{"id":"https://openalex.org/W3033751696","doi":"https://doi.org/10.1145/3390557.3394125","title":"Saliency detection based on weighted color contrast of image patch","display_name":"Saliency detection based on weighted color contrast of image patch","publication_year":2020,"publication_date":"2020-05-08","ids":{"openalex":"https://openalex.org/W3033751696","doi":"https://doi.org/10.1145/3390557.3394125","mag":"3033751696"},"language":"en","primary_location":{"id":"doi:10.1145/3390557.3394125","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3390557.3394125","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 2020 the 4th International Conference on Innovation in Artificial Intelligence","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/A5100743856","display_name":"Chao Jia","orcid":"https://orcid.org/0000-0002-1588-4836"},"institutions":[{"id":"https://openalex.org/I4400600917","display_name":"Guangzhou College of Commerce","ror":"https://ror.org/04f0j5d06","country_code":null,"type":"education","lineage":["https://openalex.org/I4400600917"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Chao Jia","raw_affiliation_strings":["Guangzhou College of Commerce, Guangzhou, China"],"affiliations":[{"raw_affiliation_string":"Guangzhou College of Commerce, Guangzhou, China","institution_ids":["https://openalex.org/I4400600917"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5010166057","display_name":"Fanshu Kong","orcid":null},"institutions":[{"id":"https://openalex.org/I39333907","display_name":"Yanshan University","ror":"https://ror.org/02txfnf15","country_code":"CN","type":"education","lineage":["https://openalex.org/I39333907"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Fanshu Kong","raw_affiliation_strings":["Yanshan University, Qinhuangdao, China"],"affiliations":[{"raw_affiliation_string":"Yanshan University, Qinhuangdao, China","institution_ids":["https://openalex.org/I39333907"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5076406655","display_name":"Changrun Jia","orcid":null},"institutions":[{"id":"https://openalex.org/I4400600917","display_name":"Guangzhou College of Commerce","ror":"https://ror.org/04f0j5d06","country_code":null,"type":"education","lineage":["https://openalex.org/I4400600917"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Changrun Jia","raw_affiliation_strings":["Guangzhou College of Commerce, Guangzhou, China"],"affiliations":[{"raw_affiliation_string":"Guangzhou College of Commerce, Guangzhou, China","institution_ids":["https://openalex.org/I4400600917"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5100743856"],"corresponding_institution_ids":["https://openalex.org/I4400600917"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.05059824,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"90","last_page":"94"},"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":0.9595000147819519,"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":0.9562000036239624,"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":0.8195908069610596},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.7096314430236816},{"id":"https://openalex.org/keywords/pixel","display_name":"Pixel","score":0.7070536017417908},{"id":"https://openalex.org/keywords/contrast","display_name":"Contrast (vision)","score":0.6768462657928467},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6308708190917969},{"id":"https://openalex.org/keywords/image-texture","display_name":"Image texture","score":0.6028687953948975},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.5808274149894714},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.5282779335975647},{"id":"https://openalex.org/keywords/precision-and-recall","display_name":"Precision and recall","score":0.5171065330505371},{"id":"https://openalex.org/keywords/salient","display_name":"Salient","score":0.5096542835235596},{"id":"https://openalex.org/keywords/noise","display_name":"Noise (video)","score":0.4871932864189148},{"id":"https://openalex.org/keywords/image-segmentation","display_name":"Image segmentation","score":0.4734622836112976},{"id":"https://openalex.org/keywords/color-image","display_name":"Color image","score":0.47128787636756897},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.3659283220767975},{"id":"https://openalex.org/keywords/image-processing","display_name":"Image processing","score":0.31090378761291504}],"concepts":[{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.8195908069610596},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.7096314430236816},{"id":"https://openalex.org/C160633673","wikidata":"https://www.wikidata.org/wiki/Q355198","display_name":"Pixel","level":2,"score":0.7070536017417908},{"id":"https://openalex.org/C2776502983","wikidata":"https://www.wikidata.org/wiki/Q690182","display_name":"Contrast (vision)","level":2,"score":0.6768462657928467},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6308708190917969},{"id":"https://openalex.org/C63099799","wikidata":"https://www.wikidata.org/wiki/Q17147001","display_name":"Image texture","level":4,"score":0.6028687953948975},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.5808274149894714},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.5282779335975647},{"id":"https://openalex.org/C81669768","wikidata":"https://www.wikidata.org/wiki/Q2359161","display_name":"Precision and recall","level":2,"score":0.5171065330505371},{"id":"https://openalex.org/C2780719617","wikidata":"https://www.wikidata.org/wiki/Q1030752","display_name":"Salient","level":2,"score":0.5096542835235596},{"id":"https://openalex.org/C99498987","wikidata":"https://www.wikidata.org/wiki/Q2210247","display_name":"Noise (video)","level":3,"score":0.4871932864189148},{"id":"https://openalex.org/C124504099","wikidata":"https://www.wikidata.org/wiki/Q56933","display_name":"Image segmentation","level":3,"score":0.4734622836112976},{"id":"https://openalex.org/C142616399","wikidata":"https://www.wikidata.org/wiki/Q5148604","display_name":"Color image","level":4,"score":0.47128787636756897},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.3659283220767975},{"id":"https://openalex.org/C9417928","wikidata":"https://www.wikidata.org/wiki/Q1070689","display_name":"Image processing","level":3,"score":0.31090378761291504}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3390557.3394125","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3390557.3394125","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 2020 the 4th International Conference on Innovation in Artificial Intelligence","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":11,"referenced_works":["https://openalex.org/W1981228217","https://openalex.org/W2037328649","https://openalex.org/W2100470808","https://openalex.org/W2118228285","https://openalex.org/W2120504013","https://openalex.org/W2128272608","https://openalex.org/W2518599539","https://openalex.org/W2592997798","https://openalex.org/W2766063389","https://openalex.org/W6658442314","https://openalex.org/W6660241312"],"related_works":["https://openalex.org/W4360784979","https://openalex.org/W3017192027","https://openalex.org/W2204605857","https://openalex.org/W1996489018","https://openalex.org/W2007664797","https://openalex.org/W2120981610","https://openalex.org/W3129669851","https://openalex.org/W2360759360","https://openalex.org/W3196005494","https://openalex.org/W2808275385"],"abstract_inverted_index":{"Image":[0],"saliency":[1,20,26,71,132,158],"analysis":[2,21],"is":[3,22,35,51,82,88,142,163],"an":[4],"important":[5,49],"research":[6],"content":[7],"in":[8,53,193],"the":[9,16,25,54,67,85,99,103,106,112,116,123,134,150,160,167,173,176,187,194,202,214],"field":[10],"of":[11,19,27,56,79,102,108,125,139,152,175,216],"computer":[12],"vision.":[13],"At":[14],"present,":[15],"main":[17],"method":[18,204],"to":[23,37,62,144,181],"measure":[24],"single":[28],"pixel":[29,171],"or":[30],"regular":[31],"image":[32,41,80,87,95,104,118,129,140],"patch.":[33],"It":[34],"easy":[36],"be":[38],"affected":[39],"by":[40,165],"texture,":[42],"noise":[43],"and":[44,47,93,111,172,208,219],"other":[45],"factors,":[46],"some":[48],"information":[50],"lost":[52],"process":[55],"segmentation,":[57],"which":[58],"makes":[59],"it":[60],"difficult":[61],"extract":[63],"salient":[64,146,161,177],"objects":[65],"from":[66],"image.":[68],"Therefore,":[69],"a":[70],"detection":[72],"algorithm":[73],"based":[74],"on":[75,131,157],"weighted":[76,135],"color":[77,100,136],"contrast":[78,101,137],"patch":[81,96,141],"proposed.":[83],"Firstly,":[84],"original":[86],"divided":[89],"into":[90],"different":[91],"size":[92],"non-overlapping":[94],"structure.":[97],"Then,":[98],"patch,":[105],"number":[107],"pixels":[109,156],"included":[110],"spatial":[113,126,153],"distance":[114,127,154,168],"between":[115,128,155,169],"two":[117],"patches":[119,130],"are":[120],"calculated.":[121],"Considering":[122],"influence":[124,151,215],"value,":[133,159],"model":[138],"used":[143],"detect":[145],"region.":[147,178],"Finally,":[148],"considering":[149],"region":[162],"enhanced":[164],"calculating":[166],"each":[170],"center":[174],"In":[179],"order":[180],"evaluate":[182],"this":[183],"algorithm,":[184],"we":[185],"use":[186],"largest":[188],"publicly":[189],"available":[190],"data":[191],"set":[192],"world":[195],"for":[196],"testing.":[197],"Experimental":[198],"results":[199],"show":[200],"that":[201],"proposed":[203],"has":[205],"better":[206],"precision":[207],"recall":[209],"rate,":[210],"can":[211],"significantly":[212],"suppress":[213],"complex":[217],"texture":[218],"noise.":[220]},"counts_by_year":[],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
