{"id":"https://openalex.org/W1577271904","doi":"https://doi.org/10.1109/chinasip.2015.7230461","title":"Video saliency prediction through machine learning with semantic information","display_name":"Video saliency prediction through machine learning with semantic information","publication_year":2015,"publication_date":"2015-07-01","ids":{"openalex":"https://openalex.org/W1577271904","doi":"https://doi.org/10.1109/chinasip.2015.7230461","mag":"1577271904"},"language":"en","primary_location":{"id":"doi:10.1109/chinasip.2015.7230461","is_oa":false,"landing_page_url":"https://doi.org/10.1109/chinasip.2015.7230461","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2015 IEEE China Summit and International Conference on Signal and Information Processing (ChinaSIP)","raw_type":"proceedings-article"},"type":"article","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/A5024888016","display_name":"Xiaohui Fu","orcid":"https://orcid.org/0000-0002-8818-7345"},"institutions":[{"id":"https://openalex.org/I4210165038","display_name":"University of Chinese Academy of Sciences","ror":"https://ror.org/05qbk4x57","country_code":"CN","type":"education","lineage":["https://openalex.org/I19820366","https://openalex.org/I4210165038"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Xiaohui Fu","raw_affiliation_strings":["University of Chinese Academy of Sciences, Beijing, China","University of Chinese Academy of Sciences, Beijing, China,"],"affiliations":[{"raw_affiliation_string":"University of Chinese Academy of Sciences, Beijing, China","institution_ids":["https://openalex.org/I4210165038"]},{"raw_affiliation_string":"University of Chinese Academy of Sciences, Beijing, China,","institution_ids":["https://openalex.org/I4210165038"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5088659027","display_name":"Li Su","orcid":"https://orcid.org/0000-0003-4038-753X"},"institutions":[{"id":"https://openalex.org/I4210165038","display_name":"University of Chinese Academy of Sciences","ror":"https://ror.org/05qbk4x57","country_code":"CN","type":"education","lineage":["https://openalex.org/I19820366","https://openalex.org/I4210165038"]},{"id":"https://openalex.org/I19820366","display_name":"Chinese Academy of Sciences","ror":"https://ror.org/034t30j35","country_code":"CN","type":"funder","lineage":["https://openalex.org/I19820366"]},{"id":"https://openalex.org/I4210096250","display_name":"Beijing Institute of Big Data Research","ror":"https://ror.org/00s1sz824","country_code":"CN","type":"facility","lineage":["https://openalex.org/I20231570","https://openalex.org/I37796252","https://openalex.org/I4210096250"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Li Su","raw_affiliation_strings":["Chinese Academy of Sciences, Key Lab on Big Data Mining and Knowledge Management, Beijing, China","University of Chinese Academy of Sciences, Beijing, China,"],"affiliations":[{"raw_affiliation_string":"Chinese Academy of Sciences, Key Lab on Big Data Mining and Knowledge Management, Beijing, China","institution_ids":["https://openalex.org/I4210096250","https://openalex.org/I19820366"]},{"raw_affiliation_string":"University of Chinese Academy of Sciences, Beijing, China,","institution_ids":["https://openalex.org/I4210165038"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5044388347","display_name":"Lei Qin","orcid":"https://orcid.org/0000-0002-6708-1285"},"institutions":[{"id":"https://openalex.org/I4210090176","display_name":"Institute of Computing Technology","ror":"https://ror.org/0090r4d87","country_code":"CN","type":"facility","lineage":["https://openalex.org/I19820366","https://openalex.org/I4210090176"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Lei Qin","raw_affiliation_strings":["Key Lab of Intelligent Information Processing, Institute of Computing Technology, Beijing, China"],"affiliations":[{"raw_affiliation_string":"Key Lab of Intelligent Information Processing, Institute of Computing Technology, Beijing, China","institution_ids":["https://openalex.org/I4210090176"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5024888016"],"corresponding_institution_ids":["https://openalex.org/I4210165038"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":1,"citation_normalized_percentile":{"value":0.03299722,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":91,"max":95},"biblio":{"volume":null,"issue":null,"first_page":"539","last_page":"543"},"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":0.9976000189781189,"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":0.9693999886512756,"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":0.8780738711357117},{"id":"https://openalex.org/keywords/automatic-summarization","display_name":"Automatic summarization","score":0.852840006351471},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.7074640989303589},{"id":"https://openalex.org/keywords/coding","display_name":"Coding (social sciences)","score":0.5476469397544861},{"id":"https://openalex.org/keywords/semantics","display_name":"Semantics (computer science)","score":0.5146268606185913},{"id":"https://openalex.org/keywords/process","display_name":"Process (computing)","score":0.4779924750328064},{"id":"https://openalex.org/keywords/feature-extraction","display_name":"Feature extraction","score":0.4510679244995117},{"id":"https://openalex.org/keywords/frame","display_name":"Frame (networking)","score":0.44657203555107117},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.4387417435646057},{"id":"https://openalex.org/keywords/orientation","display_name":"Orientation (vector space)","score":0.4124072194099426},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.37599438428878784},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.34768569469451904}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8780738711357117},{"id":"https://openalex.org/C170858558","wikidata":"https://www.wikidata.org/wiki/Q1394144","display_name":"Automatic summarization","level":2,"score":0.852840006351471},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7074640989303589},{"id":"https://openalex.org/C179518139","wikidata":"https://www.wikidata.org/wiki/Q5140297","display_name":"Coding (social sciences)","level":2,"score":0.5476469397544861},{"id":"https://openalex.org/C184337299","wikidata":"https://www.wikidata.org/wiki/Q1437428","display_name":"Semantics (computer science)","level":2,"score":0.5146268606185913},{"id":"https://openalex.org/C98045186","wikidata":"https://www.wikidata.org/wiki/Q205663","display_name":"Process (computing)","level":2,"score":0.4779924750328064},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.4510679244995117},{"id":"https://openalex.org/C126042441","wikidata":"https://www.wikidata.org/wiki/Q1324888","display_name":"Frame (networking)","level":2,"score":0.44657203555107117},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.4387417435646057},{"id":"https://openalex.org/C16345878","wikidata":"https://www.wikidata.org/wiki/Q107472979","display_name":"Orientation (vector space)","level":2,"score":0.4124072194099426},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.37599438428878784},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.34768569469451904},{"id":"https://openalex.org/C76155785","wikidata":"https://www.wikidata.org/wiki/Q418","display_name":"Telecommunications","level":1,"score":0.0},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.0},{"id":"https://openalex.org/C2524010","wikidata":"https://www.wikidata.org/wiki/Q8087","display_name":"Geometry","level":1,"score":0.0},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.0},{"id":"https://openalex.org/C199360897","wikidata":"https://www.wikidata.org/wiki/Q9143","display_name":"Programming language","level":1,"score":0.0},{"id":"https://openalex.org/C111919701","wikidata":"https://www.wikidata.org/wiki/Q9135","display_name":"Operating system","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/chinasip.2015.7230461","is_oa":false,"landing_page_url":"https://doi.org/10.1109/chinasip.2015.7230461","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2015 IEEE China Summit and International Conference on Signal and Information Processing (ChinaSIP)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":30,"referenced_works":["https://openalex.org/W65124300","https://openalex.org/W273955616","https://openalex.org/W1510835000","https://openalex.org/W1989779308","https://openalex.org/W1991857366","https://openalex.org/W2017518883","https://openalex.org/W2033362164","https://openalex.org/W2033859430","https://openalex.org/W2037328649","https://openalex.org/W2066076137","https://openalex.org/W2088827820","https://openalex.org/W2107055466","https://openalex.org/W2120807798","https://openalex.org/W2128272608","https://openalex.org/W2135957164","https://openalex.org/W2138046011","https://openalex.org/W2144764737","https://openalex.org/W2150669688","https://openalex.org/W2168356304","https://openalex.org/W2169561119","https://openalex.org/W2170869852","https://openalex.org/W2403280842","https://openalex.org/W6610017368","https://openalex.org/W6659689191","https://openalex.org/W6676006193","https://openalex.org/W6680437723","https://openalex.org/W6682641533","https://openalex.org/W6684705394","https://openalex.org/W6684745935","https://openalex.org/W6685314622"],"related_works":["https://openalex.org/W2366403280","https://openalex.org/W1495108544","https://openalex.org/W2091301346","https://openalex.org/W3148229873","https://openalex.org/W4389760904","https://openalex.org/W2150160875","https://openalex.org/W4242223894","https://openalex.org/W4306886878","https://openalex.org/W2973759123","https://openalex.org/W1517524280"],"abstract_inverted_index":{"Saliency":[0],"prediction":[1,140],"is":[2,25,41,96,125],"valuable":[3],"in":[4,74],"many":[5],"video":[6,16,19,45,56,119],"applications,":[7],"such":[8],"as":[9,128],"intelligent":[10],"retrieval,":[11],"advertisement":[12],"design":[13],"and":[14,18,71,79],"delivering,":[15],"coding":[17],"summarization":[20],"generating.":[21],"Although":[22],"image":[23],"saliency":[24,57,91],"well":[26],"explored,":[27],"less":[28],"works":[29],"have":[30],"been":[31],"done":[32],"on":[33,98,117],"videos.":[34],"Compared":[35],"to":[36,54,86,132],"images,":[37],"the":[38,69,88,106,122],"semantic":[39,60,82,89],"orientation":[40],"more":[42],"obvious":[43],"for":[44],"saliency.":[46],"In":[47],"this":[48],"paper,":[49],"we":[50,66,129],"propose":[51],"a":[52,75,81],"method":[53,95,108],"predict":[55],"by":[58],"introducing":[59],"information.":[61],"Different":[62],"from":[63],"existing":[64],"approaches,":[65],"simultaneously":[67],"consider":[68],"bottom-up":[70],"top-down":[72],"factors":[73],"machine":[76],"learning":[77,84],"framework":[78],"utilize":[80],"object":[83],"model":[85],"compute":[87],"related":[90],"map.":[92],"The":[93,101],"proposed":[94,107],"tested":[97],"two":[99],"datasets.":[100],"experiment":[102],"results":[103],"show":[104],"that":[105],"keeps":[109],"higher":[110],"consistent":[111],"with":[112],"human's":[113],"gaze":[114],"tracks":[115],"data":[116],"various":[118],"contents.":[120],"Furthermore,":[121],"computation":[123],"efficiency":[124],"also":[126],"improved":[127],"don't":[130],"need":[131],"process":[133],"every":[134],"pixel":[135],"of":[136],"each":[137],"frame":[138],"during":[139],"features":[141],"extraction.":[142]},"counts_by_year":[{"year":2025,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}