{"id":"https://openalex.org/W2902503812","doi":"https://doi.org/10.1109/icpr.2018.8546046","title":"Video Salient Object Detection via Multiple Time-scale Analysis","display_name":"Video Salient Object Detection via Multiple Time-scale Analysis","publication_year":2018,"publication_date":"2018-08-01","ids":{"openalex":"https://openalex.org/W2902503812","doi":"https://doi.org/10.1109/icpr.2018.8546046","mag":"2902503812"},"language":"en","primary_location":{"id":"doi:10.1109/icpr.2018.8546046","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icpr.2018.8546046","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2018 24th International Conference on Pattern Recognition (ICPR)","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/A5083649562","display_name":"Yuhuan Chen","orcid":null},"institutions":[{"id":"https://openalex.org/I180726961","display_name":"Shenzhen University","ror":"https://ror.org/01vy4gh70","country_code":"CN","type":"education","lineage":["https://openalex.org/I180726961"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yuhuan Chen","raw_affiliation_strings":["College of Information Engineering, Shenzhen University, Shenzhen, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"College of Information Engineering, Shenzhen University, Shenzhen, China","institution_ids":["https://openalex.org/I180726961"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101895600","display_name":"Limin Huang","orcid":"https://orcid.org/0000-0002-7944-2754"},"institutions":[{"id":"https://openalex.org/I4210105608","display_name":"ShenZhen People\u2019s Hospital","ror":"https://ror.org/01hcefx46","country_code":"CN","type":"healthcare","lineage":["https://openalex.org/I4210105608"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Limin Huang","raw_affiliation_strings":["The Operations Center of Shenzhen People's Hospital, Shenzhen, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"The Operations Center of Shenzhen People's Hospital, Shenzhen, China","institution_ids":["https://openalex.org/I4210105608"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5035616324","display_name":"Wenbin Zou","orcid":"https://orcid.org/0000-0003-1389-9089"},"institutions":[{"id":"https://openalex.org/I180726961","display_name":"Shenzhen University","ror":"https://ror.org/01vy4gh70","country_code":"CN","type":"education","lineage":["https://openalex.org/I180726961"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Wenbin Zou","raw_affiliation_strings":["College of Information Engineering, Shenzhen University, Shenzhen, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"College of Information Engineering, Shenzhen University, Shenzhen, China","institution_ids":["https://openalex.org/I180726961"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100445591","display_name":"Xia Li","orcid":"https://orcid.org/0000-0002-8043-9966"},"institutions":[{"id":"https://openalex.org/I180726961","display_name":"Shenzhen University","ror":"https://ror.org/01vy4gh70","country_code":"CN","type":"education","lineage":["https://openalex.org/I180726961"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xia Li","raw_affiliation_strings":["College of Information Engineering, Shenzhen University, Shenzhen, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"College of Information Engineering, Shenzhen University, Shenzhen, China","institution_ids":["https://openalex.org/I180726961"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5114259459","display_name":"Guoping Qiu","orcid":"https://orcid.org/0000-0002-5877-5648"},"institutions":[{"id":"https://openalex.org/I180726961","display_name":"Shenzhen University","ror":"https://ror.org/01vy4gh70","country_code":"CN","type":"education","lineage":["https://openalex.org/I180726961"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Guoping Qiu","raw_affiliation_strings":["College of Information Engineering, Shenzhen University, Shenzhen, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"College of Information Engineering, Shenzhen University, Shenzhen, China","institution_ids":["https://openalex.org/I180726961"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"2184","last_page":"2189"},"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/T11165","display_name":"Image and Video Quality Assessment","score":0.9970999956130981,"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":0.9955000281333923,"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":0.7851428985595703},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.7252367734909058},{"id":"https://openalex.org/keywords/contrast","display_name":"Contrast (vision)","score":0.6294089555740356},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.5889256000518799},{"id":"https://openalex.org/keywords/optical-flow","display_name":"Optical flow","score":0.5799764394760132},{"id":"https://openalex.org/keywords/benchmark","display_name":"Benchmark (surveying)","score":0.5699928998947144},{"id":"https://openalex.org/keywords/object-detection","display_name":"Object detection","score":0.5638102293014526},{"id":"https://openalex.org/keywords/video-tracking","display_name":"Video tracking","score":0.5492140054702759},{"id":"https://openalex.org/keywords/scale","display_name":"Scale (ratio)","score":0.5339018106460571},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.46246737241744995},{"id":"https://openalex.org/keywords/mistake","display_name":"Mistake","score":0.4474131464958191},{"id":"https://openalex.org/keywords/inference","display_name":"Inference","score":0.43039470911026},{"id":"https://openalex.org/keywords/object","display_name":"Object (grammar)","score":0.39090558886528015},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.18272697925567627}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7851428985595703},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7252367734909058},{"id":"https://openalex.org/C2776502983","wikidata":"https://www.wikidata.org/wiki/Q690182","display_name":"Contrast (vision)","level":2,"score":0.6294089555740356},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.5889256000518799},{"id":"https://openalex.org/C155542232","wikidata":"https://www.wikidata.org/wiki/Q736111","display_name":"Optical flow","level":3,"score":0.5799764394760132},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.5699928998947144},{"id":"https://openalex.org/C2776151529","wikidata":"https://www.wikidata.org/wiki/Q3045304","display_name":"Object detection","level":3,"score":0.5638102293014526},{"id":"https://openalex.org/C202474056","wikidata":"https://www.wikidata.org/wiki/Q1931635","display_name":"Video tracking","level":3,"score":0.5492140054702759},{"id":"https://openalex.org/C2778755073","wikidata":"https://www.wikidata.org/wiki/Q10858537","display_name":"Scale (ratio)","level":2,"score":0.5339018106460571},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.46246737241744995},{"id":"https://openalex.org/C2777179996","wikidata":"https://www.wikidata.org/wiki/Q911222","display_name":"Mistake","level":2,"score":0.4474131464958191},{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.43039470911026},{"id":"https://openalex.org/C2781238097","wikidata":"https://www.wikidata.org/wiki/Q175026","display_name":"Object (grammar)","level":2,"score":0.39090558886528015},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.18272697925567627},{"id":"https://openalex.org/C62520636","wikidata":"https://www.wikidata.org/wiki/Q944","display_name":"Quantum mechanics","level":1,"score":0.0},{"id":"https://openalex.org/C13280743","wikidata":"https://www.wikidata.org/wiki/Q131089","display_name":"Geodesy","level":1,"score":0.0},{"id":"https://openalex.org/C205649164","wikidata":"https://www.wikidata.org/wiki/Q1071","display_name":"Geography","level":0,"score":0.0},{"id":"https://openalex.org/C17744445","wikidata":"https://www.wikidata.org/wiki/Q36442","display_name":"Political science","level":0,"score":0.0},{"id":"https://openalex.org/C199539241","wikidata":"https://www.wikidata.org/wiki/Q7748","display_name":"Law","level":1,"score":0.0},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/icpr.2018.8546046","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icpr.2018.8546046","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2018 24th International Conference on Pattern Recognition (ICPR)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":20,"referenced_works":["https://openalex.org/W1490100156","https://openalex.org/W1593956231","https://openalex.org/W1954128991","https://openalex.org/W2012184117","https://openalex.org/W2036691646","https://openalex.org/W2076756823","https://openalex.org/W2104102555","https://openalex.org/W2110019070","https://openalex.org/W2293332611","https://openalex.org/W2461475918","https://openalex.org/W2470139095","https://openalex.org/W2519528544","https://openalex.org/W2560092514","https://openalex.org/W2757028014","https://openalex.org/W2793029440","https://openalex.org/W2963299740","https://openalex.org/W3101840568","https://openalex.org/W3125520697","https://openalex.org/W6726799623","https://openalex.org/W6785125108"],"related_works":["https://openalex.org/W1590719878","https://openalex.org/W4244271513","https://openalex.org/W4376622330","https://openalex.org/W2365974527","https://openalex.org/W4306382224","https://openalex.org/W1561425952","https://openalex.org/W2496555895","https://openalex.org/W2971088694","https://openalex.org/W2965594636","https://openalex.org/W2912550626"],"abstract_inverted_index":{"This":[0],"paper":[1],"focuses":[2],"on":[3,95,120],"salient":[4],"object":[5],"detection":[6,92,111,138],"in":[7],"video":[8],"by":[9,112],"multiple":[10],"time-scale":[11],"analysis,":[12],"which":[13],"exploits":[14],"the":[15,24,40,45,55,61,67,71,101,104,109,121,135],"temporally":[16],"consistent":[17],"information":[18],"under":[19],"three":[20,105],"different":[21],"scales.":[22],"In":[23,44,66],"first":[25],"time-scale,":[26,47,69],"we":[27,48,87],"define":[28],"an":[29],"effective":[30],"measure":[31],"called":[32],"motion":[33,57,91],"contrast":[34,58],"from":[35,103],"both":[36],"low-level":[37],"cues":[38],"and":[39,83,97,124],"optical":[41,64],"flow":[42],"fields.":[43],"second":[46],"propose":[49],"a":[50,78,89],"novel":[51],"approach":[52],"to":[53,60],"repair":[54],"inaccurate":[56],"due":[59],"mistake":[62],"of":[63,81],"flow.":[65],"third":[68],"considering":[70],"low-contrast":[72],"objects":[73],"that":[74,129],"stop":[75],"moving":[76],"for":[77],"certain":[79],"amount":[80],"time":[82],"cannot":[84],"remain":[85],"prominent,":[86],"present":[88],"robust":[90],"method":[93],"based":[94],"point-tracking":[96],"trajectories":[98],"clustering.":[99],"Finally,":[100],"outcomes":[102],"time-scales":[106],"jointly":[107],"formulate":[108],"saliency":[110,137],"Bayesian":[113],"inference.":[114],"The":[115],"proposed":[116,131],"model":[117,132],"is":[118],"evaluated":[119],"widely-used":[122],"DAVIS":[123],"FBMS":[125],"benchmark.":[126],"Experiments":[127],"demonstrate":[128],"our":[130],"substantially":[133],"outperforms":[134],"state-of-the-art":[136],"models.":[139]},"counts_by_year":[],"updated_date":"2026-07-14T23:27:15.235271","created_date":"2025-10-10T00:00:00"}
