{"id":"https://openalex.org/W1976277660","doi":"https://doi.org/10.1109/bicta.2010.5645201","title":"Self-Branching Competitive Learning for image segmentation","display_name":"Self-Branching Competitive Learning for image segmentation","publication_year":2010,"publication_date":"2010-09-01","ids":{"openalex":"https://openalex.org/W1976277660","doi":"https://doi.org/10.1109/bicta.2010.5645201","mag":"1976277660"},"language":"en","primary_location":{"id":"doi:10.1109/bicta.2010.5645201","is_oa":false,"landing_page_url":"https://doi.org/10.1109/bicta.2010.5645201","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2010 IEEE Fifth International Conference on Bio-Inspired Computing: Theories and Applications (BIC-TA)","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/A5100713382","display_name":"Tao Guan","orcid":"https://orcid.org/0000-0002-7568-0560"},"institutions":[{"id":"https://openalex.org/I192868223","display_name":"Zhengzhou University of Aeronautics","ror":"https://ror.org/01qjyzh50","country_code":"CN","type":"education","lineage":["https://openalex.org/I192868223"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Tao Guan","raw_affiliation_strings":["Department of Computer Science and Application, ZhengZhou Institute of Aeronautical Industry Management, Zhengzhou, Henan, China","Department of Computer Science and Application, Zhengzhou Institute of Aeronautic Industry Management, Henan, 450015, China"],"affiliations":[{"raw_affiliation_string":"Department of Computer Science and Application, ZhengZhou Institute of Aeronautical Industry Management, Zhengzhou, Henan, China","institution_ids":["https://openalex.org/I192868223"]},{"raw_affiliation_string":"Department of Computer Science and Application, Zhengzhou Institute of Aeronautic Industry Management, Henan, 450015, China","institution_ids":["https://openalex.org/I192868223"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5100412901","display_name":"Lin Li","orcid":"https://orcid.org/0000-0002-8014-8296"},"institutions":[{"id":"https://openalex.org/I192868223","display_name":"Zhengzhou University of Aeronautics","ror":"https://ror.org/01qjyzh50","country_code":"CN","type":"education","lineage":["https://openalex.org/I192868223"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Ling Ling Li","raw_affiliation_strings":["Department of Computer Science and Application, ZhengZhou Institute of Aeronautical Industry Management, Zhengzhou, Henan, China","Department of Computer Science and Application, Zhengzhou Institute of Aeronautic Industry Management, Henan, 450015, China"],"affiliations":[{"raw_affiliation_string":"Department of Computer Science and Application, ZhengZhou Institute of Aeronautical Industry Management, Zhengzhou, Henan, China","institution_ids":["https://openalex.org/I192868223"]},{"raw_affiliation_string":"Department of Computer Science and Application, Zhengzhou Institute of Aeronautic Industry Management, Henan, 450015, China","institution_ids":["https://openalex.org/I192868223"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5100713382"],"corresponding_institution_ids":["https://openalex.org/I192868223"],"apc_list":null,"apc_paid":null,"fwci":0.6374,"has_fulltext":false,"cited_by_count":2,"citation_normalized_percentile":{"value":0.68033283,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":94,"max":96},"biblio":{"volume":null,"issue":null,"first_page":"652","last_page":"656"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10057","display_name":"Face and Expression Recognition","score":0.9983999729156494,"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/T10057","display_name":"Face and Expression Recognition","score":0.9983999729156494,"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/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/T12676","display_name":"Machine Learning and ELM","score":0.991100013256073,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/cluster-analysis","display_name":"Cluster analysis","score":0.7551416158676147},{"id":"https://openalex.org/keywords/competitive-learning","display_name":"Competitive learning","score":0.6419085264205933},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6178918480873108},{"id":"https://openalex.org/keywords/segmentation","display_name":"Segmentation","score":0.5835086107254028},{"id":"https://openalex.org/keywords/image-segmentation","display_name":"Image segmentation","score":0.5570079684257507},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.4945497214794159},{"id":"https://openalex.org/keywords/gaussian","display_name":"Gaussian","score":0.4682038426399231},{"id":"https://openalex.org/keywords/branching","display_name":"Branching (polymer chemistry)","score":0.4387492537498474},{"id":"https://openalex.org/keywords/market-segmentation","display_name":"Market segmentation","score":0.4275937080383301},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.4208420217037201},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.328476220369339},{"id":"https://openalex.org/keywords/unsupervised-learning","display_name":"Unsupervised learning","score":0.30202627182006836}],"concepts":[{"id":"https://openalex.org/C73555534","wikidata":"https://www.wikidata.org/wiki/Q622825","display_name":"Cluster analysis","level":2,"score":0.7551416158676147},{"id":"https://openalex.org/C120822770","wikidata":"https://www.wikidata.org/wiki/Q5156355","display_name":"Competitive learning","level":3,"score":0.6419085264205933},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6178918480873108},{"id":"https://openalex.org/C89600930","wikidata":"https://www.wikidata.org/wiki/Q1423946","display_name":"Segmentation","level":2,"score":0.5835086107254028},{"id":"https://openalex.org/C124504099","wikidata":"https://www.wikidata.org/wiki/Q56933","display_name":"Image segmentation","level":3,"score":0.5570079684257507},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4945497214794159},{"id":"https://openalex.org/C163716315","wikidata":"https://www.wikidata.org/wiki/Q901177","display_name":"Gaussian","level":2,"score":0.4682038426399231},{"id":"https://openalex.org/C206175624","wikidata":"https://www.wikidata.org/wiki/Q595731","display_name":"Branching (polymer chemistry)","level":2,"score":0.4387492537498474},{"id":"https://openalex.org/C125308379","wikidata":"https://www.wikidata.org/wiki/Q363057","display_name":"Market segmentation","level":2,"score":0.4275937080383301},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.4208420217037201},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.328476220369339},{"id":"https://openalex.org/C8038995","wikidata":"https://www.wikidata.org/wiki/Q1152135","display_name":"Unsupervised learning","level":2,"score":0.30202627182006836},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0},{"id":"https://openalex.org/C162853370","wikidata":"https://www.wikidata.org/wiki/Q39809","display_name":"Marketing","level":1,"score":0.0},{"id":"https://openalex.org/C62520636","wikidata":"https://www.wikidata.org/wiki/Q944","display_name":"Quantum mechanics","level":1,"score":0.0},{"id":"https://openalex.org/C144133560","wikidata":"https://www.wikidata.org/wiki/Q4830453","display_name":"Business","level":0,"score":0.0},{"id":"https://openalex.org/C192562407","wikidata":"https://www.wikidata.org/wiki/Q228736","display_name":"Materials science","level":0,"score":0.0},{"id":"https://openalex.org/C159985019","wikidata":"https://www.wikidata.org/wiki/Q181790","display_name":"Composite material","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/bicta.2010.5645201","is_oa":false,"landing_page_url":"https://doi.org/10.1109/bicta.2010.5645201","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2010 IEEE Fifth International Conference on Bio-Inspired Computing: Theories and Applications (BIC-TA)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":21,"referenced_works":["https://openalex.org/W1598201578","https://openalex.org/W1679913846","https://openalex.org/W1968420163","https://openalex.org/W1968784568","https://openalex.org/W1987490795","https://openalex.org/W2010150441","https://openalex.org/W2059267888","https://openalex.org/W2071781501","https://openalex.org/W2072658312","https://openalex.org/W2085835513","https://openalex.org/W2113076747","https://openalex.org/W2116201135","https://openalex.org/W2131532686","https://openalex.org/W2150593711","https://openalex.org/W2155408506","https://openalex.org/W2157709461","https://openalex.org/W2166322089","https://openalex.org/W2168993425","https://openalex.org/W2799061466","https://openalex.org/W4237222446","https://openalex.org/W4244494905"],"related_works":["https://openalex.org/W2590565095","https://openalex.org/W2160366419","https://openalex.org/W2149471286","https://openalex.org/W2055870375","https://openalex.org/W2011664814","https://openalex.org/W2910555889","https://openalex.org/W2096837800","https://openalex.org/W2112751893","https://openalex.org/W2027409595","https://openalex.org/W2615424382"],"abstract_inverted_index":{"This":[0],"paper":[1],"proposes":[2],"an":[3],"online":[4,69],"competitive":[5],"learning":[6,26],"paradigm,":[7],"Self-Branching":[8],"Competitive":[9],"Learning(SBCL),":[10],"which":[11],"uses":[12],"K-Nearest":[13],"Neighborhood(KNN)":[14],"and":[15,35,64,85],"iterative":[16],"variance":[17],"estimation":[18],"for":[19],"clustering":[20,29,79],"analysis.":[21],"SBCL":[22,49,74,94],"adopts":[23],"the":[24,39,46,60,68],"incremental":[25],"strategy,":[27],"starts":[28],"data":[30,57,80],"from":[31],"one":[32],"initial":[33,53],"prototype":[34],"then":[36],"branches":[37],"if":[38],"bias":[40],"between":[41],"vectors":[42],"is":[43,50],"larger":[44],"than":[45],"pre-specified":[47],"scale.":[48],"unrelated":[51],"to":[52,66,75],"cluster":[54],"number":[55],"or":[56],"distribution,":[58],"avoids":[59],"dead":[61],"node":[62],"problem":[63],"suits":[65],"analyze":[67],"input":[70],"data.":[71],"We":[72],"apply":[73],"two":[76],"classical":[77],"problems:":[78],"with":[81],"mixed":[82],"Gaussian":[83],"distributions":[84],"segmenting":[86],"MRI":[87],"images.":[88],"The":[89],"experimental":[90],"results":[91],"shew":[92],"that":[93],"has":[95],"good":[96],"performance":[97],"in":[98],"these":[99],"problems.":[100]},"counts_by_year":[{"year":2012,"cited_by_count":2}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}