{"id":"https://openalex.org/W1997845072","doi":"https://doi.org/10.1109/icip.2014.7025903","title":"Agglomerative clustering for feature point grouping","display_name":"Agglomerative clustering for feature point grouping","publication_year":2014,"publication_date":"2014-10-01","ids":{"openalex":"https://openalex.org/W1997845072","doi":"https://doi.org/10.1109/icip.2014.7025903","mag":"1997845072"},"language":"en","primary_location":{"id":"doi:10.1109/icip.2014.7025903","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icip.2014.7025903","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2014 IEEE International Conference on Image Processing (ICIP)","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/A5111365876","display_name":"Maria Scalzo","orcid":null},"institutions":[{"id":"https://openalex.org/I70983195","display_name":"Syracuse University","ror":"https://ror.org/025r5qe02","country_code":"US","type":"education","lineage":["https://openalex.org/I70983195"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Maria Scalzo","raw_affiliation_strings":["Dept E.E.C.S, Syracuse University, New York, USA","Syracuse University, Dept E.E.C.S, Syracuse, New York, USA"],"affiliations":[{"raw_affiliation_string":"Dept E.E.C.S, Syracuse University, New York, USA","institution_ids":["https://openalex.org/I70983195"]},{"raw_affiliation_string":"Syracuse University, Dept E.E.C.S, Syracuse, New York, USA","institution_ids":["https://openalex.org/I70983195"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5004337702","display_name":"Senem Velipasalar","orcid":"https://orcid.org/0000-0002-1430-1555"},"institutions":[{"id":"https://openalex.org/I70983195","display_name":"Syracuse University","ror":"https://ror.org/025r5qe02","country_code":"US","type":"education","lineage":["https://openalex.org/I70983195"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Senem Velipasalar","raw_affiliation_strings":["Dept E.E.C.S, Syracuse University, New York, USA","Syracuse University, Dept of E.E.C.S., Syracuse, New York, USA"],"affiliations":[{"raw_affiliation_string":"Dept E.E.C.S, Syracuse University, New York, USA","institution_ids":["https://openalex.org/I70983195"]},{"raw_affiliation_string":"Syracuse University, Dept of E.E.C.S., Syracuse, New York, USA","institution_ids":["https://openalex.org/I70983195"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5111365876"],"corresponding_institution_ids":["https://openalex.org/I70983195"],"apc_list":null,"apc_paid":null,"fwci":0.4878,"has_fulltext":false,"cited_by_count":3,"citation_normalized_percentile":{"value":0.67694058,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":94},"biblio":{"volume":"24","issue":null,"first_page":"4452","last_page":"4456"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10627","display_name":"Advanced Image and Video Retrieval Techniques","score":0.9987000226974487,"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/T10627","display_name":"Advanced Image and Video Retrieval Techniques","score":0.9987000226974487,"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/T10689","display_name":"Remote-Sensing Image Classification","score":0.9983000159263611,"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":0.9980999827384949,"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/jaccard-index","display_name":"Jaccard index","score":0.8509516716003418},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.7130832076072693},{"id":"https://openalex.org/keywords/cluster-analysis","display_name":"Cluster analysis","score":0.7108864784240723},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6038289666175842},{"id":"https://openalex.org/keywords/hierarchical-clustering","display_name":"Hierarchical clustering","score":0.5445979833602905},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.5201224088668823},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.4890781044960022},{"id":"https://openalex.org/keywords/segmentation","display_name":"Segmentation","score":0.4850956201553345},{"id":"https://openalex.org/keywords/similarity","display_name":"Similarity (geometry)","score":0.45079898834228516},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.44253674149513245},{"id":"https://openalex.org/keywords/cosine-similarity","display_name":"Cosine similarity","score":0.43624016642570496},{"id":"https://openalex.org/keywords/single-linkage-clustering","display_name":"Single-linkage clustering","score":0.433887779712677},{"id":"https://openalex.org/keywords/complete-linkage-clustering","display_name":"Complete-linkage clustering","score":0.42171159386634827},{"id":"https://openalex.org/keywords/correlation-clustering","display_name":"Correlation clustering","score":0.41948655247688293},{"id":"https://openalex.org/keywords/feature-vector","display_name":"Feature vector","score":0.4152526557445526},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.3708626627922058},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.23717918992042542},{"id":"https://openalex.org/keywords/cure-data-clustering-algorithm","display_name":"CURE data clustering algorithm","score":0.18043911457061768}],"concepts":[{"id":"https://openalex.org/C203519979","wikidata":"https://www.wikidata.org/wiki/Q865360","display_name":"Jaccard index","level":3,"score":0.8509516716003418},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.7130832076072693},{"id":"https://openalex.org/C73555534","wikidata":"https://www.wikidata.org/wiki/Q622825","display_name":"Cluster analysis","level":2,"score":0.7108864784240723},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6038289666175842},{"id":"https://openalex.org/C92835128","wikidata":"https://www.wikidata.org/wiki/Q1277447","display_name":"Hierarchical clustering","level":3,"score":0.5445979833602905},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.5201224088668823},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.4890781044960022},{"id":"https://openalex.org/C89600930","wikidata":"https://www.wikidata.org/wiki/Q1423946","display_name":"Segmentation","level":2,"score":0.4850956201553345},{"id":"https://openalex.org/C103278499","wikidata":"https://www.wikidata.org/wiki/Q254465","display_name":"Similarity (geometry)","level":3,"score":0.45079898834228516},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.44253674149513245},{"id":"https://openalex.org/C2780762811","wikidata":"https://www.wikidata.org/wiki/Q1784941","display_name":"Cosine similarity","level":3,"score":0.43624016642570496},{"id":"https://openalex.org/C22648726","wikidata":"https://www.wikidata.org/wiki/Q7523744","display_name":"Single-linkage clustering","level":5,"score":0.433887779712677},{"id":"https://openalex.org/C23822008","wikidata":"https://www.wikidata.org/wiki/Q5156437","display_name":"Complete-linkage clustering","level":5,"score":0.42171159386634827},{"id":"https://openalex.org/C94641424","wikidata":"https://www.wikidata.org/wiki/Q5172845","display_name":"Correlation clustering","level":3,"score":0.41948655247688293},{"id":"https://openalex.org/C83665646","wikidata":"https://www.wikidata.org/wiki/Q42139305","display_name":"Feature vector","level":2,"score":0.4152526557445526},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.3708626627922058},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.23717918992042542},{"id":"https://openalex.org/C33704608","wikidata":"https://www.wikidata.org/wiki/Q5014717","display_name":"CURE data clustering algorithm","level":4,"score":0.18043911457061768},{"id":"https://openalex.org/C41895202","wikidata":"https://www.wikidata.org/wiki/Q8162","display_name":"Linguistics","level":1,"score":0.0},{"id":"https://openalex.org/C138885662","wikidata":"https://www.wikidata.org/wiki/Q5891","display_name":"Philosophy","level":0,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/icip.2014.7025903","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icip.2014.7025903","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2014 IEEE International Conference on Image Processing (ICIP)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"display_name":"Reduced inequalities","id":"https://metadata.un.org/sdg/10","score":0.41999998688697815}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":13,"referenced_works":["https://openalex.org/W1519594923","https://openalex.org/W1522930108","https://openalex.org/W1974014252","https://openalex.org/W1984093092","https://openalex.org/W1987737421","https://openalex.org/W2033819227","https://openalex.org/W2113873492","https://openalex.org/W2150722677","https://openalex.org/W2162167806","https://openalex.org/W2171728525","https://openalex.org/W6631307971","https://openalex.org/W6631325901","https://openalex.org/W6685257608"],"related_works":["https://openalex.org/W4241767317","https://openalex.org/W2188840951","https://openalex.org/W2599570117","https://openalex.org/W4232307982","https://openalex.org/W4220814143","https://openalex.org/W2030895416","https://openalex.org/W2098071561","https://openalex.org/W2056194206","https://openalex.org/W3087769312","https://openalex.org/W4231226332"],"abstract_inverted_index":{"The":[0,22,99,140,166],"objective":[1],"of":[2,16,41,60,79,84,124,155,200],"this":[3,103],"paper":[4,44,104],"is":[5,24,69,134,163],"to":[6,29,48,55,92,109,149,151,171],"group":[7],"feature":[8,35,141],"points":[9,36,181],"on":[10,26,182,205],"different":[11,38,206],"planes":[12],"as":[13],"a":[14,76,82,106,114,198],"means":[15],"semantic":[17],"image":[18],"segmentation":[19],"and":[20,88,126,185],"understanding.":[21],"methodology":[23],"based":[25],"the":[27,49,89,94,111,122,128,138,159,172,175],"ability":[28],"estimate":[30],"planar":[31,86],"homographies":[32,87],"from":[33,136],"grouped":[34],"spanning":[37],"unknown":[39],"number":[40],"planes.":[42],"This":[43,116],"proposes":[45],"an":[46,70,145],"alternative":[47,146],"J-linkage":[50,68,173],"method,":[51,174],"which":[52,131],"was":[53],"shown":[54],"have":[56],"benefits":[57],"in":[58,102,121,130,187],"terms":[59],"accuracy":[61],"over":[62],"other":[63],"multiple":[64,201],"model":[65,202],"estimation":[66,203],"techniques.":[67],"agglomerative":[71,132],"clustering":[72,119,133],"technique":[73,100,177],"that":[74,154],"uses":[75,105],"set":[77,83],"representation":[78,143],"support":[80,97,112],"for":[81,113,197],"possible":[85],"Jaccard":[90,150],"measure":[91,148],"determine":[93],"distance":[95,147],"between":[96],"sets.":[98],"proposed":[101,161,176],"frequency":[107],"vector":[108,142],"represent":[110],"model.":[115],"formulation":[117],"promotes":[118],"even":[120],"presence":[123],"noise":[125],"prevents":[127],"order":[129],"performed":[135],"influencing":[137],"results.":[139],"requires":[144],"be":[152],"exercised,":[153],"cosine":[156],"similarity.":[157],"Hence,":[158],"method":[160],"here":[162],"called":[164],"C-linkage.":[165],"results":[167,186],"show":[168],"that,":[169],"compared":[170],"correctly":[178],"classifies":[179],"more":[180],"each":[183],"plane,":[184],"less":[188],"over-segmentation":[189],"while":[190],"providing":[191],"higher":[192],"Normalized":[193],"Mutual":[194],"Information":[195],"scores":[196],"range":[199],"problems":[204],"datasets.":[207]},"counts_by_year":[{"year":2018,"cited_by_count":1},{"year":2017,"cited_by_count":1},{"year":2015,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
