{"id":"https://openalex.org/W2164658671","doi":"https://doi.org/10.1109/icme.2010.5583276","title":"Comparison of clustering approaches for summarizing large populations of images","display_name":"Comparison of clustering approaches for summarizing large populations of images","publication_year":2010,"publication_date":"2010-07-01","ids":{"openalex":"https://openalex.org/W2164658671","doi":"https://doi.org/10.1109/icme.2010.5583276","mag":"2164658671"},"language":"en","primary_location":{"id":"doi:10.1109/icme.2010.5583276","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icme.2010.5583276","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2010 IEEE International Conference on Multimedia and Expo","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/A5100992647","display_name":"Yushi Jing","orcid":null},"institutions":[{"id":"https://openalex.org/I1291425158","display_name":"Google (United States)","ror":"https://ror.org/00njsd438","country_code":"US","type":"company","lineage":["https://openalex.org/I1291425158","https://openalex.org/I4210128969"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Yushi Jing","raw_affiliation_strings":["Google, Inc., Mountain View, CA, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Google, Inc., Mountain View, CA, USA","institution_ids":["https://openalex.org/I1291425158"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5054902123","display_name":"Michele Covell","orcid":"https://orcid.org/0000-0002-7486-5491"},"institutions":[{"id":"https://openalex.org/I1291425158","display_name":"Google (United States)","ror":"https://ror.org/00njsd438","country_code":"US","type":"company","lineage":["https://openalex.org/I1291425158","https://openalex.org/I4210128969"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Michele Covell","raw_affiliation_strings":["Google, Inc., Mountain View, CA, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Google, Inc., Mountain View, CA, USA","institution_ids":["https://openalex.org/I1291425158"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5048198161","display_name":"Henry A. Rowley","orcid":null},"institutions":[{"id":"https://openalex.org/I1291425158","display_name":"Google (United States)","ror":"https://ror.org/00njsd438","country_code":"US","type":"company","lineage":["https://openalex.org/I1291425158","https://openalex.org/I4210128969"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Henry A. Rowley","raw_affiliation_strings":["Google, Inc., Mountain View, CA, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Google, Inc., Mountain View, CA, USA","institution_ids":["https://openalex.org/I1291425158"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":1.8646,"has_fulltext":false,"cited_by_count":9,"citation_normalized_percentile":{"value":0.88391144,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":96},"biblio":{"volume":"7","issue":null,"first_page":"1523","last_page":"1527"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10637","display_name":"Advanced Clustering Algorithms Research","score":0.9977999925613403,"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":0.9977999925613403,"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/T11106","display_name":"Data Management and Algorithms","score":0.9937000274658203,"subfield":{"id":"https://openalex.org/subfields/1711","display_name":"Signal Processing"},"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":0.9890999794006348,"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":0.8184059262275696},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7277281880378723},{"id":"https://openalex.org/keywords/affinity-propagation","display_name":"Affinity propagation","score":0.7167633771896362},{"id":"https://openalex.org/keywords/hierarchical-clustering","display_name":"Hierarchical clustering","score":0.5899472832679749},{"id":"https://openalex.org/keywords/context","display_name":"Context (archaeology)","score":0.5293845534324646},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.5209218859672546},{"id":"https://openalex.org/keywords/correlation-clustering","display_name":"Correlation clustering","score":0.5164332985877991},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.4715792238712311},{"id":"https://openalex.org/keywords/medoid","display_name":"Medoid","score":0.4713066518306732},{"id":"https://openalex.org/keywords/set","display_name":"Set (abstract data type)","score":0.4648417830467224},{"id":"https://openalex.org/keywords/brown-clustering","display_name":"Brown clustering","score":0.44767817854881287},{"id":"https://openalex.org/keywords/filter","display_name":"Filter (signal processing)","score":0.4204727113246918},{"id":"https://openalex.org/keywords/selection","display_name":"Selection (genetic algorithm)","score":0.41781607270240784},{"id":"https://openalex.org/keywords/cure-data-clustering-algorithm","display_name":"CURE data clustering algorithm","score":0.3820689618587494},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.3809429109096527},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.3767409026622772},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.10243991017341614},{"id":"https://openalex.org/keywords/geography","display_name":"Geography","score":0.07409071922302246}],"concepts":[{"id":"https://openalex.org/C73555534","wikidata":"https://www.wikidata.org/wiki/Q622825","display_name":"Cluster analysis","level":2,"score":0.8184059262275696},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7277281880378723},{"id":"https://openalex.org/C109659709","wikidata":"https://www.wikidata.org/wiki/Q3407504","display_name":"Affinity propagation","level":5,"score":0.7167633771896362},{"id":"https://openalex.org/C92835128","wikidata":"https://www.wikidata.org/wiki/Q1277447","display_name":"Hierarchical clustering","level":3,"score":0.5899472832679749},{"id":"https://openalex.org/C2779343474","wikidata":"https://www.wikidata.org/wiki/Q3109175","display_name":"Context (archaeology)","level":2,"score":0.5293845534324646},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.5209218859672546},{"id":"https://openalex.org/C94641424","wikidata":"https://www.wikidata.org/wiki/Q5172845","display_name":"Correlation clustering","level":3,"score":0.5164332985877991},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4715792238712311},{"id":"https://openalex.org/C63085389","wikidata":"https://www.wikidata.org/wiki/Q4287912","display_name":"Medoid","level":3,"score":0.4713066518306732},{"id":"https://openalex.org/C177264268","wikidata":"https://www.wikidata.org/wiki/Q1514741","display_name":"Set (abstract data type)","level":2,"score":0.4648417830467224},{"id":"https://openalex.org/C167984511","wikidata":"https://www.wikidata.org/wiki/Q17003931","display_name":"Brown clustering","level":5,"score":0.44767817854881287},{"id":"https://openalex.org/C106131492","wikidata":"https://www.wikidata.org/wiki/Q3072260","display_name":"Filter (signal processing)","level":2,"score":0.4204727113246918},{"id":"https://openalex.org/C81917197","wikidata":"https://www.wikidata.org/wiki/Q628760","display_name":"Selection (genetic algorithm)","level":2,"score":0.41781607270240784},{"id":"https://openalex.org/C33704608","wikidata":"https://www.wikidata.org/wiki/Q5014717","display_name":"CURE data clustering algorithm","level":4,"score":0.3820689618587494},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.3809429109096527},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3767409026622772},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.10243991017341614},{"id":"https://openalex.org/C205649164","wikidata":"https://www.wikidata.org/wiki/Q1071","display_name":"Geography","level":0,"score":0.07409071922302246},{"id":"https://openalex.org/C199360897","wikidata":"https://www.wikidata.org/wiki/Q9143","display_name":"Programming language","level":1,"score":0.0},{"id":"https://openalex.org/C166957645","wikidata":"https://www.wikidata.org/wiki/Q23498","display_name":"Archaeology","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/icme.2010.5583276","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icme.2010.5583276","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2010 IEEE International Conference on Multimedia and Expo","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":13,"referenced_works":["https://openalex.org/W1505865270","https://openalex.org/W1565746575","https://openalex.org/W1578226009","https://openalex.org/W1766432634","https://openalex.org/W2000527114","https://openalex.org/W2085751730","https://openalex.org/W2118509786","https://openalex.org/W2130623086","https://openalex.org/W2141111734","https://openalex.org/W2163334428","https://openalex.org/W2999729612","https://openalex.org/W6633774736","https://openalex.org/W6634576794"],"related_works":["https://openalex.org/W2358586643","https://openalex.org/W2088480663","https://openalex.org/W1975195692","https://openalex.org/W2567087402","https://openalex.org/W2245611357","https://openalex.org/W2794209582","https://openalex.org/W2411038988","https://openalex.org/W1528194447","https://openalex.org/W3216631516","https://openalex.org/W2160785859"],"abstract_inverted_index":{"This":[0,95],"paper":[1],"compares":[2],"the":[3,21,43,49,70,121,135],"efficacy":[4],"and":[5,41,113],"efficiency":[6],"of":[7,15,23,126],"different":[8],"clustering":[9,47,93],"approaches":[10,30,104],"for":[11,53],"selecting":[12,66],"a":[13,24,86],"set":[14],"exemplar":[16],"images,":[17,40],"to":[18,48,99,105],"present":[19],"in":[20,65,134],"context":[22],"semantic":[25],"concept.":[26],"We":[27,79],"evaluate":[28],"these":[29,82,144],"using":[31,85],"900":[32],"diverse":[33],"queries,":[34],"each":[35],"associated":[36],"with":[37],"1000":[38],"web":[39],"comparing":[42],"exemplars":[44,67],"chosen":[45],"by":[46,131],"top":[50,71],"20":[51],"images":[52,73],"that":[54,60,68],"search":[55,72],"term.":[56],"Our":[57],"results":[58,84,127],"suggest":[59],"Affinity":[61,132,151],"Propagation":[62,133],"is":[63,141],"effective":[64],"match":[69],"but":[74],"at":[75],"high":[76],"computational":[77,139],"cost.":[78],"improve":[80],"on":[81,91],"early":[83],"simple":[87],"distribution-based":[88],"selection":[89],"filter":[90],"incomplete":[92],"results.":[94],"improvement":[96],"allows":[97],"us":[98],"use":[100],"more":[101],"computationally":[102],"efficient":[103],"clustering,":[106],"such":[107],"as":[108,128],"Hierarchical":[109],"Agglomerative":[110],"Clustering":[111],"(HAC)":[112],"Partitioning":[114],"Around":[115],"Medoids":[116],"(PAM),":[117],"while":[118],"still":[119],"reaching":[120],"same":[122],"(or":[123],"better)":[124],"quality":[125],"were":[129],"given":[130],"original":[136],"study.":[137],"The":[138],"savings":[140],"significant":[142],"since":[143],"alternatives":[145],"are":[146],"7-27":[147],"times":[148],"faster":[149],"than":[150],"Propagation.":[152]},"counts_by_year":[{"year":2022,"cited_by_count":1},{"year":2020,"cited_by_count":1},{"year":2015,"cited_by_count":1},{"year":2014,"cited_by_count":2},{"year":2012,"cited_by_count":2}],"updated_date":"2026-06-11T09:08:48.828518","created_date":"2025-10-10T00:00:00"}