{"id":"https://openalex.org/W2558479526","doi":"https://doi.org/10.1109/wcsp.2016.7752540","title":"Robust background exclusion for salient object detection","display_name":"Robust background exclusion for salient object detection","publication_year":2016,"publication_date":"2016-10-01","ids":{"openalex":"https://openalex.org/W2558479526","doi":"https://doi.org/10.1109/wcsp.2016.7752540","mag":"2558479526"},"language":"en","primary_location":{"id":"doi:10.1109/wcsp.2016.7752540","is_oa":false,"landing_page_url":"https://doi.org/10.1109/wcsp.2016.7752540","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2016 8th International Conference on Wireless Communications & Signal Processing (WCSP)","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/A5076477449","display_name":"Yuming Hu","orcid":"https://orcid.org/0000-0002-9151-7114"},"institutions":[{"id":"https://openalex.org/I41198531","display_name":"Nanjing University of Posts and Telecommunications","ror":"https://ror.org/043bpky34","country_code":"CN","type":"education","lineage":["https://openalex.org/I41198531"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yuming Hu","raw_affiliation_strings":["Key Lab of Ministry of Education for Broad Band Communication and Sensor Network Technology, Nanjing University of Posts and Telecommunications, Nanjing, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Key Lab of Ministry of Education for Broad Band Communication and Sensor Network Technology, Nanjing University of Posts and Telecommunications, Nanjing, China","institution_ids":["https://openalex.org/I41198531"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5060090179","display_name":"Quan Zhou","orcid":"https://orcid.org/0000-0002-7017-8192"},"institutions":[{"id":"https://openalex.org/I41198531","display_name":"Nanjing University of Posts and Telecommunications","ror":"https://ror.org/043bpky34","country_code":"CN","type":"education","lineage":["https://openalex.org/I41198531"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Quan Zhou","raw_affiliation_strings":["Nanjing University of Posts and Telecommunications, Nanjing, CN"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Nanjing University of Posts and Telecommunications, Nanjing, CN","institution_ids":["https://openalex.org/I41198531"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5038362365","display_name":"Guangwei Gao","orcid":"https://orcid.org/0000-0002-3950-1844"},"institutions":[{"id":"https://openalex.org/I41198531","display_name":"Nanjing University of Posts and Telecommunications","ror":"https://ror.org/043bpky34","country_code":"CN","type":"education","lineage":["https://openalex.org/I41198531"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Guangwei Gao","raw_affiliation_strings":["Nanjing University of Posts and Telecommunications, Nanjing, CN"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Nanjing University of Posts and Telecommunications, Nanjing, CN","institution_ids":["https://openalex.org/I41198531"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5074988832","display_name":"Zhijun Yao","orcid":"https://orcid.org/0000-0003-0057-0831"},"institutions":[{"id":"https://openalex.org/I147230869","display_name":"China Shipbuilding Industry Corporation (China)","ror":"https://ror.org/0410k9915","country_code":"CN","type":"company","lineage":["https://openalex.org/I147230869"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Zhijun Yao","raw_affiliation_strings":["The 723 Institute of China Shipbuilding Industry Corporation, Yangzhou, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"The 723 Institute of China Shipbuilding Industry Corporation, Yangzhou, China","institution_ids":["https://openalex.org/I147230869"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5028245257","display_name":"Weihua Ou","orcid":"https://orcid.org/0000-0001-5241-7703"},"institutions":[{"id":"https://openalex.org/I154893126","display_name":"Guizhou Normal University","ror":"https://ror.org/02x1pa065","country_code":"CN","type":"education","lineage":["https://openalex.org/I154893126"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Weihua Ou","raw_affiliation_strings":["School of Big Data and Computer Science, Guizhou Normal University, Guiyang, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Big Data and Computer Science, Guizhou Normal University, Guiyang, China","institution_ids":["https://openalex.org/I154893126"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5064854962","display_name":"Longin Jan Latecki","orcid":"https://orcid.org/0000-0002-5102-8244"},"institutions":[{"id":"https://openalex.org/I84392919","display_name":"Temple University","ror":"https://ror.org/00kx1jb78","country_code":"US","type":"education","lineage":["https://openalex.org/I84392919"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Longin Jan Latecki","raw_affiliation_strings":["Department of Computer and Information Science, Temple University, Philadelphia, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Computer and Information Science, Temple University, Philadelphia, USA","institution_ids":["https://openalex.org/I84392919"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":4,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.1006,"has_fulltext":false,"cited_by_count":2,"citation_normalized_percentile":{"value":0.42568745,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":94},"biblio":{"volume":"22","issue":null,"first_page":"1","last_page":"5"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11605","display_name":"Visual Attention and Saliency Detection","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/T11605","display_name":"Visual Attention and Saliency Detection","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/T10627","display_name":"Advanced Image and Video Retrieval Techniques","score":0.9746999740600586,"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/T11094","display_name":"Face Recognition and Perception","score":0.9743000268936157,"subfield":{"id":"https://openalex.org/subfields/2805","display_name":"Cognitive Neuroscience"},"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":0.7154024243354797},{"id":"https://openalex.org/keywords/boundary","display_name":"Boundary (topology)","score":0.707082211971283},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6836158633232117},{"id":"https://openalex.org/keywords/salient","display_name":"Salient","score":0.6684018969535828},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.5687477588653564},{"id":"https://openalex.org/keywords/object-detection","display_name":"Object detection","score":0.5620450377464294},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.5547922253608704},{"id":"https://openalex.org/keywords/measure","display_name":"Measure (data warehouse)","score":0.5519874095916748},{"id":"https://openalex.org/keywords/segmentation","display_name":"Segmentation","score":0.544755756855011},{"id":"https://openalex.org/keywords/image-segmentation","display_name":"Image segmentation","score":0.5043588876724243},{"id":"https://openalex.org/keywords/object","display_name":"Object (grammar)","score":0.49884963035583496},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.49852490425109863},{"id":"https://openalex.org/keywords/contrast","display_name":"Contrast (vision)","score":0.48790499567985535},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.27179157733917236},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.16860559582710266}],"concepts":[{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7154024243354797},{"id":"https://openalex.org/C62354387","wikidata":"https://www.wikidata.org/wiki/Q875399","display_name":"Boundary (topology)","level":2,"score":0.707082211971283},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6836158633232117},{"id":"https://openalex.org/C2780719617","wikidata":"https://www.wikidata.org/wiki/Q1030752","display_name":"Salient","level":2,"score":0.6684018969535828},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.5687477588653564},{"id":"https://openalex.org/C2776151529","wikidata":"https://www.wikidata.org/wiki/Q3045304","display_name":"Object detection","level":3,"score":0.5620450377464294},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.5547922253608704},{"id":"https://openalex.org/C2780009758","wikidata":"https://www.wikidata.org/wiki/Q6804172","display_name":"Measure (data warehouse)","level":2,"score":0.5519874095916748},{"id":"https://openalex.org/C89600930","wikidata":"https://www.wikidata.org/wiki/Q1423946","display_name":"Segmentation","level":2,"score":0.544755756855011},{"id":"https://openalex.org/C124504099","wikidata":"https://www.wikidata.org/wiki/Q56933","display_name":"Image segmentation","level":3,"score":0.5043588876724243},{"id":"https://openalex.org/C2781238097","wikidata":"https://www.wikidata.org/wiki/Q175026","display_name":"Object (grammar)","level":2,"score":0.49884963035583496},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.49852490425109863},{"id":"https://openalex.org/C2776502983","wikidata":"https://www.wikidata.org/wiki/Q690182","display_name":"Contrast (vision)","level":2,"score":0.48790499567985535},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.27179157733917236},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.16860559582710266},{"id":"https://openalex.org/C134306372","wikidata":"https://www.wikidata.org/wiki/Q7754","display_name":"Mathematical analysis","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/wcsp.2016.7752540","is_oa":false,"landing_page_url":"https://doi.org/10.1109/wcsp.2016.7752540","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2016 8th International Conference on Wireless Communications & Signal Processing (WCSP)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"display_name":"Reduced inequalities","id":"https://metadata.un.org/sdg/10","score":0.800000011920929}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":41,"referenced_works":["https://openalex.org/W21025885","https://openalex.org/W1510835000","https://openalex.org/W1581590495","https://openalex.org/W1772076007","https://openalex.org/W1872412942","https://openalex.org/W1965301399","https://openalex.org/W1981228217","https://openalex.org/W1982075130","https://openalex.org/W1996031228","https://openalex.org/W1996326832","https://openalex.org/W1999478155","https://openalex.org/W2002574940","https://openalex.org/W2002781701","https://openalex.org/W2034436892","https://openalex.org/W2037954058","https://openalex.org/W2067191022","https://openalex.org/W2071138464","https://openalex.org/W2080142539","https://openalex.org/W2098702446","https://openalex.org/W2100470808","https://openalex.org/W2103666701","https://openalex.org/W2105454024","https://openalex.org/W2116724443","https://openalex.org/W2117301471","https://openalex.org/W2121947440","https://openalex.org/W2128272608","https://openalex.org/W2128715914","https://openalex.org/W2133589685","https://openalex.org/W2135957164","https://openalex.org/W2137110664","https://openalex.org/W2146103513","https://openalex.org/W2157381954","https://openalex.org/W2161185676","https://openalex.org/W2293241115","https://openalex.org/W2301941309","https://openalex.org/W2398245424","https://openalex.org/W2567978322","https://openalex.org/W6651042577","https://openalex.org/W6670748880","https://openalex.org/W6674625355","https://openalex.org/W6680437723"],"related_works":["https://openalex.org/W2329500892","https://openalex.org/W28991112","https://openalex.org/W2370726991","https://openalex.org/W2369710579","https://openalex.org/W4327728159","https://openalex.org/W4394266730","https://openalex.org/W1990856605","https://openalex.org/W2053783616","https://openalex.org/W2545348020","https://openalex.org/W1522196789"],"abstract_inverted_index":{"Recently,":[0],"investigating":[1],"boundary":[2,56,93],"prior":[3],"to":[4,80,98,106,125,141,150],"aid":[5],"other":[6],"low-level":[7,144],"image":[8,27,55,107],"cues,":[9,145],"have":[10],"gained":[11],"great":[12],"attention":[13],"in":[14,25,119],"salient":[15,20,50],"object":[16,131],"detection.":[17],"Although":[18],"the":[19,26,29,54,58,127],"regions":[21,51],"are":[22,60,117],"mostly":[23],"located":[24],"center,":[28],"inverse":[30],"might":[31],"not":[32],"necessarily":[33],"be":[34],"true.":[35],"In":[36,123],"addition,":[37],"such":[38],"kind":[39],"of":[40,129],"center-bias":[41],"assumption":[42],"is":[43,96,104],"very":[44],"simple":[45],"and":[46,67,153],"fragile,":[47],"especially":[48],"when":[49],"often":[52],"touch":[53],"or":[57],"images":[59],"with":[61],"complex":[62],"background":[63,90,148],"(e.g.,":[64],"small":[65],"scale":[66],"high":[68],"contrast":[69],"patterns).":[70],"To":[71],"this":[72,74],"end,":[73],"paper":[75],"presents":[76],"a":[77,88,102,136],"new":[78],"framework":[79,140],"address":[81],"these":[82],"two":[83],"issues.":[84],"First,":[85],"we":[86,133],"propose":[87,135],"robust":[89],"descriptor,":[91],"called":[92],"connectivity,":[94],"which":[95],"calculated":[97],"measure":[99,110],"how":[100],"heavily":[101],"region":[103],"connected":[105],"boundary.":[108],"This":[109],"has":[111],"an":[112],"intuitive":[113],"geometrical":[114],"interpretation":[115],"that":[116,161],"absent":[118],"previous":[120],"saliency":[121,155],"formulation.":[122],"order":[124],"reduce":[126],"effect":[128],"imprecise":[130],"boundary,":[132],"also":[134],"multiple":[137,143],"layer":[138],"over-segmentation":[139],"integrate":[142],"including":[146],"our":[147,162],"measure,":[149],"highlight":[151],"clean":[152],"uniform":[154],"maps.":[156],"The":[157],"experiment":[158],"results":[159,166],"demonstrate":[160],"method":[163],"achieves":[164],"state-of-the-art":[165],"on":[167],"MSRA-1000":[168],"datasets.":[169]},"counts_by_year":[{"year":2020,"cited_by_count":1},{"year":2018,"cited_by_count":1}],"updated_date":"2026-07-29T14:22:42.915294","created_date":"2025-10-10T00:00:00"}