{"id":"https://openalex.org/W2974375613","doi":"https://doi.org/10.1109/tip.2019.2941663","title":"50 FPS Object-Level Saliency Detection via Maximally Stable Region","display_name":"50 FPS Object-Level Saliency Detection via Maximally Stable Region","publication_year":2019,"publication_date":"2019-09-20","ids":{"openalex":"https://openalex.org/W2974375613","doi":"https://doi.org/10.1109/tip.2019.2941663","mag":"2974375613","pmid":"https://pubmed.ncbi.nlm.nih.gov/31545727"},"language":"en","primary_location":{"id":"doi:10.1109/tip.2019.2941663","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tip.2019.2941663","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/A5100668774","display_name":"Xiaoming Huang","orcid":"https://orcid.org/0000-0003-4254-2820"},"institutions":[{"id":"https://openalex.org/I78675632","display_name":"Beijing Information Science & Technology University","ror":"https://ror.org/04xnqep60","country_code":"CN","type":"education","lineage":["https://openalex.org/I78675632"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xiaoming Huang","raw_affiliation_strings":["Computer School, Beijing Information Science and Technology University, Beijing, China","Computer school Beijing Information Science and Technology University, Beijing, China"],"raw_orcid":"https://orcid.org/0000-0003-4254-2820","affiliations":[{"raw_affiliation_string":"Computer School, Beijing Information Science and Technology University, Beijing, China","institution_ids":["https://openalex.org/I78675632"]},{"raw_affiliation_string":"Computer school Beijing Information Science and Technology University, Beijing, China","institution_ids":["https://openalex.org/I78675632"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5063581021","display_name":"Yin Zheng","orcid":"https://orcid.org/0000-0001-7838-4794"},"institutions":[{"id":"https://openalex.org/I2250653659","display_name":"Tencent (China)","ror":"https://ror.org/00hhjss72","country_code":"CN","type":"company","lineage":["https://openalex.org/I2250653659"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yin Zheng","raw_affiliation_strings":["WeChat Search Application Department, Tencent Beijing, Beijing, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"WeChat Search Application Department, Tencent Beijing, Beijing, China","institution_ids":["https://openalex.org/I2250653659"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5068865316","display_name":"Junzhou Huang","orcid":"https://orcid.org/0000-0002-9548-1227"},"institutions":[{"id":"https://openalex.org/I2250653659","display_name":"Tencent (China)","ror":"https://ror.org/00hhjss72","country_code":"CN","type":"company","lineage":["https://openalex.org/I2250653659"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Junzhou Huang","raw_affiliation_strings":["Tencent AI Lab, Shenzhen, China","Tencent AI Lab, ShenZhen, China#TAB#"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Tencent AI Lab, Shenzhen, China","institution_ids":["https://openalex.org/I2250653659"]},{"raw_affiliation_string":"Tencent AI Lab, ShenZhen, China#TAB#","institution_ids":["https://openalex.org/I2250653659"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5100684571","display_name":"Yu\u2010Jin Zhang","orcid":"https://orcid.org/0000-0002-2372-1180"},"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","Tsinghua National Laboratory for Information Science and Technology, Tsinghua University, Beijing, China","Tsinghua National Laboratory for Information Science and Technology, Tsinghua Univ, Beijing, China#TAB#"],"raw_orcid":"https://orcid.org/0000-0002-2372-1180","affiliations":[{"raw_affiliation_string":"Department of Electronic Engineering, Tsinghua University, Beijing, China","institution_ids":["https://openalex.org/I99065089"]},{"raw_affiliation_string":"Tsinghua National Laboratory for Information Science and Technology, Tsinghua University, Beijing, China","institution_ids":["https://openalex.org/I99065089"]},{"raw_affiliation_string":"Tsinghua National Laboratory for Information Science and Technology, Tsinghua Univ, Beijing, China#TAB#","institution_ids":["https://openalex.org/I99065089"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":4,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.6101,"has_fulltext":false,"cited_by_count":9,"citation_normalized_percentile":{"value":0.72859685,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":97},"biblio":{"volume":"29","issue":null,"first_page":"1384","last_page":"1396"},"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":0.9911999702453613,"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/T11165","display_name":"Image and Video Quality Assessment","score":0.9818000197410583,"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/minimum-bounding-box","display_name":"Minimum bounding box","score":0.8455237150192261},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.7925407886505127},{"id":"https://openalex.org/keywords/pixel","display_name":"Pixel","score":0.7679735422134399},{"id":"https://openalex.org/keywords/bounding-overwatch","display_name":"Bounding overwatch","score":0.7462155818939209},{"id":"https://openalex.org/keywords/object","display_name":"Object (grammar)","score":0.7357541918754578},{"id":"https://openalex.org/keywords/object-detection","display_name":"Object detection","score":0.7265258431434631},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6844407916069031},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.6500263214111328},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.5186416506767273},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.4742419123649597},{"id":"https://openalex.org/keywords/saliency-map","display_name":"Saliency map","score":0.44533661007881165},{"id":"https://openalex.org/keywords/viola\u2013jones-object-detection-framework","display_name":"Viola\u2013Jones object detection framework","score":0.43936556577682495},{"id":"https://openalex.org/keywords/cognitive-neuroscience-of-visual-object-recognition","display_name":"Cognitive neuroscience of visual object recognition","score":0.42903172969818115}],"concepts":[{"id":"https://openalex.org/C147037132","wikidata":"https://www.wikidata.org/wiki/Q6865426","display_name":"Minimum bounding box","level":3,"score":0.8455237150192261},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7925407886505127},{"id":"https://openalex.org/C160633673","wikidata":"https://www.wikidata.org/wiki/Q355198","display_name":"Pixel","level":2,"score":0.7679735422134399},{"id":"https://openalex.org/C63584917","wikidata":"https://www.wikidata.org/wiki/Q333286","display_name":"Bounding overwatch","level":2,"score":0.7462155818939209},{"id":"https://openalex.org/C2781238097","wikidata":"https://www.wikidata.org/wiki/Q175026","display_name":"Object (grammar)","level":2,"score":0.7357541918754578},{"id":"https://openalex.org/C2776151529","wikidata":"https://www.wikidata.org/wiki/Q3045304","display_name":"Object detection","level":3,"score":0.7265258431434631},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6844407916069031},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.6500263214111328},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.5186416506767273},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.4742419123649597},{"id":"https://openalex.org/C2779679900","wikidata":"https://www.wikidata.org/wiki/Q25304431","display_name":"Saliency map","level":3,"score":0.44533661007881165},{"id":"https://openalex.org/C182521987","wikidata":"https://www.wikidata.org/wiki/Q2493877","display_name":"Viola\u2013Jones object detection framework","level":5,"score":0.43936556577682495},{"id":"https://openalex.org/C64876066","wikidata":"https://www.wikidata.org/wiki/Q5141226","display_name":"Cognitive neuroscience of visual object recognition","level":3,"score":0.42903172969818115},{"id":"https://openalex.org/C31510193","wikidata":"https://www.wikidata.org/wiki/Q1192553","display_name":"Facial recognition system","level":3,"score":0.0},{"id":"https://openalex.org/C4641261","wikidata":"https://www.wikidata.org/wiki/Q11681085","display_name":"Face detection","level":4,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1109/tip.2019.2941663","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tip.2019.2941663","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:31545727","is_oa":false,"landing_page_url":"https://pubmed.ncbi.nlm.nih.gov/31545727","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/G1795326473","display_name":null,"funder_award_id":"U1636124","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G3632112915","display_name":"\u975e\u7ebf\u6027\u6a21\u5f0f\u4e0b\u7684\u975e\u8d1f\u77e9\u9635\u5206\u89e3\u7814\u7a76","funder_award_id":"61171118","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G5552182671","display_name":null,"funder_award_id":"61673234","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"}],"funders":[{"id":"https://openalex.org/F4320321001","display_name":"National Natural Science Foundation of China","ror":"https://ror.org/01h0zpd94"}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":59,"referenced_works":["https://openalex.org/W7746136","https://openalex.org/W21025885","https://openalex.org/W1772076007","https://openalex.org/W1903001680","https://openalex.org/W1923594904","https://openalex.org/W1942214758","https://openalex.org/W1947031653","https://openalex.org/W1982075130","https://openalex.org/W2020236530","https://openalex.org/W2037954058","https://openalex.org/W2039313011","https://openalex.org/W2046747811","https://openalex.org/W2047670868","https://openalex.org/W2048180049","https://openalex.org/W2066624635","https://openalex.org/W2086791339","https://openalex.org/W2087018183","https://openalex.org/W2098702446","https://openalex.org/W2100470808","https://openalex.org/W2118246710","https://openalex.org/W2127807804","https://openalex.org/W2128272608","https://openalex.org/W2128340050","https://openalex.org/W2129933262","https://openalex.org/W2130502991","https://openalex.org/W2131297486","https://openalex.org/W2133059825","https://openalex.org/W2146103513","https://openalex.org/W2166650627","https://openalex.org/W2211996548","https://openalex.org/W2280755940","https://openalex.org/W2293332611","https://openalex.org/W2294182682","https://openalex.org/W2342491128","https://openalex.org/W2358876993","https://openalex.org/W2472480899","https://openalex.org/W2486803733","https://openalex.org/W2520274358","https://openalex.org/W2528092473","https://openalex.org/W2581874211","https://openalex.org/W2585592883","https://openalex.org/W2605793178","https://openalex.org/W2612390049","https://openalex.org/W2615981376","https://openalex.org/W2620968817","https://openalex.org/W2742556866","https://openalex.org/W2757028014","https://openalex.org/W2766013565","https://openalex.org/W2773771410","https://openalex.org/W2798807298","https://openalex.org/W2798825526","https://openalex.org/W2908622466","https://openalex.org/W2955060956","https://openalex.org/W3098389804","https://openalex.org/W4212906384","https://openalex.org/W4239147634","https://openalex.org/W6640351089","https://openalex.org/W6679401573","https://openalex.org/W6784647857"],"related_works":["https://openalex.org/W4237171675","https://openalex.org/W3036286480","https://openalex.org/W4287027631","https://openalex.org/W3192357901","https://openalex.org/W2387360586","https://openalex.org/W2952736415","https://openalex.org/W3209723314","https://openalex.org/W3205398323","https://openalex.org/W2883297582","https://openalex.org/W4390524233"],"abstract_inverted_index":{"The":[0,105,161],"human":[1],"visual":[2],"system":[3],"tends":[4],"to":[5,59,82,94,140],"consider":[6,87],"saliency":[7,16,145],"of":[8,53],"an":[9],"object":[10,24,48,74,157],"as":[11,35,122],"a":[12,51],"whole.":[13],"Some":[14],"object-level":[15,84],"detection":[17,146],"methods":[18,155,169],"have":[19],"been":[20],"proposed":[21,121,162],"by":[22],"leveraging":[23],"proposals":[25,75],"in":[26,55,65,127],"bounding":[27,33,42,56,66],"boxes,":[28],"and":[29,50,113,156,195],"regarding":[30],"the":[31,41,60,102,117,166],"entire":[32],"box":[34,67],"one":[36,96,132],"candidate":[37],"salient":[38],"region.":[39],"However,":[40],"boxes":[43,57],"can":[44],"not":[45],"provide":[46],"exact":[47],"position":[49],"lot":[52],"pixels":[54,64],"belong":[58],"background.":[61],"Consequently,":[62],"background":[63],"also":[68],"show":[69,180],"high":[70,77],"saliency.":[71],"Besides,":[72],"acquiring":[73],"needs":[76],"time":[78],"cost.":[79],"In":[80,130],"order":[81],"compute":[83],"saliency,":[85],"we":[86,179],"region":[88,98,108,125],"growing":[89],"from":[90],"some":[91],"seed":[92,134],"superpixels,":[93],"find":[95],"surrounding":[97,107],"which":[99,119,189],"probably":[100],"represents":[101],"whole":[103],"object.":[104],"desired":[106],"has":[109],"similar":[110],"appearance":[111],"inside":[112],"obvious":[114],"difference":[115],"with":[116,174,184],"outside,":[118],"is":[120,138,147],"maximally":[123],"stable":[124],"(MSR)":[126],"this":[128],"paper.":[129],"addition,":[131],"effective":[133],"superpixel":[135,153],"selection":[136],"strategy":[137],"presented":[139],"improve":[141],"speed.":[142,196],"MSR":[143],"based":[144,159,177],"more":[148],"robust":[149],"than":[150],"pixel":[151],"or":[152],"level":[154],"proposal":[158],"methods.":[160],"method":[163],"significantly":[164],"outperforms":[165],"state-of-the-art":[167],"unsupervised":[168],"at":[170],"50":[171],"FPS.":[172],"Compared":[173],"deep":[175],"learning":[176],"methods,":[178],"worse":[181],"performance,":[182],"but":[183],"about":[185],"1200-1600":[186],"times":[187],"faster,":[188],"means":[190],"better":[191],"trade-off":[192],"between":[193],"performance":[194]},"counts_by_year":[{"year":2024,"cited_by_count":1},{"year":2023,"cited_by_count":2},{"year":2022,"cited_by_count":3},{"year":2021,"cited_by_count":2},{"year":2020,"cited_by_count":1}],"updated_date":"2026-06-11T09:08:48.828518","created_date":"2025-10-10T00:00:00"}
