{"id":"https://openalex.org/W4286750038","doi":"https://doi.org/10.1109/icip46576.2022.9897742","title":"Deep Convolutional K-Means Clustering","display_name":"Deep Convolutional K-Means Clustering","publication_year":2022,"publication_date":"2022-10-16","ids":{"openalex":"https://openalex.org/W4286750038","doi":"https://doi.org/10.1109/icip46576.2022.9897742"},"language":"en","primary_location":{"id":"doi:10.1109/icip46576.2022.9897742","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icip46576.2022.9897742","pdf_url":null,"source":{"id":"https://openalex.org/S4363607719","display_name":"2022 IEEE International Conference on Image Processing (ICIP)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2022 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/A5004746534","display_name":"Anurag Goel","orcid":"https://orcid.org/0000-0003-2660-7540"},"institutions":[{"id":"https://openalex.org/I863896202","display_name":"Delhi Technological University","ror":"https://ror.org/01ztcvt22","country_code":"IN","type":"education","lineage":["https://openalex.org/I863896202"]},{"id":"https://openalex.org/I119939252","display_name":"Indraprastha Institute of Information Technology Delhi","ror":"https://ror.org/03vfp4g33","country_code":"IN","type":"education","lineage":["https://openalex.org/I119939252"]}],"countries":["IN"],"is_corresponding":true,"raw_author_name":"Anurag Goel","raw_affiliation_strings":["Indraprastha Institute of Information Technology,New Delhi,India","Indraprastha Institute of Information Technology, New Delhi, India","Delhi Technological University, New Delhi, India"],"affiliations":[{"raw_affiliation_string":"Indraprastha Institute of Information Technology,New Delhi,India","institution_ids":["https://openalex.org/I119939252"]},{"raw_affiliation_string":"Indraprastha Institute of Information Technology, New Delhi, India","institution_ids":["https://openalex.org/I119939252"]},{"raw_affiliation_string":"Delhi Technological University, New Delhi, India","institution_ids":["https://openalex.org/I863896202"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5020310463","display_name":"Angshul Majumdar","orcid":"https://orcid.org/0000-0002-1065-3000"},"institutions":[{"id":"https://openalex.org/I119939252","display_name":"Indraprastha Institute of Information Technology Delhi","ror":"https://ror.org/03vfp4g33","country_code":"IN","type":"education","lineage":["https://openalex.org/I119939252"]}],"countries":["IN"],"is_corresponding":false,"raw_author_name":"Angshul Majumdar","raw_affiliation_strings":["Indraprastha Institute of Information Technology,New Delhi,India","Indraprastha Institute of Information Technology, New Delhi, India"],"affiliations":[{"raw_affiliation_string":"Indraprastha Institute of Information Technology,New Delhi,India","institution_ids":["https://openalex.org/I119939252"]},{"raw_affiliation_string":"Indraprastha Institute of Information Technology, New Delhi, India","institution_ids":["https://openalex.org/I119939252"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5062777204","display_name":"\u00c9milie Chouzenoux","orcid":"https://orcid.org/0000-0003-3631-6093"},"institutions":[{"id":"https://openalex.org/I1326498283","display_name":"Institut national de recherche en informatique et en automatique","ror":"https://ror.org/02kvxyf05","country_code":"FR","type":"funder","lineage":["https://openalex.org/I1326498283"]}],"countries":["FR"],"is_corresponding":false,"raw_author_name":"Emilie Chouzenoux","raw_affiliation_strings":["Univ. Paris,CVN, Inria,Paris,France","CVN, Inria, Univ. Paris, Paris, France"],"affiliations":[{"raw_affiliation_string":"Univ. Paris,CVN, Inria,Paris,France","institution_ids":["https://openalex.org/I1326498283"]},{"raw_affiliation_string":"CVN, Inria, Univ. Paris, Paris, France","institution_ids":["https://openalex.org/I1326498283"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5057226360","display_name":"Giovanni Chierchia","orcid":"https://orcid.org/0000-0001-5899-689X"},"institutions":[{"id":"https://openalex.org/I12187552","display_name":"UniLaSalle Amiens (ESIEE-Amiens)","ror":"https://ror.org/03r13py57","country_code":"FR","type":"education","lineage":["https://openalex.org/I12187552"]}],"countries":["FR"],"is_corresponding":false,"raw_author_name":"Giovanni Chierchia","raw_affiliation_strings":["Esiee,Paris,France","Esiee, Paris, France"],"affiliations":[{"raw_affiliation_string":"Esiee,Paris,France","institution_ids":["https://openalex.org/I12187552"]},{"raw_affiliation_string":"Esiee, Paris, France","institution_ids":["https://openalex.org/I12187552"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5004746534"],"corresponding_institution_ids":["https://openalex.org/I119939252","https://openalex.org/I863896202"],"apc_list":null,"apc_paid":null,"fwci":0.5194,"has_fulltext":false,"cited_by_count":6,"citation_normalized_percentile":{"value":0.62438052,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":94,"max":99},"biblio":{"volume":null,"issue":null,"first_page":"211","last_page":"215"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10637","display_name":"Advanced Clustering Algorithms Research","score":0.9990000128746033,"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":0.9990000128746033,"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/T11512","display_name":"Anomaly Detection Techniques and Applications","score":0.9990000128746033,"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/T11775","display_name":"COVID-19 diagnosis using AI","score":0.9987999796867371,"subfield":{"id":"https://openalex.org/subfields/2741","display_name":"Radiology, Nuclear Medicine and Imaging"},"field":{"id":"https://openalex.org/fields/27","display_name":"Medicine"},"domain":{"id":"https://openalex.org/domains/4","display_name":"Health Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/cluster-analysis","display_name":"Cluster analysis","score":0.8290178775787354},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.8046151399612427},{"id":"https://openalex.org/keywords/benchmark","display_name":"Benchmark (surveying)","score":0.7191937565803528},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.6852750182151794},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.6522480845451355},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6379106044769287},{"id":"https://openalex.org/keywords/encoder","display_name":"Encoder","score":0.497348815202713},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.48600563406944275},{"id":"https://openalex.org/keywords/correlation-clustering","display_name":"Correlation clustering","score":0.43153178691864014},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.4078517556190491},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.34750229120254517}],"concepts":[{"id":"https://openalex.org/C73555534","wikidata":"https://www.wikidata.org/wiki/Q622825","display_name":"Cluster analysis","level":2,"score":0.8290178775787354},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8046151399612427},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.7191937565803528},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.6852750182151794},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.6522480845451355},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6379106044769287},{"id":"https://openalex.org/C118505674","wikidata":"https://www.wikidata.org/wiki/Q42586063","display_name":"Encoder","level":2,"score":0.497348815202713},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.48600563406944275},{"id":"https://openalex.org/C94641424","wikidata":"https://www.wikidata.org/wiki/Q5172845","display_name":"Correlation clustering","level":3,"score":0.43153178691864014},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.4078517556190491},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.34750229120254517},{"id":"https://openalex.org/C111919701","wikidata":"https://www.wikidata.org/wiki/Q9135","display_name":"Operating system","level":1,"score":0.0},{"id":"https://openalex.org/C13280743","wikidata":"https://www.wikidata.org/wiki/Q131089","display_name":"Geodesy","level":1,"score":0.0},{"id":"https://openalex.org/C205649164","wikidata":"https://www.wikidata.org/wiki/Q1071","display_name":"Geography","level":0,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/icip46576.2022.9897742","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icip46576.2022.9897742","pdf_url":null,"source":{"id":"https://openalex.org/S4363607719","display_name":"2022 IEEE International Conference on Image Processing (ICIP)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2022 IEEE International Conference on Image Processing (ICIP)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[{"id":"https://openalex.org/F4320320883","display_name":"Agence Nationale de la Recherche","ror":"https://ror.org/00rbzpz17"}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":24,"referenced_works":["https://openalex.org/W1995450389","https://openalex.org/W2002932002","https://openalex.org/W2108433027","https://openalex.org/W2158703410","https://openalex.org/W2219531456","https://openalex.org/W2405933695","https://openalex.org/W2747622561","https://openalex.org/W2765741717","https://openalex.org/W2900696134","https://openalex.org/W2953791858","https://openalex.org/W3002481680","https://openalex.org/W3008739840","https://openalex.org/W3024865103","https://openalex.org/W3043476515","https://openalex.org/W3101691277","https://openalex.org/W3106029105","https://openalex.org/W3119631648","https://openalex.org/W3159249382","https://openalex.org/W4250657332","https://openalex.org/W4256361765","https://openalex.org/W6685380521","https://openalex.org/W6688609405","https://openalex.org/W6728550200","https://openalex.org/W6732395776"],"related_works":["https://openalex.org/W2378211422","https://openalex.org/W2745001401","https://openalex.org/W4321353415","https://openalex.org/W2130974462","https://openalex.org/W4226493464","https://openalex.org/W4312417841","https://openalex.org/W3193565141","https://openalex.org/W3133861977","https://openalex.org/W3167935049","https://openalex.org/W3029198973"],"abstract_inverted_index":{"Conventional":[0],"Convolutional":[1,60],"Neural":[2],"Network":[3],"(CNN)":[4],"based":[5,9,13,64,97],"clustering":[6,17,65,98],"formulations":[7],"are":[8,118],"on":[10,100],"the":[11,16,22,80,111],"encoder-decoder":[12],"framework,":[14],"where":[15,116],"loss":[18],"is":[19,30,73,87],"incorporated":[20],"after":[21],"encoder":[23],"network.":[24,83],"The":[25,67],"problem":[26],"with":[27,93,121],"this":[28],"approach":[29],"that":[31,74,105],"it":[32],"requires":[33],"training":[34],"an":[35],"additional":[36,44,81],"decoder":[37,82],"network;":[38],"this,":[39],"in":[40,51,113],"turn,":[41],"means":[42],"learning":[43,79,96],"weights":[45],"which":[46],"can":[47],"lead":[48],"to":[49,90],"over-fitting":[50],"data":[52],"constrained":[53],"scenarios.":[54],"This":[55],"work":[56],"introduces":[57],"a":[58],"Deep":[59],"Transform":[61],"Learning":[62],"(DCTL)":[63],"framework.":[66],"advantage":[68],"of":[69],"our":[70,85,106],"proposed":[71,107],"formulation":[72,86],"we":[75],"do":[76],"not":[77],"require":[78],"Therefore":[84],"less":[88],"prone":[89],"over-fitting.":[91],"Comparison":[92],"state-of-the-art":[94],"deep":[95],"solutions":[99],"benchmark":[101],"image":[102],"datasets":[103],"shows":[104],"method":[108],"improves":[109],"over":[110],"rest":[112],"challenging":[114],"scenarios":[115],"there":[117],"many":[119],"clusters":[120],"limited":[122],"samples.":[123]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2025,"cited_by_count":3},{"year":2024,"cited_by_count":2}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}