{"id":"https://openalex.org/W2113812158","doi":"https://doi.org/10.1109/lsp.2009.2023939","title":"Heteroscedastic Feature Extraction for Texture Classification","display_name":"Heteroscedastic Feature Extraction for Texture Classification","publication_year":2009,"publication_date":"2009-05-27","ids":{"openalex":"https://openalex.org/W2113812158","doi":"https://doi.org/10.1109/lsp.2009.2023939","mag":"2113812158"},"language":"en","primary_location":{"id":"doi:10.1109/lsp.2009.2023939","is_oa":false,"landing_page_url":"https://doi.org/10.1109/lsp.2009.2023939","pdf_url":null,"source":{"id":"https://openalex.org/S120629676","display_name":"IEEE Signal Processing Letters","issn_l":"1070-9908","issn":["1070-9908","1558-2361"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Signal Processing Letters","raw_type":"journal-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/A5029771864","display_name":"Wenming Zheng","orcid":"https://orcid.org/0000-0002-7764-5179"},"institutions":[{"id":"https://openalex.org/I76569877","display_name":"Southeast University","ror":"https://ror.org/04ct4d772","country_code":"CN","type":"education","lineage":["https://openalex.org/I76569877"]},{"id":"https://openalex.org/I1327237609","display_name":"Ministry of Education of the People's Republic of China","ror":"https://ror.org/01mv9t934","country_code":"CN","type":"funder","lineage":["https://openalex.org/I1327237609","https://openalex.org/I4210127390"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Wenming Zheng","raw_affiliation_strings":["Key Laboratory of Child Development and Learning Science, Ministry of Education, Research Center for Learning Science, South-East University, Nanjing, Jiangsu, China","Key Lab. of Child Dev. & Learning Sci., Southeast Univ., Nanjing, China"],"affiliations":[{"raw_affiliation_string":"Key Laboratory of Child Development and Learning Science, Ministry of Education, Research Center for Learning Science, South-East University, Nanjing, Jiangsu, China","institution_ids":["https://openalex.org/I1327237609"]},{"raw_affiliation_string":"Key Lab. of Child Dev. & Learning Sci., Southeast Univ., Nanjing, China","institution_ids":["https://openalex.org/I76569877"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":["https://openalex.org/A5029771864"],"corresponding_institution_ids":["https://openalex.org/I1327237609","https://openalex.org/I76569877"],"apc_list":null,"apc_paid":null,"fwci":1.9414,"has_fulltext":false,"cited_by_count":11,"citation_normalized_percentile":{"value":0.87853983,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":96},"biblio":{"volume":"16","issue":"9","first_page":"766","last_page":"769"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10057","display_name":"Face and Expression Recognition","score":0.998199999332428,"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":0.998199999332428,"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/T10824","display_name":"Image Retrieval and Classification Techniques","score":0.9951000213623047,"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/T10640","display_name":"Spectroscopy and Chemometric Analyses","score":0.9915000200271606,"subfield":{"id":"https://openalex.org/subfields/1602","display_name":"Analytical Chemistry"},"field":{"id":"https://openalex.org/fields/16","display_name":"Chemistry"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/linear-discriminant-analysis","display_name":"Linear discriminant analysis","score":0.9005817174911499},{"id":"https://openalex.org/keywords/optimal-discriminant-analysis","display_name":"Optimal discriminant analysis","score":0.8346260786056519},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.7859532833099365},{"id":"https://openalex.org/keywords/kernel-fisher-discriminant-analysis","display_name":"Kernel Fisher discriminant analysis","score":0.7801337242126465},{"id":"https://openalex.org/keywords/heteroscedasticity","display_name":"Heteroscedasticity","score":0.6514444947242737},{"id":"https://openalex.org/keywords/discriminant","display_name":"Discriminant","score":0.6504956483840942},{"id":"https://openalex.org/keywords/multiple-discriminant-analysis","display_name":"Multiple discriminant analysis","score":0.6395874619483948},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6323559880256653},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.5823214650154114},{"id":"https://openalex.org/keywords/covariance","display_name":"Covariance","score":0.5162370204925537},{"id":"https://openalex.org/keywords/fisher-kernel","display_name":"Fisher kernel","score":0.4957205057144165},{"id":"https://openalex.org/keywords/feature-extraction","display_name":"Feature extraction","score":0.4927559196949005},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.42515090107917786},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.35482531785964966},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.3202729821205139}],"concepts":[{"id":"https://openalex.org/C69738355","wikidata":"https://www.wikidata.org/wiki/Q1228929","display_name":"Linear discriminant analysis","level":2,"score":0.9005817174911499},{"id":"https://openalex.org/C104500394","wikidata":"https://www.wikidata.org/wiki/Q17104912","display_name":"Optimal discriminant analysis","level":3,"score":0.8346260786056519},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.7859532833099365},{"id":"https://openalex.org/C181367576","wikidata":"https://www.wikidata.org/wiki/Q6394184","display_name":"Kernel Fisher discriminant analysis","level":4,"score":0.7801337242126465},{"id":"https://openalex.org/C101104100","wikidata":"https://www.wikidata.org/wiki/Q1063540","display_name":"Heteroscedasticity","level":2,"score":0.6514444947242737},{"id":"https://openalex.org/C78397625","wikidata":"https://www.wikidata.org/wiki/Q192487","display_name":"Discriminant","level":2,"score":0.6504956483840942},{"id":"https://openalex.org/C58596280","wikidata":"https://www.wikidata.org/wiki/Q28324849","display_name":"Multiple discriminant analysis","level":3,"score":0.6395874619483948},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6323559880256653},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.5823214650154114},{"id":"https://openalex.org/C178650346","wikidata":"https://www.wikidata.org/wiki/Q201984","display_name":"Covariance","level":2,"score":0.5162370204925537},{"id":"https://openalex.org/C207798031","wikidata":"https://www.wikidata.org/wiki/Q8563425","display_name":"Fisher kernel","level":5,"score":0.4957205057144165},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.4927559196949005},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.42515090107917786},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.35482531785964966},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.3202729821205139},{"id":"https://openalex.org/C31510193","wikidata":"https://www.wikidata.org/wiki/Q1192553","display_name":"Facial recognition system","level":3,"score":0.0},{"id":"https://openalex.org/C138885662","wikidata":"https://www.wikidata.org/wiki/Q5891","display_name":"Philosophy","level":0,"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":1,"locations":[{"id":"doi:10.1109/lsp.2009.2023939","is_oa":false,"landing_page_url":"https://doi.org/10.1109/lsp.2009.2023939","pdf_url":null,"source":{"id":"https://openalex.org/S120629676","display_name":"IEEE Signal Processing Letters","issn_l":"1070-9908","issn":["1070-9908","1558-2361"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Signal Processing Letters","raw_type":"journal-article"}],"best_oa_location":null,"sustainable_development_goals":[{"score":0.7400000095367432,"display_name":"Reduced inequalities","id":"https://metadata.un.org/sdg/10"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":12,"referenced_works":["https://openalex.org/W1985809919","https://openalex.org/W1993655741","https://openalex.org/W2012352340","https://openalex.org/W2031586513","https://openalex.org/W2084134149","https://openalex.org/W2121647436","https://openalex.org/W2130418330","https://openalex.org/W2135346934","https://openalex.org/W2142975003","https://openalex.org/W2144451942","https://openalex.org/W2158272077","https://openalex.org/W2163352848"],"related_works":["https://openalex.org/W2141981133","https://openalex.org/W2365801610","https://openalex.org/W2354551251","https://openalex.org/W2386228546","https://openalex.org/W2068218029","https://openalex.org/W2162393942","https://openalex.org/W2538559652","https://openalex.org/W2025089370","https://openalex.org/W3147024994","https://openalex.org/W2321869780"],"abstract_inverted_index":{"Linear":[0],"discriminant":[1,27,33,45,78,95,114,124],"analysis":[2,125],"(LDA)":[3],"is":[4,20,42],"a":[5,23,93,118],"well-known":[6],"feature":[7],"extraction":[8],"method":[9,57,121,136],"in":[10,88],"statistical":[11],"pattern":[12],"recognition":[13],"community.":[14],"The":[15,150],"basic":[16],"idea":[17],"of":[18,25,38,66,76,98,122,161],"LDA":[19,56],"to":[21,105,145],"find":[22],"set":[24],"optimal":[26],"vectors":[28],"that":[29,43],"maximize":[30],"the":[31,39,50,55,64,67,77,81,107,112,127,147,157],"Fisher's":[32,40,108],"criterion.":[34,109],"One":[35],"major":[36],"problem":[37,69],"criterion":[41,96],"its":[44],"performance":[46,160],"largely":[47],"depends":[48],"on":[49,111,153],"class":[51,82,100],"mean":[52,101],"differences.":[53,84],"Hence,":[54],"may":[58],"not":[59,73],"work":[60],"well":[61],"as":[62],"for":[63,137],"case":[65],"heteroscedastic":[68,119],"since":[70],"it":[71],"can":[72],"make":[74],"use":[75],"information":[79],"from":[80],"covariance":[83,103],"To":[85],"this":[86,89],"end,":[87],"paper":[90],"we":[91,116],"propose":[92,117,132],"new":[94,113],"consisting":[97],"both":[99],"and":[102],"differences":[104],"replace":[106],"Based":[110],"criterion,":[115],"extension":[120],"linear":[123],"(namely":[126],"HELDA":[128,138],"method).":[129],"We":[130],"also":[131],"an":[133],"approximate":[134],"solution":[135],"(AHELDA)":[139],"via":[140],"matrices":[141],"joint":[142],"diagonalization":[143],"(JD)":[144],"reduce":[146],"computational":[148],"complexity.":[149],"extensive":[151],"experiments":[152],"texture":[154],"classification":[155,159],"confirm":[156],"better":[158],"our":[162],"method.":[163]},"counts_by_year":[{"year":2020,"cited_by_count":1},{"year":2018,"cited_by_count":2},{"year":2014,"cited_by_count":1},{"year":2013,"cited_by_count":1},{"year":2012,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}