{"id":"https://openalex.org/W1967125100","doi":"https://doi.org/10.1145/1873951.1874160","title":"Improved saliency detection based on superpixel clustering and saliency propagation","display_name":"Improved saliency detection based on superpixel clustering and saliency propagation","publication_year":2010,"publication_date":"2010-10-25","ids":{"openalex":"https://openalex.org/W1967125100","doi":"https://doi.org/10.1145/1873951.1874160","mag":"1967125100"},"language":"en","primary_location":{"id":"doi:10.1145/1873951.1874160","is_oa":false,"landing_page_url":"https://doi.org/10.1145/1873951.1874160","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/A5070028902","display_name":"Zhixiang Ren","orcid":"https://orcid.org/0000-0002-4104-3790"},"institutions":[{"id":"https://openalex.org/I172675005","display_name":"Nanyang Technological University","ror":"https://ror.org/02e7b5302","country_code":"SG","type":"education","lineage":["https://openalex.org/I172675005"]}],"countries":["SG"],"is_corresponding":true,"raw_author_name":"Zhixiang Ren","raw_affiliation_strings":["Nanyang Technological University, Singapore, Singapore","Nanyang Technological University, Singapore, SINGAPORE#TAB#"],"affiliations":[{"raw_affiliation_string":"Nanyang Technological University, Singapore, Singapore","institution_ids":["https://openalex.org/I172675005"]},{"raw_affiliation_string":"Nanyang Technological University, Singapore, SINGAPORE#TAB#","institution_ids":["https://openalex.org/I172675005"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5102837904","display_name":"Yiqun Hu","orcid":"https://orcid.org/0000-0001-9157-7865"},"institutions":[{"id":"https://openalex.org/I172675005","display_name":"Nanyang Technological University","ror":"https://ror.org/02e7b5302","country_code":"SG","type":"education","lineage":["https://openalex.org/I172675005"]}],"countries":["SG"],"is_corresponding":false,"raw_author_name":"Yiqun Hu","raw_affiliation_strings":["Nanyang Technological University, Singapore, Singapore","Nanyang Technological University, Singapore, SINGAPORE#TAB#"],"affiliations":[{"raw_affiliation_string":"Nanyang Technological University, Singapore, Singapore","institution_ids":["https://openalex.org/I172675005"]},{"raw_affiliation_string":"Nanyang Technological University, Singapore, SINGAPORE#TAB#","institution_ids":["https://openalex.org/I172675005"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5110091686","display_name":"Liang-Tien Chia","orcid":null},"institutions":[{"id":"https://openalex.org/I172675005","display_name":"Nanyang Technological University","ror":"https://ror.org/02e7b5302","country_code":"SG","type":"education","lineage":["https://openalex.org/I172675005"]}],"countries":["SG"],"is_corresponding":false,"raw_author_name":"Liang-Tien Chia","raw_affiliation_strings":["Nanyang Technological University, Singapore, Singapore","Nanyang Technological University, Singapore, SINGAPORE#TAB#"],"affiliations":[{"raw_affiliation_string":"Nanyang Technological University, Singapore, Singapore","institution_ids":["https://openalex.org/I172675005"]},{"raw_affiliation_string":"Nanyang Technological University, Singapore, SINGAPORE#TAB#","institution_ids":["https://openalex.org/I172675005"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5009372982","display_name":"Deepu Rajan","orcid":"https://orcid.org/0000-0001-7788-8368"},"institutions":[{"id":"https://openalex.org/I172675005","display_name":"Nanyang Technological University","ror":"https://ror.org/02e7b5302","country_code":"SG","type":"education","lineage":["https://openalex.org/I172675005"]}],"countries":["SG"],"is_corresponding":false,"raw_author_name":"Deepu Rajan","raw_affiliation_strings":["Nanyang Technological University, Singapore, Singapore","Nanyang Technological University, Singapore, SINGAPORE#TAB#"],"affiliations":[{"raw_affiliation_string":"Nanyang Technological University, Singapore, Singapore","institution_ids":["https://openalex.org/I172675005"]},{"raw_affiliation_string":"Nanyang Technological University, Singapore, SINGAPORE#TAB#","institution_ids":["https://openalex.org/I172675005"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5070028902"],"corresponding_institution_ids":["https://openalex.org/I172675005"],"apc_list":null,"apc_paid":null,"fwci":3.1871,"has_fulltext":false,"cited_by_count":42,"citation_normalized_percentile":{"value":0.92142306,"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":"1099","last_page":"1102"},"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":0.9884999990463257,"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/T11165","display_name":"Image and Video Quality Assessment","score":0.9879000186920166,"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":0.7944297790527344},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.7385013103485107},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.709859311580658},{"id":"https://openalex.org/keywords/cluster-analysis","display_name":"Cluster analysis","score":0.623751163482666},{"id":"https://openalex.org/keywords/metric","display_name":"Metric (unit)","score":0.5655750036239624},{"id":"https://openalex.org/keywords/mixture-model","display_name":"Mixture model","score":0.5211820006370544},{"id":"https://openalex.org/keywords/mean-shift","display_name":"Mean-shift","score":0.5149345993995667},{"id":"https://openalex.org/keywords/object-detection","display_name":"Object detection","score":0.5006990432739258},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.45167604088783264},{"id":"https://openalex.org/keywords/image-segmentation","display_name":"Image segmentation","score":0.4302322268486023},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.4116392433643341}],"concepts":[{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7944297790527344},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.7385013103485107},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.709859311580658},{"id":"https://openalex.org/C73555534","wikidata":"https://www.wikidata.org/wiki/Q622825","display_name":"Cluster analysis","level":2,"score":0.623751163482666},{"id":"https://openalex.org/C176217482","wikidata":"https://www.wikidata.org/wiki/Q860554","display_name":"Metric (unit)","level":2,"score":0.5655750036239624},{"id":"https://openalex.org/C61224824","wikidata":"https://www.wikidata.org/wiki/Q2260434","display_name":"Mixture model","level":2,"score":0.5211820006370544},{"id":"https://openalex.org/C48548287","wikidata":"https://www.wikidata.org/wiki/Q6803557","display_name":"Mean-shift","level":3,"score":0.5149345993995667},{"id":"https://openalex.org/C2776151529","wikidata":"https://www.wikidata.org/wiki/Q3045304","display_name":"Object detection","level":3,"score":0.5006990432739258},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.45167604088783264},{"id":"https://openalex.org/C124504099","wikidata":"https://www.wikidata.org/wiki/Q56933","display_name":"Image segmentation","level":3,"score":0.4302322268486023},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.4116392433643341},{"id":"https://openalex.org/C21547014","wikidata":"https://www.wikidata.org/wiki/Q1423657","display_name":"Operations management","level":1,"score":0.0},{"id":"https://openalex.org/C162324750","wikidata":"https://www.wikidata.org/wiki/Q8134","display_name":"Economics","level":0,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1145/1873951.1874160","is_oa":false,"landing_page_url":"https://doi.org/10.1145/1873951.1874160","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:pure.atira.dk:publications/c876ddd2-6762-4a17-9744-d5418ac2beaf","is_oa":false,"landing_page_url":"https://research-repository.uwa.edu.au/en/publications/c876ddd2-6762-4a17-9744-d5418ac2beaf","pdf_url":null,"source":{"id":"https://openalex.org/S4306402523","display_name":"UWA Profiles and Research Repository (University of Western Australia)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I177877127","host_organization_name":"The University of Western Australia","host_organization_lineage":["https://openalex.org/I177877127"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"Ren, Z, Hu, Y, Chia, L-T & Rajan, D 2010, Improved Saliency Detection based on Superpixel Clustering and Saliency Propogation. in MM '10 Proceedings of the international conference on Multimedia. vol. -, Association for Computing Machinery (ACM), ACM New York, NY, USA, pp. 1099-1102, 18th ACM International Conference on Multimedia, Firenze, Italy, 25/10/09. https://doi.org/10.1145/1873951.1874160","raw_type":"info:eu-repo/semantics/publishedVersion"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":16,"referenced_works":["https://openalex.org/W1663973292","https://openalex.org/W2009086942","https://openalex.org/W2066636486","https://openalex.org/W2067191022","https://openalex.org/W2100435576","https://openalex.org/W2100470808","https://openalex.org/W2103357101","https://openalex.org/W2104125540","https://openalex.org/W2120807798","https://openalex.org/W2125647562","https://openalex.org/W2128272608","https://openalex.org/W2135957164","https://openalex.org/W2146103513","https://openalex.org/W2990138404","https://openalex.org/W6629510986","https://openalex.org/W6680437723"],"related_works":["https://openalex.org/W2023748438","https://openalex.org/W2169903804","https://openalex.org/W2352790313","https://openalex.org/W1481296176","https://openalex.org/W2545971808","https://openalex.org/W2153481672","https://openalex.org/W2094810659","https://openalex.org/W2142709933","https://openalex.org/W2376628591","https://openalex.org/W1994653991"],"abstract_inverted_index":{"Saliency":[0],"detection":[1,121],"is":[2,77,89],"useful":[3],"for":[4,26,64],"high":[5],"level":[6],"applications":[7],"such":[8],"as":[9],"adaptive":[10,33,108],"compression,":[11],"image":[12,82],"retargeting,":[13],"object":[14],"recognition,":[15],"etc.":[16],"In":[17,123],"this":[18],"paper,":[19],"we":[20],"introduce":[21],"an":[22],"effective":[23],"region-based":[24],"solution":[25,76],"saliency":[27,62,120],"detection.":[28],"We":[29],"first":[30],"use":[31],"the":[32,41,61,81,107,112,119,125],"mean":[34,109],"shift":[35,110],"algorithm":[36,115],"to":[37,50,79,91,118],"extract":[38],"superpixels":[39,52],"from":[40],"input":[42],"image,":[43],"then":[44],"apply":[45],"Gaussian":[46],"Mixture":[47],"Model":[48],"(GMM)":[49],"cluster":[51,66],"based":[53],"on":[54],"their":[55],"color":[56],"similarity,":[57],"and":[58,88,111],"finally":[59],"calculate":[60],"value":[63],"each":[65],"using":[67],"compactness":[68],"metric":[69],"together":[70],"with":[71,97],"modified":[72,113],"PageRank":[73,114],"propagation.":[74],"This":[75],"able":[78],"represent":[80],"in":[83],"a":[84],"perceptually":[85],"meaningful":[86],"way":[87],"robust":[90],"over-segmentation.":[92],"It":[93],"highlights":[94],"salient":[95],"regions":[96],"full":[98],"resolution,":[99],"well-defined":[100],"boundary.":[101],"Experimental":[102],"results":[103],"show":[104],"that":[105,129],"both":[106],"contribute":[116],"substantially":[117],"result.":[122],"addition,":[124],"ROC":[126],"analysis":[127],"demonstrates":[128],"our":[130],"approach":[131],"significantly":[132],"outperforms":[133],"five":[134],"existing":[135],"popular":[136],"methods.":[137]},"counts_by_year":[{"year":2023,"cited_by_count":2},{"year":2022,"cited_by_count":1},{"year":2021,"cited_by_count":1},{"year":2020,"cited_by_count":2},{"year":2019,"cited_by_count":4},{"year":2018,"cited_by_count":4},{"year":2017,"cited_by_count":1},{"year":2016,"cited_by_count":3},{"year":2015,"cited_by_count":9},{"year":2014,"cited_by_count":5},{"year":2013,"cited_by_count":6},{"year":2012,"cited_by_count":2}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
