":[124],"locally":[125],"clustered":[126],"as":[129],"pseudo-labeled":[131],"sharing":[133],"same":[135],"utilizes":[139],"them":[140],"model":[142],"region.":[154],"Both":[155],"non-parametric":[156],"parametric":[158],"methods":[159],"are":[160],"developed":[161],"for":[162],"purpose,":[164],"careful":[166],"justification.":[168],"Extensive":[169],"experimental":[170],"study":[171],"multiple":[173],"benchmark":[174],"data":[175],"sets":[176],"demonstrates":[177],"proposed":[183],"pseudo-label":[184],"other":[189],"comparable":[190],"methods,":[191],"maximum":[194],"improvement":[195],"than":[198],"10":[199],"percentage":[200],"points":[201],"over":[202],"CRM.":[203]},"counts_by_year":[{"year":2025,"cited_by_count":1},{"year":2022,"cited_by_count":2},{"year":2021,"cited_by_count":4},{"year":2020,"cited_by_count":2},{"year":2019,"cited_by_count":2}],"updated_date":"2026-03-27T05:58:40.876381","created_date":"2025-10-10T00:00:00"}
