{"id":"https://openalex.org/W2137276306","doi":"https://doi.org/10.1109/cvpr.2003.1211452","title":"Learning affinity functions for image segmentation: combining patch-based and gradient-based approaches","display_name":"Learning affinity functions for image segmentation: combining patch-based and gradient-based approaches","publication_year":2003,"publication_date":"2003-12-22","ids":{"openalex":"https://openalex.org/W2137276306","doi":"https://doi.org/10.1109/cvpr.2003.1211452","mag":"2137276306"},"language":"en","primary_location":{"id":"doi:10.1109/cvpr.2003.1211452","is_oa":false,"landing_page_url":"https://doi.org/10.1109/cvpr.2003.1211452","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2003 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2003. Proceedings.","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/A5044978076","display_name":"Charless C. Fowlkes","orcid":"https://orcid.org/0000-0002-2990-1780"},"institutions":[{"id":"https://openalex.org/I95457486","display_name":"University of California, Berkeley","ror":"https://ror.org/01an7q238","country_code":"US","type":"education","lineage":["https://openalex.org/I95457486"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"C. Fowlkes","raw_affiliation_strings":["Department of Electrical Engineering and Computer Science, University of California, Berkeley, CA, USA"],"affiliations":[{"raw_affiliation_string":"Department of Electrical Engineering and Computer Science, University of California, Berkeley, CA, USA","institution_ids":["https://openalex.org/I95457486"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101961834","display_name":"David Mart\u00edn","orcid":"https://orcid.org/0000-0003-3764-5083"},"institutions":[{"id":"https://openalex.org/I95457486","display_name":"University of California, Berkeley","ror":"https://ror.org/01an7q238","country_code":"US","type":"education","lineage":["https://openalex.org/I95457486"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"D. Martin","raw_affiliation_strings":["Department of Electrical Engineering and Computer Science, University of California, Berkeley, CA, USA"],"affiliations":[{"raw_affiliation_string":"Department of Electrical Engineering and Computer Science, University of California, Berkeley, CA, USA","institution_ids":["https://openalex.org/I95457486"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5001594573","display_name":"Jitendra Malik","orcid":"https://orcid.org/0000-0003-3695-1580"},"institutions":[{"id":"https://openalex.org/I95457486","display_name":"University of California, Berkeley","ror":"https://ror.org/01an7q238","country_code":"US","type":"education","lineage":["https://openalex.org/I95457486"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"J. Malik","raw_affiliation_strings":["Department of Electrical Engineering and Computer Science, University of California, Berkeley, CA, USA"],"affiliations":[{"raw_affiliation_string":"Department of Electrical Engineering and Computer Science, University of California, Berkeley, CA, USA","institution_ids":["https://openalex.org/I95457486"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5044978076"],"corresponding_institution_ids":["https://openalex.org/I95457486"],"apc_list":null,"apc_paid":null,"fwci":4.3096,"has_fulltext":false,"cited_by_count":132,"citation_normalized_percentile":{"value":0.94750251,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":89,"max":99},"biblio":{"volume":"2","issue":null,"first_page":"II","last_page":"54"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10052","display_name":"Medical Image Segmentation Techniques","score":0.9993000030517578,"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/T10052","display_name":"Medical Image Segmentation Techniques","score":0.9993000030517578,"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":0.9976000189781189,"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":0.9973000288009644,"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":0.8216934204101562},{"id":"https://openalex.org/keywords/pixel","display_name":"Pixel","score":0.6672616004943848},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.654449462890625},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.5901457071304321},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.5887464284896851},{"id":"https://openalex.org/keywords/similarity","display_name":"Similarity (geometry)","score":0.5412201285362244},{"id":"https://openalex.org/keywords/brightness","display_name":"Brightness","score":0.5012156963348389},{"id":"https://openalex.org/keywords/image-segmentation","display_name":"Image segmentation","score":0.49712350964546204},{"id":"https://openalex.org/keywords/segmentation","display_name":"Segmentation","score":0.4926353096961975},{"id":"https://openalex.org/keywords/image-texture","display_name":"Image texture","score":0.4184541404247284},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.4047022759914398},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.261530339717865}],"concepts":[{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.8216934204101562},{"id":"https://openalex.org/C160633673","wikidata":"https://www.wikidata.org/wiki/Q355198","display_name":"Pixel","level":2,"score":0.6672616004943848},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.654449462890625},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5901457071304321},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.5887464284896851},{"id":"https://openalex.org/C103278499","wikidata":"https://www.wikidata.org/wiki/Q254465","display_name":"Similarity (geometry)","level":3,"score":0.5412201285362244},{"id":"https://openalex.org/C125245961","wikidata":"https://www.wikidata.org/wiki/Q221656","display_name":"Brightness","level":2,"score":0.5012156963348389},{"id":"https://openalex.org/C124504099","wikidata":"https://www.wikidata.org/wiki/Q56933","display_name":"Image segmentation","level":3,"score":0.49712350964546204},{"id":"https://openalex.org/C89600930","wikidata":"https://www.wikidata.org/wiki/Q1423946","display_name":"Segmentation","level":2,"score":0.4926353096961975},{"id":"https://openalex.org/C63099799","wikidata":"https://www.wikidata.org/wiki/Q17147001","display_name":"Image texture","level":4,"score":0.4184541404247284},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.4047022759914398},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.261530339717865},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0},{"id":"https://openalex.org/C120665830","wikidata":"https://www.wikidata.org/wiki/Q14620","display_name":"Optics","level":1,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1109/cvpr.2003.1211452","is_oa":false,"landing_page_url":"https://doi.org/10.1109/cvpr.2003.1211452","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2003 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2003. Proceedings.","raw_type":"proceedings-article"},{"id":"pmh:oai:CiteSeerX.psu:10.1.1.127.3686","is_oa":false,"landing_page_url":"http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.127.3686","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"http://www.cs.berkeley.edu/~fowlkes/papers/fmm-cvpr03.pdf","raw_type":"text"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":47,"referenced_works":["https://openalex.org/W1528420426","https://openalex.org/W1528775006","https://openalex.org/W1558964055","https://openalex.org/W1560055494","https://openalex.org/W1640070940","https://openalex.org/W1983024748","https://openalex.org/W2020999234","https://openalex.org/W2029265472","https://openalex.org/W2041373047","https://openalex.org/W2042030802","https://openalex.org/W2063603851","https://openalex.org/W2074870484","https://openalex.org/W2078790577","https://openalex.org/W2083761303","https://openalex.org/W2093191240","https://openalex.org/W2098801352","https://openalex.org/W2105414067","https://openalex.org/W2106110775","https://openalex.org/W2109562068","https://openalex.org/W2114487471","https://openalex.org/W2115738369","https://openalex.org/W2117740473","https://openalex.org/W2121927366","https://openalex.org/W2122903255","https://openalex.org/W2128197592","https://openalex.org/W2132603077","https://openalex.org/W2136120429","https://openalex.org/W2140000690","https://openalex.org/W2141376824","https://openalex.org/W2145023731","https://openalex.org/W2149184914","https://openalex.org/W2151061498","https://openalex.org/W2160063258","https://openalex.org/W2160167256","https://openalex.org/W2160619799","https://openalex.org/W2161595292","https://openalex.org/W2164507445","https://openalex.org/W2167566599","https://openalex.org/W2171009857","https://openalex.org/W2332457645","https://openalex.org/W4254997627","https://openalex.org/W6629369048","https://openalex.org/W6631868196","https://openalex.org/W6636661408","https://openalex.org/W6677945368","https://openalex.org/W6685061373","https://openalex.org/W6836242169"],"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/W2799097543"],"abstract_inverted_index":{"The":[0,135],"paper":[1],"studies":[2],"the":[3,45,56,73,92,102,117,125,139,142,145,160,182,186,204,208,221,224,236,243,248,255,259],"problem":[4],"of":[5,20,34,75,94,101,119,129,141,165,207,223,258],"combining":[6],"region":[7],"and":[8,61,104,110,127,144,168,174,214],"boundary":[9],"cues":[10,52,67],"for":[11,72,107,180],"natural":[12],"image":[13,64],"segmentation.":[14],"We":[15,89,113,177,239],"employ":[16],"a":[17,79,83],"large":[18,80],"database":[19],"manually":[21],"segmented":[22],"images":[23],"in":[24,58,171],"order":[25],"to":[26,43,97],"learn":[27],"an":[28,76,150],"optimal":[29],"affinity":[30],"function":[31,148],"between":[32,63,138],"pairs":[33],"pixels.":[35,88],"These":[36],"pairwise":[37],"affinities":[38],"can":[39],"then":[40,114],"be":[41],"used":[42],"cluster":[44],"pixels":[46,226],"into":[47],"visually":[48],"coherent":[49],"groups.":[50],"Region":[51],"are":[53,68,252],"computed":[54],"as":[55],"similarity":[57,194,237],"brightness,":[59,108,166,181],"color,":[60,109,167],"texture":[62,111,169],"patches.":[65],"Boundary":[66],"incorporated":[69],"by":[70,123,235,247,254],"looking":[71],"presence":[74],"\"intervening":[77],"contour\",":[78],"gradient":[81,105,175,183],"along":[82],"straight":[84],"line":[85],"connecting":[86],"two":[87,225],"first":[90],"use":[91,164],"dataset":[93],"human":[95],"segmentations":[96],"individually":[98],"optimize":[99],"parameters":[100],"patch":[103,173,187,193],"features":[106],"cues.":[112,238],"quantitatively":[115],"measure":[116],"power":[118],"different":[120],"feature":[121],"combinations":[122],"computing":[124],"precision":[126],"recall":[128],"classifiers":[130,143],"trained":[131],"using":[132,191,199],"those":[133],"features.":[134],"mutual":[136],"information":[137,231],"output":[140],"same-segment":[146],"indicator":[147],"provides":[149],"alternative":[151],"evaluation":[152],"technique":[153],"that":[154,179,233,242],"yields":[155,195],"identical":[156],"conclusions.":[157],"As":[158],"expected,":[159],"best":[161],"classifier":[162],"makes":[163],"features,":[170],"both":[172,212],"forms.":[176],"find":[178,241],"cue":[184],"outperforms":[185],"similarity.":[188],"In":[189],"contrast,":[190],"color":[192,200],"better":[196],"results":[197],"than":[198],"gradients.":[201],"Texture":[202],"is":[203],"most":[205],"powerful":[206],"three":[209],"channels,":[210],"with":[211],"patches":[213],"gradients":[215],"carrying":[216],"significant":[217],"independent":[218],"information.":[219],"Interestingly,":[220],"proximity":[222],"does":[227],"not":[228],"add":[229],"any":[230],"beyond":[232],"provided":[234],"also":[240],"convexity":[244],"assumptions":[245],"made":[246],"intervening":[249],"contour":[250],"approach":[251],"supported":[253],"ecological":[256],"statistics":[257],"dataset.":[260]},"counts_by_year":[{"year":2025,"cited_by_count":3},{"year":2024,"cited_by_count":2},{"year":2023,"cited_by_count":2},{"year":2022,"cited_by_count":2},{"year":2021,"cited_by_count":3},{"year":2020,"cited_by_count":1},{"year":2019,"cited_by_count":2},{"year":2018,"cited_by_count":1},{"year":2017,"cited_by_count":5},{"year":2016,"cited_by_count":4},{"year":2015,"cited_by_count":11},{"year":2014,"cited_by_count":5},{"year":2013,"cited_by_count":7},{"year":2012,"cited_by_count":7}],"updated_date":"2026-04-05T17:49:38.594831","created_date":"2025-10-10T00:00:00"}