{"id":"https://openalex.org/W2114727884","doi":"https://doi.org/10.1145/1873951.1874105","title":"Automatic interesting object extraction from images using complementary saliency maps","display_name":"Automatic interesting object extraction from images using complementary saliency maps","publication_year":2010,"publication_date":"2010-10-25","ids":{"openalex":"https://openalex.org/W2114727884","doi":"https://doi.org/10.1145/1873951.1874105","mag":"2114727884"},"language":"en","primary_location":{"id":"doi:10.1145/1873951.1874105","is_oa":false,"landing_page_url":"https://doi.org/10.1145/1873951.1874105","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 18th ACM international conference on Multimedia","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/A5009585935","display_name":"Haonan Yu","orcid":"https://orcid.org/0000-0001-9882-4929"},"institutions":[{"id":"https://openalex.org/I20231570","display_name":"Peking University","ror":"https://ror.org/02v51f717","country_code":"CN","type":"education","lineage":["https://openalex.org/I20231570"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Haonan Yu","raw_affiliation_strings":["National Engineering Laboratory for Video Technology (NELVT), School of EE & CS, Peking University, Beijing, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"National Engineering Laboratory for Video Technology (NELVT), School of EE & CS, Peking University, Beijing, China","institution_ids":["https://openalex.org/I20231570"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100648222","display_name":"Jia Li","orcid":"https://orcid.org/0000-0002-2956-2846"},"institutions":[{"id":"https://openalex.org/I19820366","display_name":"Chinese Academy of Sciences","ror":"https://ror.org/034t30j35","country_code":"CN","type":"government","lineage":["https://openalex.org/I19820366"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Jia Li","raw_affiliation_strings":["Key Lab of Intell. Info. Process, Inst. of Comput. Tech., Chinese Academy of Sciences, China, Beijing, China","Key Lab of Intell. Info. Process, Inst. of Comput. Tech., Chinese Academy of Sciences, China, Beijing, China#TAB#"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Key Lab of Intell. Info. Process, Inst. of Comput. Tech., Chinese Academy of Sciences, China, Beijing, China","institution_ids":["https://openalex.org/I19820366"]},{"raw_affiliation_string":"Key Lab of Intell. Info. Process, Inst. of Comput. Tech., Chinese Academy of Sciences, China, Beijing, China#TAB#","institution_ids":["https://openalex.org/I19820366"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5023918894","display_name":"Yonghong Tian","orcid":"https://orcid.org/0000-0002-2978-5935"},"institutions":[{"id":"https://openalex.org/I20231570","display_name":"Peking University","ror":"https://ror.org/02v51f717","country_code":"CN","type":"education","lineage":["https://openalex.org/I20231570"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yonghong Tian","raw_affiliation_strings":["National Engineering Laboratory for Video Technology (NELVT), School of EE & CS, Peking University, Beijing, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"National Engineering Laboratory for Video Technology (NELVT), School of EE & CS, Peking University, Beijing, China","institution_ids":["https://openalex.org/I20231570"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5058066577","display_name":"Tiejun Huang","orcid":"https://orcid.org/0000-0002-4234-6099"},"institutions":[{"id":"https://openalex.org/I20231570","display_name":"Peking University","ror":"https://ror.org/02v51f717","country_code":"CN","type":"education","lineage":["https://openalex.org/I20231570"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Tiejun Huang","raw_affiliation_strings":["National Engineering Laboratory for Video Technology (NELVT), School of EE & CS, Peking University, Beijing, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"National Engineering Laboratory for Video Technology (NELVT), School of EE & CS, Peking University, Beijing, China","institution_ids":["https://openalex.org/I20231570"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":4,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":4.1978,"has_fulltext":false,"cited_by_count":39,"citation_normalized_percentile":{"value":0.94742005,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":89,"max":99},"biblio":{"volume":null,"issue":null,"first_page":"891","last_page":"894"},"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":0.9994000196456909,"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/T10824","display_name":"Image Retrieval and Classification Techniques","score":0.9943000078201294,"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":0.7241194844245911},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.694472074508667},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6890153288841248},{"id":"https://openalex.org/keywords/object","display_name":"Object (grammar)","score":0.607835054397583},{"id":"https://openalex.org/keywords/extraction","display_name":"Extraction (chemistry)","score":0.5557224750518799},{"id":"https://openalex.org/keywords/feature-extraction","display_name":"Feature extraction","score":0.5361291170120239},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.5296416282653809},{"id":"https://openalex.org/keywords/visualization","display_name":"Visualization","score":0.42988088726997375},{"id":"https://openalex.org/keywords/chromatography","display_name":"Chromatography","score":0.06347689032554626}],"concepts":[{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7241194844245911},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.694472074508667},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6890153288841248},{"id":"https://openalex.org/C2781238097","wikidata":"https://www.wikidata.org/wiki/Q175026","display_name":"Object (grammar)","level":2,"score":0.607835054397583},{"id":"https://openalex.org/C4725764","wikidata":"https://www.wikidata.org/wiki/Q844704","display_name":"Extraction (chemistry)","level":2,"score":0.5557224750518799},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.5361291170120239},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.5296416282653809},{"id":"https://openalex.org/C36464697","wikidata":"https://www.wikidata.org/wiki/Q451553","display_name":"Visualization","level":2,"score":0.42988088726997375},{"id":"https://openalex.org/C43617362","wikidata":"https://www.wikidata.org/wiki/Q170050","display_name":"Chromatography","level":1,"score":0.06347689032554626},{"id":"https://openalex.org/C185592680","wikidata":"https://www.wikidata.org/wiki/Q2329","display_name":"Chemistry","level":0,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1145/1873951.1874105","is_oa":false,"landing_page_url":"https://doi.org/10.1145/1873951.1874105","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 18th ACM international conference on Multimedia","raw_type":"proceedings-article"},{"id":"pmh:oai:CiteSeerX.psu:10.1.1.658.6486","is_oa":false,"landing_page_url":"http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.658.6486","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"http://www.jdl.ac.cn/doc/2010/mm2010_Automatic+interesting+object+extraction+from+images+using+complementary+saliency+maps.pdf","raw_type":"text"}],"best_oa_location":null,"sustainable_development_goals":[{"display_name":"Reduced inequalities","score":0.41999998688697815,"id":"https://metadata.un.org/sdg/10"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":18,"referenced_works":["https://openalex.org/W1872412942","https://openalex.org/W2078132377","https://openalex.org/W2100470808","https://openalex.org/W2117193984","https://openalex.org/W2122184585","https://openalex.org/W2124351162","https://openalex.org/W2128272608","https://openalex.org/W2135957164","https://openalex.org/W2146103513","https://openalex.org/W2151153808","https://openalex.org/W2153963165","https://openalex.org/W2157554677","https://openalex.org/W2344048381","https://openalex.org/W2978707742","https://openalex.org/W2990138404","https://openalex.org/W4246999471","https://openalex.org/W4248635988","https://openalex.org/W6680437723"],"related_works":["https://openalex.org/W2975200075","https://openalex.org/W2007544051","https://openalex.org/W1837097281","https://openalex.org/W1966410754","https://openalex.org/W2363840281","https://openalex.org/W2016398835","https://openalex.org/W2325242284","https://openalex.org/W2095705906","https://openalex.org/W2789220062","https://openalex.org/W258625772"],"abstract_inverted_index":{"Automatic":[0],"interesting":[1],"object":[2,51,79,142,183],"extraction":[3,13,80,99,152],"is":[4,44,101,136,186],"widely":[5],"used":[6,107,121,137],"in":[7,181],"many":[8],"image":[9],"applications.":[10],"Among":[11],"various":[12],"approaches,":[14],"saliency-based":[15,34,178],"ones":[16],"usually":[17],"have":[18],"a":[19,46,57,77,110,124,133],"better":[20],"performance":[21],"since":[22],"they":[23],"well":[24],"accord":[25],"with":[26],"human":[27],"visual":[28],"perception.":[29],"However,":[30],"nearly":[31],"all":[32],"existing":[33],"approaches":[35],"suffer":[36],"the":[37,41,50,66,98,115,119,129,145,150,165,172],"integrity":[38,166],"problem,":[39,158],"namely,":[40],"extracted":[42],"result":[43,112,126],"either":[45],"small":[47],"part":[48,64],"of":[49,65,86],"(referred":[52,68],"to":[53,69,108,122,138,154,189],"as":[54,70],"sketch-like)":[55],"or":[56],"large":[58],"region":[59],"that":[60,171],"contains":[61],"some":[62,190],"redundant":[63],"background":[67],"envelope-like).":[71],"In":[72,95],"this":[73],"paper,":[74],"we":[75],"propose":[76],"novel":[78],"approach":[81,160,174],"by":[82],"integrating":[83],"two":[84,104,146],"kinds":[85],"\"complementary\"":[87],"saliency":[88],"maps":[89],"(i.e.,":[90],"sketch-like":[91,116],"and":[92,118,185],"envelope-like":[93,130],"maps).":[94],"our":[96,159],"approach,":[97],"process":[100],"decomposed":[102],"into":[103],"sub-processes,":[105],"one":[106],"extract":[109,123,139],"high-precision":[111],"based":[113,127,143],"on":[114,128,144],"map,":[117],"other":[120],"high-recall":[125],"map.":[131],"Then":[132],"classification":[134,157],"step":[135],"an":[140,155],"exact":[141],"results.":[147],"By":[148],"transferring":[149],"complex":[151],"task":[153],"easier":[156],"can":[161],"effectively":[162],"break":[163],"down":[164],"problem.":[167],"Experimental":[168],"results":[169],"show":[170],"proposed":[173],"outperforms":[175],"six":[176],"state-of-art":[177],"methods":[179],"remarkably":[180],"automatic":[182],"extraction,":[184],"even":[187],"comparable":[188],"interactive":[191],"approaches.":[192]},"counts_by_year":[{"year":2022,"cited_by_count":3},{"year":2021,"cited_by_count":1},{"year":2020,"cited_by_count":1},{"year":2018,"cited_by_count":5},{"year":2017,"cited_by_count":3},{"year":2016,"cited_by_count":5},{"year":2015,"cited_by_count":1},{"year":2014,"cited_by_count":7},{"year":2013,"cited_by_count":5}],"updated_date":"2026-06-11T09:08:48.828518","created_date":"2025-10-10T00:00:00"}