{"id":"https://openalex.org/W2070884193","doi":"https://doi.org/10.1109/cvpr.2010.5539888","title":"Learning full pairwise affinities for spectral segmentation","display_name":"Learning full pairwise affinities for spectral segmentation","publication_year":2010,"publication_date":"2010-06-01","ids":{"openalex":"https://openalex.org/W2070884193","doi":"https://doi.org/10.1109/cvpr.2010.5539888","mag":"2070884193"},"language":"en","primary_location":{"id":"doi:10.1109/cvpr.2010.5539888","is_oa":false,"landing_page_url":"https://doi.org/10.1109/cvpr.2010.5539888","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition","raw_type":"proceedings-article"},"type":"conference-paper","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/A5100383853","display_name":"Tae\u2010Hoon Kim","orcid":"https://orcid.org/0000-0002-3505-4899"},"institutions":[{"id":"https://openalex.org/I139264467","display_name":"Seoul National University","ror":"https://ror.org/04h9pn542","country_code":"KR","type":"education","lineage":["https://openalex.org/I139264467"]}],"countries":["KR"],"is_corresponding":false,"raw_author_name":"Tae Hoon Kim","raw_affiliation_strings":["Department of EECS, ASRI, Seoul National University, Seoul, South Korea"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of EECS, ASRI, Seoul National University, Seoul, South Korea","institution_ids":["https://openalex.org/I139264467"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5046504049","display_name":"Kyoung Mu Lee","orcid":"https://orcid.org/0000-0001-7210-1036"},"institutions":[{"id":"https://openalex.org/I139264467","display_name":"Seoul National University","ror":"https://ror.org/04h9pn542","country_code":"KR","type":"education","lineage":["https://openalex.org/I139264467"]}],"countries":["KR"],"is_corresponding":false,"raw_author_name":"Kyoung Mu Lee","raw_affiliation_strings":["Department of EECS, ASRI, Seoul National University, Seoul, South Korea"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of EECS, ASRI, Seoul National University, Seoul, South Korea","institution_ids":["https://openalex.org/I139264467"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I139264467"],"apc_list":null,"apc_paid":null,"fwci":16.2761,"has_fulltext":false,"cited_by_count":64,"citation_normalized_percentile":{"value":0.99489796,"is_in_top_1_percent":true,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":96,"max":100},"biblio":{"volume":null,"issue":null,"first_page":"2101","last_page":"2108"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10689","display_name":"Remote-Sensing Image Classification","score":0.9997000098228455,"subfield":{"id":"https://openalex.org/subfields/2214","display_name":"Media Technology"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T10689","display_name":"Remote-Sensing Image Classification","score":0.9997000098228455,"subfield":{"id":"https://openalex.org/subfields/2214","display_name":"Media Technology"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T11659","display_name":"Advanced Image Fusion Techniques","score":0.995199978351593,"subfield":{"id":"https://openalex.org/subfields/2214","display_name":"Media Technology"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T10824","display_name":"Image Retrieval and Classification Techniques","score":0.9871000051498413,"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/pairwise-comparison","display_name":"Pairwise comparison","score":0.7885327935218811},{"id":"https://openalex.org/keywords/affinities","display_name":"Affinities","score":0.7579247355461121},{"id":"https://openalex.org/keywords/segmentation","display_name":"Segmentation","score":0.6359145045280457},{"id":"https://openalex.org/keywords/pixel","display_name":"Pixel","score":0.6122639179229736},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5695576071739197},{"id":"https://openalex.org/keywords/image-segmentation","display_name":"Image segmentation","score":0.5672601461410522},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.5368130207061768},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.5362536311149597},{"id":"https://openalex.org/keywords/graph","display_name":"Graph","score":0.51315838098526},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.3904620110988617},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.38041049242019653},{"id":"https://openalex.org/keywords/theoretical-computer-science","display_name":"Theoretical computer science","score":0.2255241870880127},{"id":"https://openalex.org/keywords/chemistry","display_name":"Chemistry","score":0.07439523935317993}],"concepts":[{"id":"https://openalex.org/C184898388","wikidata":"https://www.wikidata.org/wiki/Q1435712","display_name":"Pairwise comparison","level":2,"score":0.7885327935218811},{"id":"https://openalex.org/C2780283098","wikidata":"https://www.wikidata.org/wiki/Q4688960","display_name":"Affinities","level":2,"score":0.7579247355461121},{"id":"https://openalex.org/C89600930","wikidata":"https://www.wikidata.org/wiki/Q1423946","display_name":"Segmentation","level":2,"score":0.6359145045280457},{"id":"https://openalex.org/C160633673","wikidata":"https://www.wikidata.org/wiki/Q355198","display_name":"Pixel","level":2,"score":0.6122639179229736},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5695576071739197},{"id":"https://openalex.org/C124504099","wikidata":"https://www.wikidata.org/wiki/Q56933","display_name":"Image segmentation","level":3,"score":0.5672601461410522},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.5368130207061768},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5362536311149597},{"id":"https://openalex.org/C132525143","wikidata":"https://www.wikidata.org/wiki/Q141488","display_name":"Graph","level":2,"score":0.51315838098526},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.3904620110988617},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.38041049242019653},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.2255241870880127},{"id":"https://openalex.org/C185592680","wikidata":"https://www.wikidata.org/wiki/Q2329","display_name":"Chemistry","level":0,"score":0.07439523935317993},{"id":"https://openalex.org/C71240020","wikidata":"https://www.wikidata.org/wiki/Q186011","display_name":"Stereochemistry","level":1,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1109/cvpr.2010.5539888","is_oa":false,"landing_page_url":"https://doi.org/10.1109/cvpr.2010.5539888","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition","raw_type":"proceedings-article"},{"id":"pmh:oai:CiteSeerX.psu:10.1.1.713.2751","is_oa":false,"landing_page_url":"http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.713.2751","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"http://cv.snu.ac.kr/publication/conf/2010/FNCUT_CVPR2010.pdf","raw_type":"text"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":38,"referenced_works":["https://openalex.org/W1498238238","https://openalex.org/W1528420426","https://openalex.org/W1528775006","https://openalex.org/W1606247071","https://openalex.org/W1999478155","https://openalex.org/W2063049279","https://openalex.org/W2063266501","https://openalex.org/W2066636486","https://openalex.org/W2067191022","https://openalex.org/W2097323414","https://openalex.org/W2104019579","https://openalex.org/W2104094303","https://openalex.org/W2105414067","https://openalex.org/W2105960416","https://openalex.org/W2116810533","https://openalex.org/W2116877738","https://openalex.org/W2119300483","https://openalex.org/W2121189958","https://openalex.org/W2121947440","https://openalex.org/W2122555510","https://openalex.org/W2132106992","https://openalex.org/W2135674549","https://openalex.org/W2137276306","https://openalex.org/W2141240481","https://openalex.org/W2141729166","https://openalex.org/W2151398556","https://openalex.org/W2154455818","https://openalex.org/W2155697938","https://openalex.org/W2160167256","https://openalex.org/W2546160422","https://openalex.org/W3141717690","https://openalex.org/W4252621450","https://openalex.org/W6629812418","https://openalex.org/W6631532162","https://openalex.org/W6631868196","https://openalex.org/W6636255973","https://openalex.org/W6678192408","https://openalex.org/W6682494755"],"related_works":["https://openalex.org/W4241081643","https://openalex.org/W1974414168","https://openalex.org/W1971291106","https://openalex.org/W1996889227","https://openalex.org/W2924053807","https://openalex.org/W2769641421","https://openalex.org/W2120642597","https://openalex.org/W2337206335","https://openalex.org/W2484487206","https://openalex.org/W1522196789"],"abstract_inverted_index":{"This":[0],"paper":[1],"studies":[2],"the":[3,26,32,47,64,72,82,133,138,142,149,170],"problem":[4],"of":[5,10,25,90,119,151,174],"learning":[6,40,74],"a":[7,38,55,115,152],"full":[8,134,143],"range":[9,135],"pairwise":[11,33],"affinities":[12,43,86,95],"gained":[13],"by":[14,63,130,148],"integrating":[15],"local":[16],"grouping":[17],"cues":[18],"for":[19],"spectral":[20,27,139],"segmentation.":[21],"The":[22,160],"overall":[23],"quality":[24],"segmentation":[28],"depends":[29],"mainly":[30],"on":[31,163],"pixel":[34,103],"affinities.":[35],"By":[36,70],"employing":[37],"semi-supervised":[39,73],"technique,":[41],"optimal":[42],"are":[44,96],"learnt":[45],"from":[46],"test":[48],"image":[49,167],"without":[50],"iteration.":[51],"We":[52],"first":[53],"construct":[54],"multi-layer":[56,117],"graph":[57],"with":[58,127],"pixels":[59],"and":[60,84,104,165,172],"regions,":[61],"generated":[62],"mean":[65],"shift":[66],"algorithm,":[67],"as":[68,177],"nodes.":[69],"applying":[71],"strategy":[75],"to":[76,99,179],"this":[77],"graph,":[78],"we":[79],"can":[80],"estimate":[81],"intra-":[83],"inter-layer":[85],"between":[87],"all":[88,102,112],"pairs":[89],"nodes":[91,106],"together.":[92],"These":[93],"pair-wise":[94],"then":[97],"used":[98],"simultaneously":[100],"cluster":[101],"region":[105],"into":[107],"visually":[108],"coherent":[109],"groups":[110],"across":[111],"layers":[113],"in":[114,137],"single":[116],"framework":[118],"Normalized":[120],"Cuts.":[121],"Our":[122],"algorithm":[123,176],"provides":[124],"high-quality":[125],"segmentations":[126],"object":[128],"details":[129],"directly":[131],"incorporating":[132],"connections":[136],"framework.":[140],"Since":[141],"affinity":[144],"matrix":[145],"is":[146,157],"defined":[147],"inverse":[150],"sparse":[153],"matrix,":[154],"its":[155],"eigen-decomposition":[156],"efficiently":[158],"computed.":[159],"experimental":[161],"results":[162],"Berkeley":[164],"MSRC":[166],"databases":[168],"demonstrate":[169],"relevance":[171],"accuracy":[173],"our":[175],"compared":[178],"existing":[180],"popular":[181],"methods.":[182]},"counts_by_year":[{"year":2022,"cited_by_count":3},{"year":2019,"cited_by_count":3},{"year":2017,"cited_by_count":5},{"year":2016,"cited_by_count":9},{"year":2015,"cited_by_count":6},{"year":2014,"cited_by_count":7},{"year":2013,"cited_by_count":13},{"year":2012,"cited_by_count":8}],"updated_date":"2026-07-29T14:22:42.915294","created_date":"2025-10-10T00:00:00"}
