{"id":"https://openalex.org/W1947031653","doi":"https://doi.org/10.1109/cvpr.2015.7298938","title":"Deep networks for saliency detection via local estimation and global search","display_name":"Deep networks for saliency detection via local estimation and global search","publication_year":2015,"publication_date":"2015-06-01","ids":{"openalex":"https://openalex.org/W1947031653","doi":"https://doi.org/10.1109/cvpr.2015.7298938","mag":"1947031653"},"language":"en","primary_location":{"id":"doi:10.1109/cvpr.2015.7298938","is_oa":false,"landing_page_url":"https://doi.org/10.1109/cvpr.2015.7298938","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)","raw_type":"proceedings-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"http://www.cv-foundation.org/openaccess/content_cvpr_2015/papers/Wang_Deep_Networks_for_2015_CVPR_paper.pdf","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5100318892","display_name":"Lijun Wang","orcid":"https://orcid.org/0000-0003-2538-8358"},"institutions":[{"id":"https://openalex.org/I27357992","display_name":"Dalian University of Technology","ror":"https://ror.org/023hj5876","country_code":"CN","type":"education","lineage":["https://openalex.org/I27357992"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Lijun Wang","raw_affiliation_strings":["Dalian University of Technology, Dalian, Liaoning, CN",", Dalian University of Technology, , China"],"affiliations":[{"raw_affiliation_string":"Dalian University of Technology, Dalian, Liaoning, CN","institution_ids":["https://openalex.org/I27357992"]},{"raw_affiliation_string":", Dalian University of Technology, , China","institution_ids":["https://openalex.org/I27357992"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5006986293","display_name":"Huchuan Lu","orcid":"https://orcid.org/0000-0002-6668-9758"},"institutions":[{"id":"https://openalex.org/I27357992","display_name":"Dalian University of Technology","ror":"https://ror.org/023hj5876","country_code":"CN","type":"education","lineage":["https://openalex.org/I27357992"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Huchuan Lu","raw_affiliation_strings":["Dalian University of Technology",", Dalian University of Technology, , China"],"affiliations":[{"raw_affiliation_string":"Dalian University of Technology","institution_ids":["https://openalex.org/I27357992"]},{"raw_affiliation_string":", Dalian University of Technology, , China","institution_ids":["https://openalex.org/I27357992"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5015967560","display_name":"Xiang Ruan","orcid":"https://orcid.org/0000-0003-4500-7516"},"institutions":[{"id":"https://openalex.org/I146230289","display_name":"Omron (Japan)","ror":"https://ror.org/00q0w1h45","country_code":"JP","type":"company","lineage":["https://openalex.org/I146230289"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"Xiang Ruan","raw_affiliation_strings":["Omron Kabushiki Kaisha, Kyoto, JP","OMRON Corporation, Japan#TAB#"],"affiliations":[{"raw_affiliation_string":"Omron Kabushiki Kaisha, Kyoto, JP","institution_ids":["https://openalex.org/I146230289"]},{"raw_affiliation_string":"OMRON Corporation, Japan#TAB#","institution_ids":["https://openalex.org/I146230289"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5100418319","display_name":"Ming\u2013Hsuan Yang","orcid":"https://orcid.org/0000-0003-4848-2304"},"institutions":[{"id":"https://openalex.org/I156087764","display_name":"University of California, Merced","ror":"https://ror.org/00d9ah105","country_code":"US","type":"education","lineage":["https://openalex.org/I156087764"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Ming-Hsuan Yang","raw_affiliation_strings":["University of California at Merced","University of California at Merced, USA"],"affiliations":[{"raw_affiliation_string":"University of California at Merced","institution_ids":["https://openalex.org/I156087764"]},{"raw_affiliation_string":"University of California at Merced, USA","institution_ids":["https://openalex.org/I156087764"]}]}],"institutions":[],"countries_distinct_count":3,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5100318892"],"corresponding_institution_ids":["https://openalex.org/I27357992"],"apc_list":null,"apc_paid":null,"fwci":43.4339,"has_fulltext":true,"cited_by_count":709,"citation_normalized_percentile":{"value":0.99825998,"is_in_top_1_percent":true,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":94,"max":100},"biblio":{"volume":null,"issue":null,"first_page":"3183","last_page":"3192"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11605","display_name":"Visual Attention and Saliency Detection","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/T11605","display_name":"Visual Attention and Saliency Detection","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/T10971","display_name":"Olfactory and Sensory Function Studies","score":0.996399998664856,"subfield":{"id":"https://openalex.org/subfields/2809","display_name":"Sensory Systems"},"field":{"id":"https://openalex.org/fields/28","display_name":"Neuroscience"},"domain":{"id":"https://openalex.org/domains/1","display_name":"Life Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.8164888024330139},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6987252831459045},{"id":"https://openalex.org/keywords/benchmark","display_name":"Benchmark (surveying)","score":0.6769247055053711},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.661226749420166},{"id":"https://openalex.org/keywords/contrast","display_name":"Contrast (vision)","score":0.60903400182724},{"id":"https://openalex.org/keywords/object","display_name":"Object (grammar)","score":0.5532141923904419},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.5146490335464478},{"id":"https://openalex.org/keywords/object-detection","display_name":"Object detection","score":0.5021486282348633},{"id":"https://openalex.org/keywords/pixel","display_name":"Pixel","score":0.4923001527786255},{"id":"https://openalex.org/keywords/saliency-map","display_name":"Saliency map","score":0.4720899760723114},{"id":"https://openalex.org/keywords/deep-neural-networks","display_name":"Deep neural networks","score":0.45881327986717224},{"id":"https://openalex.org/keywords/kadir\u2013brady-saliency-detector","display_name":"Kadir\u2013Brady saliency detector","score":0.4317290186882019},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.4114045798778534},{"id":"https://openalex.org/keywords/salient","display_name":"Salient","score":0.39883488416671753},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.3886585831642151},{"id":"https://openalex.org/keywords/geography","display_name":"Geography","score":0.0658847987651825}],"concepts":[{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.8164888024330139},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6987252831459045},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.6769247055053711},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.661226749420166},{"id":"https://openalex.org/C2776502983","wikidata":"https://www.wikidata.org/wiki/Q690182","display_name":"Contrast (vision)","level":2,"score":0.60903400182724},{"id":"https://openalex.org/C2781238097","wikidata":"https://www.wikidata.org/wiki/Q175026","display_name":"Object (grammar)","level":2,"score":0.5532141923904419},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.5146490335464478},{"id":"https://openalex.org/C2776151529","wikidata":"https://www.wikidata.org/wiki/Q3045304","display_name":"Object detection","level":3,"score":0.5021486282348633},{"id":"https://openalex.org/C160633673","wikidata":"https://www.wikidata.org/wiki/Q355198","display_name":"Pixel","level":2,"score":0.4923001527786255},{"id":"https://openalex.org/C2779679900","wikidata":"https://www.wikidata.org/wiki/Q25304431","display_name":"Saliency map","level":3,"score":0.4720899760723114},{"id":"https://openalex.org/C2984842247","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep neural networks","level":3,"score":0.45881327986717224},{"id":"https://openalex.org/C202227193","wikidata":"https://www.wikidata.org/wiki/Q6345568","display_name":"Kadir\u2013Brady saliency detector","level":4,"score":0.4317290186882019},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.4114045798778534},{"id":"https://openalex.org/C2780719617","wikidata":"https://www.wikidata.org/wiki/Q1030752","display_name":"Salient","level":2,"score":0.39883488416671753},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.3886585831642151},{"id":"https://openalex.org/C205649164","wikidata":"https://www.wikidata.org/wiki/Q1071","display_name":"Geography","level":0,"score":0.0658847987651825},{"id":"https://openalex.org/C13280743","wikidata":"https://www.wikidata.org/wiki/Q131089","display_name":"Geodesy","level":1,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1109/cvpr.2015.7298938","is_oa":false,"landing_page_url":"https://doi.org/10.1109/cvpr.2015.7298938","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)","raw_type":"proceedings-article"},{"id":"pmh:oai:repository.hanyang.ac.kr:20.500.11754/25604","is_oa":true,"landing_page_url":"http://www.cv-foundation.org/openaccess/content_cvpr_2015/html/Wang_Deep_Networks_for_2015_CVPR_paper.html","pdf_url":"http://www.cv-foundation.org/openaccess/content_cvpr_2015/papers/Wang_Deep_Networks_for_2015_CVPR_paper.pdf","source":{"id":"https://openalex.org/S4306401328","display_name":"The Royal Society of Chemistry\u2019s Journals, Books and Databases (The Royal Society of Chemistry)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I2751430930","host_organization_name":"Royal Society of Chemistry","host_organization_lineage":["https://openalex.org/I2751430930"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"","raw_type":"Article"}],"best_oa_location":{"id":"pmh:oai:repository.hanyang.ac.kr:20.500.11754/25604","is_oa":true,"landing_page_url":"http://www.cv-foundation.org/openaccess/content_cvpr_2015/html/Wang_Deep_Networks_for_2015_CVPR_paper.html","pdf_url":"http://www.cv-foundation.org/openaccess/content_cvpr_2015/papers/Wang_Deep_Networks_for_2015_CVPR_paper.pdf","source":{"id":"https://openalex.org/S4306401328","display_name":"The Royal Society of Chemistry\u2019s Journals, Books and Databases (The Royal Society of Chemistry)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I2751430930","host_organization_name":"Royal Society of Chemistry","host_organization_lineage":["https://openalex.org/I2751430930"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"","raw_type":"Article"},"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G1231421488","display_name":null,"funder_award_id":"under","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G2087396116","display_name":null,"funder_award_id":"China","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G2376276132","display_name":null,"funder_award_id":"China","funder_id":"https://openalex.org/F4320335787","funder_display_name":"Fundamental Research Funds for the Central Universities"},{"id":"https://openalex.org/G3317480652","display_name":null,"funder_award_id":"Science","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G4613586837","display_name":null,"funder_award_id":"1149783","funder_id":"https://openalex.org/F4320306076","funder_display_name":"National Science Foundation"},{"id":"https://openalex.org/G5669947928","display_name":null,"funder_award_id":"61472060","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G5994120800","display_name":null,"funder_award_id":"Natural","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G6471552119","display_name":null,"funder_award_id":"61472060","funder_id":"https://openalex.org/F4320335787","funder_display_name":"Fundamental Research Funds for the Central Universities"},{"id":"https://openalex.org/G6671297155","display_name":null,"funder_award_id":"CAREER","funder_id":"https://openalex.org/F4320306076","funder_display_name":"National Science Foundation"},{"id":"https://openalex.org/G6989841983","display_name":null,"funder_award_id":"DUT14YQ101","funder_id":"https://openalex.org/F4320335787","funder_display_name":"Fundamental Research Funds for the Central Universities"},{"id":"https://openalex.org/G848032724","display_name":null,"funder_award_id":"Science","funder_id":"https://openalex.org/F4320306076","funder_display_name":"National Science Foundation"},{"id":"https://openalex.org/G8951484681","display_name":null,"funder_award_id":"Grant","funder_id":"https://openalex.org/F4320335787","funder_display_name":"Fundamental Research Funds for the Central Universities"}],"funders":[{"id":"https://openalex.org/F4320306076","display_name":"National Science Foundation","ror":"https://ror.org/021nxhr62"},{"id":"https://openalex.org/F4320321001","display_name":"National Natural Science Foundation of China","ror":"https://ror.org/01h0zpd94"},{"id":"https://openalex.org/F4320335787","display_name":"Fundamental Research Funds for the Central Universities","ror":null}],"has_content":{"pdf":true,"grobid_xml":false},"content_urls":{"pdf":"https://content.openalex.org/works/W1947031653.pdf"},"referenced_works_count":53,"referenced_works":["https://openalex.org/W261873710","https://openalex.org/W1507506748","https://openalex.org/W1546771929","https://openalex.org/W1665214252","https://openalex.org/W1966822461","https://openalex.org/W1982075130","https://openalex.org/W1993939318","https://openalex.org/W1996326832","https://openalex.org/W2002574940","https://openalex.org/W2002781701","https://openalex.org/W2010181071","https://openalex.org/W2017691720","https://openalex.org/W2020523103","https://openalex.org/W2022508996","https://openalex.org/W2037954058","https://openalex.org/W2039313011","https://openalex.org/W2047670868","https://openalex.org/W2065985528","https://openalex.org/W2066624635","https://openalex.org/W2078132377","https://openalex.org/W2086791339","https://openalex.org/W2088049833","https://openalex.org/W2097117768","https://openalex.org/W2100470808","https://openalex.org/W2102605133","https://openalex.org/W2122076510","https://openalex.org/W2127983178","https://openalex.org/W2128272608","https://openalex.org/W2128340050","https://openalex.org/W2130306094","https://openalex.org/W2130502991","https://openalex.org/W2131095668","https://openalex.org/W2131297486","https://openalex.org/W2135957164","https://openalex.org/W2155541015","https://openalex.org/W2155893237","https://openalex.org/W2156777442","https://openalex.org/W2159772167","https://openalex.org/W2161185676","https://openalex.org/W2163605009","https://openalex.org/W2166650627","https://openalex.org/W2171378720","https://openalex.org/W4294375521","https://openalex.org/W6632360823","https://openalex.org/W6637242042","https://openalex.org/W6648775575","https://openalex.org/W6651042577","https://openalex.org/W6674914833","https://openalex.org/W6679349572","https://openalex.org/W6679401573","https://openalex.org/W6680437723","https://openalex.org/W6682778277","https://openalex.org/W6684191040"],"related_works":["https://openalex.org/W2805433183","https://openalex.org/W2001667162","https://openalex.org/W3009179364","https://openalex.org/W2351778949","https://openalex.org/W1967391339","https://openalex.org/W2725337569","https://openalex.org/W1510463930","https://openalex.org/W2381059340","https://openalex.org/W2286516139","https://openalex.org/W2145475630"],"abstract_inverted_index":{"This":[0],"paper":[1],"presents":[2,125],"a":[3,26,82,116,134],"saliency":[4,23,39,47,66,98,111,147,156],"detection":[5],"algorithm":[6,182],"by":[7,24,52,115,133,161],"integrating":[8],"both":[9],"local":[10,17,22,33,46,65,130,140],"estimation":[11,18],"and":[12,72,143,164,171],"global":[13,61,70,78,107,155],"search.":[14],"In":[15,59],"the":[16,38,54,60,64,97,106,150,186],"stage,":[19,63],"we":[20],"detect":[21],"using":[25],"deep":[27,89,162],"neural":[28,90],"network":[29,91],"(DNN-L)":[30],"which":[31],"learns":[32],"patch":[34],"features":[35,79,131],"to":[36,80,95],"determine":[37],"value":[40],"of":[41,84,100,119],"each":[42,101],"pixel.":[43],"The":[44,109],"estimated":[45],"maps":[48],"are":[49,75],"further":[50],"refined":[51],"exploring":[53],"high":[55],"level":[56],"object":[57,85,102,121],"concepts.":[58],"search":[62],"map":[67,112],"together":[68],"with":[69],"contrast":[71],"geometric":[73],"information":[74,145],"used":[76],"as":[77],"describe":[81],"set":[83],"candidate":[86],"regions.":[87,122],"Another":[88],"(DNN-G)":[92],"is":[93,113],"trained":[94],"predict":[96],"score":[99],"region":[103],"based":[104],"on":[105,174],"features.":[108],"final":[110],"generated":[114],"weighted":[117],"sum":[118],"salient":[120],"Our":[123],"method":[124],"two":[126],"interesting":[127],"insights.":[128],"First,":[129],"learned":[132],"supervised":[135],"scheme":[136],"can":[137,158],"effectively":[138],"capture":[139],"contrast,":[141],"texture":[142],"shape":[144],"for":[146],"detection.":[148],"Second,":[149],"complex":[151],"relationship":[152],"between":[153],"different":[154],"cues":[157],"be":[159],"captured":[160],"networks":[163],"exploited":[165],"principally":[166],"rather":[167],"than":[168],"heuristically.":[169],"Quantitative":[170],"qualitative":[172],"experiments":[173],"several":[175],"benchmark":[176],"data":[177],"sets":[178],"demonstrate":[179],"that":[180],"our":[181],"performs":[183],"favorably":[184],"against":[185],"state-of-the-art":[187],"methods.":[188]},"counts_by_year":[{"year":2026,"cited_by_count":2},{"year":2025,"cited_by_count":16},{"year":2024,"cited_by_count":27},{"year":2023,"cited_by_count":47},{"year":2022,"cited_by_count":54},{"year":2021,"cited_by_count":73},{"year":2020,"cited_by_count":117},{"year":2019,"cited_by_count":141},{"year":2018,"cited_by_count":101},{"year":2017,"cited_by_count":75},{"year":2016,"cited_by_count":54},{"year":2015,"cited_by_count":2}],"updated_date":"2026-04-19T08:26:33.389920","created_date":"2016-06-24T00:00:00"}