{"id":"https://openalex.org/W2512094178","doi":"https://doi.org/10.1109/icip.2016.7532862","title":"Salient object detection by multi-level features learning determined sparse reconstruction","display_name":"Salient object detection by multi-level features learning determined sparse reconstruction","publication_year":2016,"publication_date":"2016-08-17","ids":{"openalex":"https://openalex.org/W2512094178","doi":"https://doi.org/10.1109/icip.2016.7532862","mag":"2512094178"},"language":"en","primary_location":{"id":"doi:10.1109/icip.2016.7532862","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icip.2016.7532862","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2016 IEEE International Conference on Image Processing (ICIP)","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/A5042567603","display_name":"Xiaoyun Yan","orcid":"https://orcid.org/0000-0003-3545-2077"},"institutions":[{"id":"https://openalex.org/I47720641","display_name":"Huazhong University of Science and Technology","ror":"https://ror.org/00p991c53","country_code":"CN","type":"education","lineage":["https://openalex.org/I47720641"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Xiaoyun Yan","raw_affiliation_strings":["The School of Automation, Huazhong University of Science and Technology, Wuhan, China"],"affiliations":[{"raw_affiliation_string":"The School of Automation, Huazhong University of Science and Technology, Wuhan, China","institution_ids":["https://openalex.org/I47720641"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5030517812","display_name":"Yuehuan Wang","orcid":"https://orcid.org/0000-0001-7046-7587"},"institutions":[{"id":"https://openalex.org/I47720641","display_name":"Huazhong University of Science and Technology","ror":"https://ror.org/00p991c53","country_code":"CN","type":"education","lineage":["https://openalex.org/I47720641"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yuehuan Wang","raw_affiliation_strings":["The School of Automation, Huazhong University of Science and Technology, Wuhan, China"],"affiliations":[{"raw_affiliation_string":"The School of Automation, Huazhong University of Science and Technology, Wuhan, China","institution_ids":["https://openalex.org/I47720641"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5022200431","display_name":"Qiong Song","orcid":null},"institutions":[{"id":"https://openalex.org/I47720641","display_name":"Huazhong University of Science and Technology","ror":"https://ror.org/00p991c53","country_code":"CN","type":"education","lineage":["https://openalex.org/I47720641"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Qiong Song","raw_affiliation_strings":["The School of Automation, Huazhong University of Science and Technology, Wuhan, China"],"affiliations":[{"raw_affiliation_string":"The School of Automation, Huazhong University of Science and Technology, Wuhan, China","institution_ids":["https://openalex.org/I47720641"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5004071442","display_name":"Kaiheng Dai","orcid":"https://orcid.org/0000-0003-3591-9247"},"institutions":[{"id":"https://openalex.org/I47720641","display_name":"Huazhong University of Science and Technology","ror":"https://ror.org/00p991c53","country_code":"CN","type":"education","lineage":["https://openalex.org/I47720641"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Kaiheng Dai","raw_affiliation_strings":["The School of Automation, Huazhong University of Science and Technology, Wuhan, China"],"affiliations":[{"raw_affiliation_string":"The School of Automation, Huazhong University of Science and Technology, Wuhan, China","institution_ids":["https://openalex.org/I47720641"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5042567603"],"corresponding_institution_ids":["https://openalex.org/I47720641"],"apc_list":null,"apc_paid":null,"fwci":0.334,"has_fulltext":false,"cited_by_count":5,"citation_normalized_percentile":{"value":0.65974122,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":96},"biblio":{"volume":"33","issue":null,"first_page":"2762","last_page":"2766"},"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/T10971","display_name":"Olfactory and Sensory Function Studies","score":0.9866999983787537,"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"}},{"id":"https://openalex.org/T10627","display_name":"Advanced Image and Video Retrieval Techniques","score":0.9815999865531921,"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/artificial-intelligence","display_name":"Artificial intelligence","score":0.8134860992431641},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7096540331840515},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.7087739109992981},{"id":"https://openalex.org/keywords/salient","display_name":"Salient","score":0.6636934280395508},{"id":"https://openalex.org/keywords/false-positive-paradox","display_name":"False positive paradox","score":0.643683910369873},{"id":"https://openalex.org/keywords/object-detection","display_name":"Object detection","score":0.5862006545066833},{"id":"https://openalex.org/keywords/object","display_name":"Object (grammar)","score":0.495760053396225},{"id":"https://openalex.org/keywords/segmentation","display_name":"Segmentation","score":0.4950841963291168},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.47774428129196167},{"id":"https://openalex.org/keywords/random-forest","display_name":"Random forest","score":0.45858025550842285},{"id":"https://openalex.org/keywords/image-segmentation","display_name":"Image segmentation","score":0.4348652958869934},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.4205489456653595}],"concepts":[{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.8134860992431641},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7096540331840515},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.7087739109992981},{"id":"https://openalex.org/C2780719617","wikidata":"https://www.wikidata.org/wiki/Q1030752","display_name":"Salient","level":2,"score":0.6636934280395508},{"id":"https://openalex.org/C64869954","wikidata":"https://www.wikidata.org/wiki/Q1859747","display_name":"False positive paradox","level":2,"score":0.643683910369873},{"id":"https://openalex.org/C2776151529","wikidata":"https://www.wikidata.org/wiki/Q3045304","display_name":"Object detection","level":3,"score":0.5862006545066833},{"id":"https://openalex.org/C2781238097","wikidata":"https://www.wikidata.org/wiki/Q175026","display_name":"Object (grammar)","level":2,"score":0.495760053396225},{"id":"https://openalex.org/C89600930","wikidata":"https://www.wikidata.org/wiki/Q1423946","display_name":"Segmentation","level":2,"score":0.4950841963291168},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.47774428129196167},{"id":"https://openalex.org/C169258074","wikidata":"https://www.wikidata.org/wiki/Q245748","display_name":"Random forest","level":2,"score":0.45858025550842285},{"id":"https://openalex.org/C124504099","wikidata":"https://www.wikidata.org/wiki/Q56933","display_name":"Image segmentation","level":3,"score":0.4348652958869934},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.4205489456653595}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/icip.2016.7532862","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icip.2016.7532862","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2016 IEEE International Conference on Image Processing (ICIP)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"score":0.6100000143051147,"display_name":"Life in Land","id":"https://metadata.un.org/sdg/15"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":33,"referenced_works":["https://openalex.org/W21025885","https://openalex.org/W1772076007","https://openalex.org/W1903001680","https://openalex.org/W1918837316","https://openalex.org/W1923594904","https://openalex.org/W1982075130","https://openalex.org/W1996326832","https://openalex.org/W2002574940","https://openalex.org/W2002781701","https://openalex.org/W2020523103","https://openalex.org/W2037954058","https://openalex.org/W2039313011","https://openalex.org/W2041719651","https://openalex.org/W2047670868","https://openalex.org/W2055180303","https://openalex.org/W2065372998","https://openalex.org/W2066624635","https://openalex.org/W2081357526","https://openalex.org/W2086791339","https://openalex.org/W2098702446","https://openalex.org/W2100470808","https://openalex.org/W2118246710","https://openalex.org/W2128340050","https://openalex.org/W2131297486","https://openalex.org/W2158535435","https://openalex.org/W2161185676","https://openalex.org/W2567978322","https://openalex.org/W3104979525","https://openalex.org/W4239147634","https://openalex.org/W6640351089","https://openalex.org/W6640425316","https://openalex.org/W6651042577","https://openalex.org/W6678542115"],"related_works":["https://openalex.org/W1557094818","https://openalex.org/W2329500892","https://openalex.org/W28991112","https://openalex.org/W2370726991","https://openalex.org/W3193043704","https://openalex.org/W4386259002","https://openalex.org/W1546989560","https://openalex.org/W2183246718","https://openalex.org/W3171520305","https://openalex.org/W2099261052"],"abstract_inverted_index":{"We":[0],"propose":[1],"a":[2,29,54],"salient":[3,103],"object":[4,104],"detection":[5,74,123],"algorithm":[6],"via":[7],"multilevel":[8],"features":[9,40],"learning":[10],"determined":[11,81],"sparse":[12,79],"reconstruction.":[13],"There":[14],"are":[15,25,41,105,116],"three":[16,37],"stages":[17],"in":[18,111,121],"our":[19,122,129],"method.":[20],"First,":[21,98],"the":[22,58,72,83,91,99],"test":[23],"image":[24],"successively":[26],"processed":[27],"by":[28,82],"segmentation":[30],"and":[31,46],"semantic":[32],"information":[33],"generation":[34],"procedures.":[35],"Second,":[36,114],"kinds":[38],"of":[39,128],"extracted":[42],"from":[43],"semantic,":[44],"global,":[45],"local":[47],"levels":[48],"for":[49],"each":[50],"superpixel":[51],"to":[52,65],"train":[53],"random":[55],"forest":[56],"regressor,":[57],"learned":[59],"regression":[60],"model":[61],"is":[62,76],"then":[63],"used":[64],"generate":[66],"an":[67],"initial":[68,84],"saliency":[69,85,109,112],"map.":[70,86,113],"Third,":[71],"ultimate":[73],"result":[75],"produced":[77],"using":[78],"reconstruction":[80],"Compared":[87],"with":[88,136],"most":[89],"approaches,":[90],"proposed":[92],"method":[93,130],"has":[94],"two":[95],"obvious":[96],"advantages.":[97],"heterogeneous":[100],"regions":[101],"inside":[102],"often":[106],"allocated":[107],"similar":[108],"values":[110],"there":[115],"much":[117],"fewer":[118],"false":[119],"positives":[120],"results.":[124],"The":[125],"superior":[126],"performance":[127],"were":[131],"evaluated":[132],"on":[133],"four":[134],"datasets":[135],"12":[137],"state-of-the-art":[138],"approaches.":[139]},"counts_by_year":[{"year":2023,"cited_by_count":1},{"year":2020,"cited_by_count":2},{"year":2019,"cited_by_count":1},{"year":2018,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}