{"id":"https://openalex.org/W1937315027","doi":"https://doi.org/10.1109/cvpr.2015.7298973","title":"Fusion moves for correlation clustering","display_name":"Fusion moves for correlation clustering","publication_year":2015,"publication_date":"2015-06-01","ids":{"openalex":"https://openalex.org/W1937315027","doi":"https://doi.org/10.1109/cvpr.2015.7298973","mag":"1937315027"},"language":"en","primary_location":{"id":"doi:10.1109/cvpr.2015.7298973","is_oa":false,"landing_page_url":"https://doi.org/10.1109/cvpr.2015.7298973","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)","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/A5054977360","display_name":"Thorsten Beier","orcid":null},"institutions":[{"id":"https://openalex.org/I171246369","display_name":"\u00c9cole Nationale Sup\u00e9rieure de M\u00e9canique et d'A\u00e9rotechnique","ror":"https://ror.org/04jx68594","country_code":"FR","type":"facility","lineage":["https://openalex.org/I171246369"]},{"id":"https://openalex.org/I2802931824","display_name":"Institut Pprime","ror":"https://ror.org/05vjdsn22","country_code":"FR","type":"facility","lineage":["https://openalex.org/I1294671590","https://openalex.org/I1294671590","https://openalex.org/I171246369","https://openalex.org/I2802931824","https://openalex.org/I32881790","https://openalex.org/I4210095849"]}],"countries":["FR"],"is_corresponding":false,"raw_author_name":"Thorsten Beier","raw_affiliation_strings":["LIAS/ISAE-ENSMA, Futuroscope, Poitiers, France","[LIAS/ISAE-ENSMA, Futuroscope, Poitiers, France]"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"LIAS/ISAE-ENSMA, Futuroscope, Poitiers, France","institution_ids":["https://openalex.org/I171246369","https://openalex.org/I2802931824"]},{"raw_affiliation_string":"[LIAS/ISAE-ENSMA, Futuroscope, Poitiers, France]","institution_ids":["https://openalex.org/I171246369","https://openalex.org/I2802931824"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5020048224","display_name":"Fred A. Hamprecht","orcid":"https://orcid.org/0000-0003-4148-5043"},"institutions":[{"id":"https://openalex.org/I171246369","display_name":"\u00c9cole Nationale Sup\u00e9rieure de M\u00e9canique et d'A\u00e9rotechnique","ror":"https://ror.org/04jx68594","country_code":"FR","type":"facility","lineage":["https://openalex.org/I171246369"]},{"id":"https://openalex.org/I2802931824","display_name":"Institut Pprime","ror":"https://ror.org/05vjdsn22","country_code":"FR","type":"facility","lineage":["https://openalex.org/I1294671590","https://openalex.org/I1294671590","https://openalex.org/I171246369","https://openalex.org/I2802931824","https://openalex.org/I32881790","https://openalex.org/I4210095849"]}],"countries":["FR"],"is_corresponding":false,"raw_author_name":"Fred A. Hamprecht","raw_affiliation_strings":["LIAS/ISAE-ENSMA, Futuroscope, Poitiers, France","[LIAS/ISAE-ENSMA, Futuroscope, Poitiers, France]"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"LIAS/ISAE-ENSMA, Futuroscope, Poitiers, France","institution_ids":["https://openalex.org/I171246369","https://openalex.org/I2802931824"]},{"raw_affiliation_string":"[LIAS/ISAE-ENSMA, Futuroscope, Poitiers, France]","institution_ids":["https://openalex.org/I171246369","https://openalex.org/I2802931824"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5002861126","display_name":"J\u00f6rg Hendrik Kappes","orcid":null},"institutions":[{"id":"https://openalex.org/I223822909","display_name":"Heidelberg University","ror":"https://ror.org/038t36y30","country_code":"DE","type":"education","lineage":["https://openalex.org/I223822909"]}],"countries":["DE"],"is_corresponding":false,"raw_author_name":"Jorg H. Kappes","raw_affiliation_strings":["Heidelberg University"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Heidelberg University","institution_ids":["https://openalex.org/I223822909"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":3,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":5.3498,"has_fulltext":false,"cited_by_count":46,"citation_normalized_percentile":{"value":0.96679938,"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":"3507","last_page":"3516"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10637","display_name":"Advanced Clustering Algorithms Research","score":0.9916999936103821,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"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/T10637","display_name":"Advanced Clustering Algorithms Research","score":0.9916999936103821,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"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/T10689","display_name":"Remote-Sensing Image Classification","score":0.991599977016449,"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/T10627","display_name":"Advanced Image and Video Retrieval Techniques","score":0.9814000129699707,"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/cluster-analysis","display_name":"Cluster analysis","score":0.8367246389389038},{"id":"https://openalex.org/keywords/correlation","display_name":"Correlation","score":0.6183621287345886},{"id":"https://openalex.org/keywords/enhanced-data-rates-for-gsm-evolution","display_name":"Enhanced Data Rates for GSM Evolution","score":0.6171988248825073},{"id":"https://openalex.org/keywords/scalability","display_name":"Scalability","score":0.6118746399879456},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.5809116959571838},{"id":"https://openalex.org/keywords/graph-partition","display_name":"Graph partition","score":0.5479417443275452},{"id":"https://openalex.org/keywords/fusion","display_name":"Fusion","score":0.5320252776145935},{"id":"https://openalex.org/keywords/correlation-clustering","display_name":"Correlation clustering","score":0.529453456401825},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.48910051584243774},{"id":"https://openalex.org/keywords/image-segmentation","display_name":"Image segmentation","score":0.48502177000045776},{"id":"https://openalex.org/keywords/segmentation","display_name":"Segmentation","score":0.484928160905838},{"id":"https://openalex.org/keywords/graph","display_name":"Graph","score":0.4579964280128479},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.41363441944122314},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.36957859992980957},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.3649057447910309},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.31731948256492615},{"id":"https://openalex.org/keywords/theoretical-computer-science","display_name":"Theoretical computer science","score":0.24272343516349792},{"id":"https://openalex.org/keywords/geometry","display_name":"Geometry","score":0.06478357315063477}],"concepts":[{"id":"https://openalex.org/C73555534","wikidata":"https://www.wikidata.org/wiki/Q622825","display_name":"Cluster analysis","level":2,"score":0.8367246389389038},{"id":"https://openalex.org/C117220453","wikidata":"https://www.wikidata.org/wiki/Q5172842","display_name":"Correlation","level":2,"score":0.6183621287345886},{"id":"https://openalex.org/C162307627","wikidata":"https://www.wikidata.org/wiki/Q204833","display_name":"Enhanced Data Rates for GSM Evolution","level":2,"score":0.6171988248825073},{"id":"https://openalex.org/C48044578","wikidata":"https://www.wikidata.org/wiki/Q727490","display_name":"Scalability","level":2,"score":0.6118746399879456},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5809116959571838},{"id":"https://openalex.org/C48903430","wikidata":"https://www.wikidata.org/wiki/Q491370","display_name":"Graph partition","level":3,"score":0.5479417443275452},{"id":"https://openalex.org/C158525013","wikidata":"https://www.wikidata.org/wiki/Q2593739","display_name":"Fusion","level":2,"score":0.5320252776145935},{"id":"https://openalex.org/C94641424","wikidata":"https://www.wikidata.org/wiki/Q5172845","display_name":"Correlation clustering","level":3,"score":0.529453456401825},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.48910051584243774},{"id":"https://openalex.org/C124504099","wikidata":"https://www.wikidata.org/wiki/Q56933","display_name":"Image segmentation","level":3,"score":0.48502177000045776},{"id":"https://openalex.org/C89600930","wikidata":"https://www.wikidata.org/wiki/Q1423946","display_name":"Segmentation","level":2,"score":0.484928160905838},{"id":"https://openalex.org/C132525143","wikidata":"https://www.wikidata.org/wiki/Q141488","display_name":"Graph","level":2,"score":0.4579964280128479},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.41363441944122314},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.36957859992980957},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.3649057447910309},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.31731948256492615},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.24272343516349792},{"id":"https://openalex.org/C2524010","wikidata":"https://www.wikidata.org/wiki/Q8087","display_name":"Geometry","level":1,"score":0.06478357315063477},{"id":"https://openalex.org/C138885662","wikidata":"https://www.wikidata.org/wiki/Q5891","display_name":"Philosophy","level":0,"score":0.0},{"id":"https://openalex.org/C77088390","wikidata":"https://www.wikidata.org/wiki/Q8513","display_name":"Database","level":1,"score":0.0},{"id":"https://openalex.org/C41895202","wikidata":"https://www.wikidata.org/wiki/Q8162","display_name":"Linguistics","level":1,"score":0.0}],"mesh":[],"locations_count":3,"locations":[{"id":"doi:10.1109/cvpr.2015.7298973","is_oa":false,"landing_page_url":"https://doi.org/10.1109/cvpr.2015.7298973","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)","raw_type":"proceedings-article"},{"id":"pmh:oai:CiteSeerX.psu:10.1.1.696.1247","is_oa":false,"landing_page_url":"http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.696.1247","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"http://hci.iwr.uni-heidelberg.de/publications/mip/techrep/beier_15_fusion.pdf","raw_type":"text"},{"id":"pmh:oai:CiteSeerX.psu:10.1.1.716.8498","is_oa":false,"landing_page_url":"http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.716.8498","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"http://hci.iwr.uni-heidelberg.de/Staff/jkappes/publications/cvpr-2015.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":47,"referenced_works":["https://openalex.org/W138878435","https://openalex.org/W203959742","https://openalex.org/W1207903865","https://openalex.org/W1842158843","https://openalex.org/W1971254795","https://openalex.org/W1971861370","https://openalex.org/W1981264533","https://openalex.org/W1993052292","https://openalex.org/W2033403400","https://openalex.org/W2035836267","https://openalex.org/W2048044409","https://openalex.org/W2082165536","https://openalex.org/W2091858563","https://openalex.org/W2102338614","https://openalex.org/W2111040408","https://openalex.org/W2115193009","https://openalex.org/W2115615127","https://openalex.org/W2121927366","https://openalex.org/W2129070834","https://openalex.org/W2137682961","https://openalex.org/W2141985162","https://openalex.org/W2142517301","https://openalex.org/W2146807782","https://openalex.org/W2149956050","https://openalex.org/W2158345769","https://openalex.org/W2161455936","https://openalex.org/W2164455818","https://openalex.org/W2164625277","https://openalex.org/W2171147867","https://openalex.org/W2257047011","https://openalex.org/W2284309279","https://openalex.org/W2295256067","https://openalex.org/W2295665070","https://openalex.org/W2626188615","https://openalex.org/W2949207643","https://openalex.org/W2963798309","https://openalex.org/W4244030505","https://openalex.org/W4289255343","https://openalex.org/W4297922390","https://openalex.org/W6605645989","https://openalex.org/W6636060498","https://openalex.org/W6675377224","https://openalex.org/W6676847830","https://openalex.org/W6677263849","https://openalex.org/W6681123220","https://openalex.org/W6682032907","https://openalex.org/W6695257658"],"related_works":["https://openalex.org/W2099421762","https://openalex.org/W2530546662","https://openalex.org/W2967030268","https://openalex.org/W2185253430","https://openalex.org/W4210345652","https://openalex.org/W3205103124","https://openalex.org/W2364238915","https://openalex.org/W1984333081","https://openalex.org/W2090790166","https://openalex.org/W2945063165"],"abstract_inverted_index":{"Correlation":[0],"clustering,":[1],"or":[2,16],"multicut":[3],"partitioning,":[4],"is":[5,32],"widely":[6],"used":[7],"in":[8],"image":[9,17],"segmentation":[10],"for":[11,54,64],"partitioning":[12,78,83,88],"an":[13],"undirected":[14],"graph":[15],"with":[18],"positive":[19],"and":[20,43,75,84,102],"negative":[21],"edge":[22,30],"weights":[23,31],"such":[24],"that":[25],"the":[26,65,73,82,104],"sum":[27],"of":[28],"cut":[29],"minimized.":[33],"Due":[34],"to":[35,95],"its":[36],"NP-hardness,":[37],"exact":[38],"solvers":[39,45],"do":[40],"not":[41],"scale":[42],"approximative":[44],"often":[46],"give":[47],"unsatisfactory":[48],"results.":[49],"We":[50],"investigate":[51],"scalable":[52],"methods":[53],"correlation":[55,66],"clustering.":[56],"To":[57],"this":[58],"end":[59],"we":[60],"define":[61],"fusion":[62],"moves":[63],"clustering":[67],"problem.":[68],"Our":[69],"algorithm":[70],"iteratively":[71],"fuses":[72],"current":[74],"a":[76,86,108],"proposed":[77],"which":[79],"monotonously":[80],"improves":[81],"maintains":[85],"valid":[87],"at":[89,103],"all":[90],"times.":[91],"Furthermore,":[92],"it":[93],"scales":[94],"larger":[96],"datasets,":[97],"gives":[98],"near":[99],"optimal":[100],"solutions,":[101],"same":[105],"time":[106],"shows":[107],"good":[109],"anytime":[110],"performance.":[111]},"counts_by_year":[{"year":2024,"cited_by_count":4},{"year":2023,"cited_by_count":1},{"year":2022,"cited_by_count":3},{"year":2021,"cited_by_count":4},{"year":2020,"cited_by_count":1},{"year":2019,"cited_by_count":7},{"year":2018,"cited_by_count":6},{"year":2017,"cited_by_count":8},{"year":2016,"cited_by_count":10},{"year":2015,"cited_by_count":2}],"updated_date":"2026-07-29T14:22:42.915294","created_date":"2025-10-10T00:00:00"}
