{"id":"https://openalex.org/W4296172684","doi":"https://doi.org/10.3233/faia220261","title":"Image Entropy Equalization for Autoencoder-Based One-Class Classification","display_name":"Image Entropy Equalization for Autoencoder-Based One-Class Classification","publication_year":2022,"publication_date":"2022-09-14","ids":{"openalex":"https://openalex.org/W4296172684","doi":"https://doi.org/10.3233/faia220261"},"language":"en","primary_location":{"id":"doi:10.3233/faia220261","is_oa":false,"landing_page_url":"https://doi.org/10.3233/faia220261","pdf_url":null,"source":{"id":"https://openalex.org/S4210201731","display_name":"Frontiers in artificial intelligence and applications","issn_l":"0922-6389","issn":["0922-6389","1879-8314"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Frontiers in Artificial Intelligence and Applications","raw_type":"book-chapter"},"type":"book-chapter","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/A5027599673","display_name":"Toshitaka Hayashi","orcid":"https://orcid.org/0000-0002-7599-4404"},"institutions":[{"id":"https://openalex.org/I75092371","display_name":"University of Hradec Kr\u00e1lov\u00e9","ror":"https://ror.org/05k238v14","country_code":"CZ","type":"education","lineage":["https://openalex.org/I75092371"]}],"countries":["CZ"],"is_corresponding":true,"raw_author_name":"Toshitaka Hayashi","raw_affiliation_strings":["Faculty of Science, University of Hradec Kralove"],"affiliations":[{"raw_affiliation_string":"Faculty of Science, University of Hradec Kralove","institution_ids":["https://openalex.org/I75092371"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5046503217","display_name":"Dalibor Cimr","orcid":"https://orcid.org/0000-0003-2197-8553"},"institutions":[{"id":"https://openalex.org/I75092371","display_name":"University of Hradec Kr\u00e1lov\u00e9","ror":"https://ror.org/05k238v14","country_code":"CZ","type":"education","lineage":["https://openalex.org/I75092371"]}],"countries":["CZ"],"is_corresponding":false,"raw_author_name":"Dalibor Cimr","raw_affiliation_strings":["Faculty of Science, University of Hradec Kralove"],"affiliations":[{"raw_affiliation_string":"Faculty of Science, University of Hradec Kralove","institution_ids":["https://openalex.org/I75092371"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5021405554","display_name":"Richard Cimler","orcid":"https://orcid.org/0000-0001-6712-9894"},"institutions":[{"id":"https://openalex.org/I75092371","display_name":"University of Hradec Kr\u00e1lov\u00e9","ror":"https://ror.org/05k238v14","country_code":"CZ","type":"education","lineage":["https://openalex.org/I75092371"]}],"countries":["CZ"],"is_corresponding":false,"raw_author_name":"Richard Cimler","raw_affiliation_strings":["Faculty of Science, University of Hradec Kralove"],"affiliations":[{"raw_affiliation_string":"Faculty of Science, University of Hradec Kralove","institution_ids":["https://openalex.org/I75092371"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5027599673"],"corresponding_institution_ids":["https://openalex.org/I75092371"],"apc_list":null,"apc_paid":null,"fwci":0.1399,"has_fulltext":false,"cited_by_count":1,"citation_normalized_percentile":{"value":0.52376259,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":94},"biblio":{"volume":null,"issue":null,"first_page":null,"last_page":null},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12874","display_name":"Digital Imaging for Blood Diseases","score":0.9959999918937683,"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/T12874","display_name":"Digital Imaging for Blood Diseases","score":0.9959999918937683,"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/T11775","display_name":"COVID-19 diagnosis using AI","score":0.9858999848365784,"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"}},{"id":"https://openalex.org/T11512","display_name":"Anomaly Detection Techniques and Applications","score":0.9825999736785889,"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/mnist-database","display_name":"MNIST database","score":0.897207498550415},{"id":"https://openalex.org/keywords/autoencoder","display_name":"Autoencoder","score":0.7489952445030212},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.6990069150924683},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6913443207740784},{"id":"https://openalex.org/keywords/pixel","display_name":"Pixel","score":0.6883779764175415},{"id":"https://openalex.org/keywords/entropy","display_name":"Entropy (arrow of time)","score":0.6406101584434509},{"id":"https://openalex.org/keywords/preprocessor","display_name":"Preprocessor","score":0.6004535555839539},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.48820239305496216},{"id":"https://openalex.org/keywords/contextual-image-classification","display_name":"Contextual image classification","score":0.4808710217475891},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.4338744282722473},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.3660515546798706},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.19350802898406982},{"id":"https://openalex.org/keywords/physics","display_name":"Physics","score":0.05976918339729309}],"concepts":[{"id":"https://openalex.org/C190502265","wikidata":"https://www.wikidata.org/wiki/Q17069496","display_name":"MNIST database","level":3,"score":0.897207498550415},{"id":"https://openalex.org/C101738243","wikidata":"https://www.wikidata.org/wiki/Q786435","display_name":"Autoencoder","level":3,"score":0.7489952445030212},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.6990069150924683},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6913443207740784},{"id":"https://openalex.org/C160633673","wikidata":"https://www.wikidata.org/wiki/Q355198","display_name":"Pixel","level":2,"score":0.6883779764175415},{"id":"https://openalex.org/C106301342","wikidata":"https://www.wikidata.org/wiki/Q4117933","display_name":"Entropy (arrow of time)","level":2,"score":0.6406101584434509},{"id":"https://openalex.org/C34736171","wikidata":"https://www.wikidata.org/wiki/Q918333","display_name":"Preprocessor","level":2,"score":0.6004535555839539},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.48820239305496216},{"id":"https://openalex.org/C75294576","wikidata":"https://www.wikidata.org/wiki/Q5165192","display_name":"Contextual image classification","level":3,"score":0.4808710217475891},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.4338744282722473},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.3660515546798706},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.19350802898406982},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.05976918339729309},{"id":"https://openalex.org/C62520636","wikidata":"https://www.wikidata.org/wiki/Q944","display_name":"Quantum mechanics","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.3233/faia220261","is_oa":false,"landing_page_url":"https://doi.org/10.3233/faia220261","pdf_url":null,"source":{"id":"https://openalex.org/S4210201731","display_name":"Frontiers in artificial intelligence and applications","issn_l":"0922-6389","issn":["0922-6389","1879-8314"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Frontiers in Artificial Intelligence and Applications","raw_type":"book-chapter"}],"best_oa_location":null,"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/10","score":0.4300000071525574,"display_name":"Reduced inequalities"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":["https://openalex.org/W2912987408","https://openalex.org/W4296978181","https://openalex.org/W2937381246","https://openalex.org/W3004801820","https://openalex.org/W4230582276","https://openalex.org/W4313444753","https://openalex.org/W4281672036","https://openalex.org/W2002563186","https://openalex.org/W4387163578","https://openalex.org/W3162343139"],"abstract_inverted_index":{"Autoencoder":[0],"(AE)":[1],"is":[2,13,42,50],"a":[3,31,61,80],"common":[4],"technique":[5],"for":[6],"one-class":[7],"classification":[8],"(OCC).":[9],"Reconstruction":[10],"error":[11],"(RE)":[12],"used":[14],"to":[15,44,53],"classify":[16],"one":[17],"seen":[18,107],"class":[19],"or":[20],"other":[21],"unseen":[22],"classes.":[23],"However,":[24],"AE-based":[25],"OCC":[26],"(OCAE)":[27],"does":[28],"not":[29],"provide":[30],"high":[32,115],"AUC":[33,103],"score.":[34],"This":[35],"study":[36],"considers":[37],"the":[38,48,54,70,110],"hypothesis":[39],"that":[40],"RE":[41],"related":[43],"image":[45,55,66,75],"entropy,":[46],"and":[47,96],"OCAE":[49,91],"biased":[51],"due":[52],"entropy":[56,67,116],"differences.":[57],"Based":[58],"on":[59,117],"such":[60],"hypothesis,":[62],"this":[63],"paper":[64],"proposes":[65],"equalization":[68],"as":[69],"preprocessing":[71],"technique.":[72],"In":[73],"which,":[74],"pixels":[76],"are":[77,88],"replaced":[78],"by":[79],"defined":[81],"set":[82],"of":[83],"pixels.":[84],"Entropy":[85,100],"equalized":[86],"images":[87],"experimented":[89],"with":[90,105],"using":[92],"MNIST,":[93,95],"Fashion":[94],"CIFAR10":[97],"datasets.":[98],"Image":[99],"Equalization":[101],"improves":[102],"scores":[104],"several":[106],"classes,":[108],"where":[109],"improved":[111],"classes":[112],"have":[113],"relatively":[114],"original":[118],"images.":[119]},"counts_by_year":[{"year":2023,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}