{"id":"https://openalex.org/W4327521065","doi":"https://doi.org/10.1145/3573428.3573728","title":"Image Classification Method Based on Neural Network Feature Extraction","display_name":"Image Classification Method Based on Neural Network Feature Extraction","publication_year":2022,"publication_date":"2022-10-21","ids":{"openalex":"https://openalex.org/W4327521065","doi":"https://doi.org/10.1145/3573428.3573728"},"language":"en","primary_location":{"id":"doi:10.1145/3573428.3573728","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3573428.3573728","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 2022 6th International Conference on Electronic Information Technology and Computer Engineering","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/A5005169607","display_name":"Hanwen Zhang","orcid":"https://orcid.org/0000-0001-6983-6672"},"institutions":[{"id":"https://openalex.org/I135714990","display_name":"North University of China","ror":"https://ror.org/047bp1713","country_code":"CN","type":"education","lineage":["https://openalex.org/I135714990"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Hanwen Zhang","raw_affiliation_strings":["Software Engineering, North University of China, China"],"affiliations":[{"raw_affiliation_string":"Software Engineering, North University of China, China","institution_ids":["https://openalex.org/I135714990"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":["https://openalex.org/A5005169607"],"corresponding_institution_ids":["https://openalex.org/I135714990"],"apc_list":null,"apc_paid":null,"fwci":0.2667,"has_fulltext":false,"cited_by_count":2,"citation_normalized_percentile":{"value":0.64243812,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":95},"biblio":{"volume":null,"issue":null,"first_page":"1696","last_page":"1699"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12111","display_name":"Industrial Vision Systems and Defect Detection","score":0.9728000164031982,"subfield":{"id":"https://openalex.org/subfields/2209","display_name":"Industrial and Manufacturing Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T12111","display_name":"Industrial Vision Systems and Defect Detection","score":0.9728000164031982,"subfield":{"id":"https://openalex.org/subfields/2209","display_name":"Industrial and Manufacturing Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T10616","display_name":"Smart Agriculture and AI","score":0.9710000157356262,"subfield":{"id":"https://openalex.org/subfields/1110","display_name":"Plant Science"},"field":{"id":"https://openalex.org/fields/11","display_name":"Agricultural and Biological Sciences"},"domain":{"id":"https://openalex.org/domains/1","display_name":"Life Sciences"}},{"id":"https://openalex.org/T12702","display_name":"Brain Tumor Detection and Classification","score":0.9661999940872192,"subfield":{"id":"https://openalex.org/subfields/2808","display_name":"Neurology"},"field":{"id":"https://openalex.org/fields/28","display_name":"Neuroscience"},"domain":{"id":"https://openalex.org/domains/1","display_name":"Life Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/support-vector-machine","display_name":"Support vector machine","score":0.7903246283531189},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.788423478603363},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.7876095771789551},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.7518841028213501},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.6294924020767212},{"id":"https://openalex.org/keywords/feature-extraction","display_name":"Feature extraction","score":0.5778355002403259},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.5687047243118286},{"id":"https://openalex.org/keywords/contextual-image-classification","display_name":"Contextual image classification","score":0.4657875597476959},{"id":"https://openalex.org/keywords/feature-selection","display_name":"Feature selection","score":0.45265406370162964},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.4451373219490051},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.33985477685928345}],"concepts":[{"id":"https://openalex.org/C12267149","wikidata":"https://www.wikidata.org/wiki/Q282453","display_name":"Support vector machine","level":2,"score":0.7903246283531189},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.788423478603363},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7876095771789551},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.7518841028213501},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.6294924020767212},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.5778355002403259},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.5687047243118286},{"id":"https://openalex.org/C75294576","wikidata":"https://www.wikidata.org/wiki/Q5165192","display_name":"Contextual image classification","level":3,"score":0.4657875597476959},{"id":"https://openalex.org/C148483581","wikidata":"https://www.wikidata.org/wiki/Q446488","display_name":"Feature selection","level":2,"score":0.45265406370162964},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.4451373219490051},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.33985477685928345},{"id":"https://openalex.org/C41895202","wikidata":"https://www.wikidata.org/wiki/Q8162","display_name":"Linguistics","level":1,"score":0.0},{"id":"https://openalex.org/C138885662","wikidata":"https://www.wikidata.org/wiki/Q5891","display_name":"Philosophy","level":0,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3573428.3573728","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3573428.3573728","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 2022 6th International Conference on Electronic Information Technology and Computer Engineering","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":12,"referenced_works":["https://openalex.org/W1686810756","https://openalex.org/W1964017540","https://openalex.org/W1990298245","https://openalex.org/W2572219278","https://openalex.org/W2942818594","https://openalex.org/W2963951032","https://openalex.org/W3010398094","https://openalex.org/W3107675414","https://openalex.org/W4200056912","https://openalex.org/W4206378740","https://openalex.org/W4214940715","https://openalex.org/W4224033643"],"related_works":["https://openalex.org/W2090763504","https://openalex.org/W148178222","https://openalex.org/W2104657898","https://openalex.org/W1948992892","https://openalex.org/W1886884218","https://openalex.org/W1910826599","https://openalex.org/W2012353789","https://openalex.org/W4386564352","https://openalex.org/W2952668426","https://openalex.org/W2565656575"],"abstract_inverted_index":{"Image":[0],"classification":[1,59,133],"relies":[2],"on":[3,61],"supervised":[4],"learning,":[5],"which":[6],"employs":[7],"deep":[8],"neural":[9,79,94,127,153,169],"networks":[10],"and":[11,25,41,77,83,100,163,177],"conventional":[12],"machine":[13,70],"learning":[14,71],"techniques":[15],"to":[16,23,125],"continually":[17],"learn":[18],"the":[19,31,34,36,39,42,53,62,68,78,86,89,93,103,123,132,136,151,161,168],"characteristics":[20],"that":[21,120,160],"correlate":[22],"tags":[24],"accomplish":[26],"classification.":[27],"In":[28,56],"this":[29,57],"situation,":[30],"quantity":[32],"of":[33,38,44,135,150,165],"dataset,":[35],"quality":[37],"labels,":[40],"selection":[43],"an":[45],"appropriate":[46],"approach":[47],"frequently":[48],"significantly":[49,130],"impact":[50],"how":[51],"robust":[52],"model":[54],"is.":[55],"study,":[58],"predictions":[60],"CIFAR-10":[63],"dataset":[64,137],"are":[65,96],"made":[66],"using":[67],"traditional":[69],"algorithm":[72],"Support":[73],"Vector":[74],"Machine":[75],"(SVM)":[76],"network":[80,95,128,154,170],"models":[81,155],"LeNet5":[82],"VGG16.":[84],"At":[85],"same":[87],"time,":[88],"features":[90],"learned":[91],"by":[92],"used":[97,108],"for":[98,114,145,174],"migration":[99],"combined":[101],"with":[102],"SVM.":[104],"The":[105,116,147],"SVM":[106,124],"is":[107,142,156],"as":[109],"a":[110,126],"fully":[111],"connected":[112],"layer":[113],"prediction.":[115],"experimental":[117],"results":[118],"show":[119],"switching":[121],"from":[122],"can":[129],"refine":[131],"accuracy":[134,149],"CIFAR-10.":[138],"Still,":[139],"feature":[140],"transfer":[141],"not":[143],"helpful":[144],"optimization.":[146],"prediction":[148],"two":[152],"quite":[157],"different,":[158],"indicating":[159],"order":[162],"number":[164],"layers":[166],"in":[167],"design":[171],"perform":[172],"differently":[173],"other":[175],"datasets":[176],"tasks.":[178]},"counts_by_year":[{"year":2025,"cited_by_count":1},{"year":2024,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}