{"id":"https://openalex.org/W2593244682","doi":"https://doi.org/10.1109/icip.2017.8296550","title":"Stacking-based deep neural network: Deep analytic network on convolutional spectral histogram features","display_name":"Stacking-based deep neural network: Deep analytic network on convolutional spectral histogram features","publication_year":2017,"publication_date":"2017-09-01","ids":{"openalex":"https://openalex.org/W2593244682","doi":"https://doi.org/10.1109/icip.2017.8296550","mag":"2593244682"},"language":"en","primary_location":{"id":"doi:10.1109/icip.2017.8296550","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icip.2017.8296550","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2017 IEEE International Conference on Image Processing (ICIP)","raw_type":"proceedings-article"},"type":"conference-paper","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/A5083277795","display_name":"Cheng-Yaw Low","orcid":"https://orcid.org/0000-0002-6764-0614"},"institutions":[{"id":"https://openalex.org/I193775966","display_name":"Yonsei University","ror":"https://ror.org/01wjejq96","country_code":"KR","type":"education","lineage":["https://openalex.org/I193775966"]}],"countries":["KR"],"is_corresponding":false,"raw_author_name":"Cheng-Yaw Low","raw_affiliation_strings":["School of Electrical and Electronic Engineering, Yonsei University, South Korea"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Electrical and Electronic Engineering, Yonsei University, South Korea","institution_ids":["https://openalex.org/I193775966"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5051093782","display_name":"Andrew Beng Jin Teoh","orcid":"https://orcid.org/0000-0001-5063-9484"},"institutions":[{"id":"https://openalex.org/I193775966","display_name":"Yonsei University","ror":"https://ror.org/01wjejq96","country_code":"KR","type":"education","lineage":["https://openalex.org/I193775966"]}],"countries":["KR"],"is_corresponding":false,"raw_author_name":"Andrew Beng-Jin Teoh","raw_affiliation_strings":["School of Electrical and Electronic Engineering, Yonsei University, South Korea"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Electrical and Electronic Engineering, Yonsei University, South Korea","institution_ids":["https://openalex.org/I193775966"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I193775966"],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":12,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"1592","last_page":"1596"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10036","display_name":"Advanced Neural Network Applications","score":0.9995999932289124,"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/T10036","display_name":"Advanced Neural Network Applications","score":0.9995999932289124,"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/T12111","display_name":"Industrial Vision Systems and Defect Detection","score":0.9991000294685364,"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/T10057","display_name":"Face and Expression Recognition","score":0.9988999962806702,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.7187035083770752},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7132266759872437},{"id":"https://openalex.org/keywords/mnist-database","display_name":"MNIST database","score":0.6863199472427368},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.6651096343994141},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.6256460547447205},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.6060176491737366},{"id":"https://openalex.org/keywords/histogram","display_name":"Histogram","score":0.5950325727462769},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.519932210445404},{"id":"https://openalex.org/keywords/normalization","display_name":"Normalization (sociology)","score":0.5182892680168152},{"id":"https://openalex.org/keywords/network-architecture","display_name":"Network architecture","score":0.4572053551673889},{"id":"https://openalex.org/keywords/feedforward-neural-network","display_name":"Feedforward neural network","score":0.43456417322158813},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.1183023750782013}],"concepts":[{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7187035083770752},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7132266759872437},{"id":"https://openalex.org/C190502265","wikidata":"https://www.wikidata.org/wiki/Q17069496","display_name":"MNIST database","level":3,"score":0.6863199472427368},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.6651096343994141},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.6256460547447205},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.6060176491737366},{"id":"https://openalex.org/C53533937","wikidata":"https://www.wikidata.org/wiki/Q185020","display_name":"Histogram","level":3,"score":0.5950325727462769},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.519932210445404},{"id":"https://openalex.org/C136886441","wikidata":"https://www.wikidata.org/wiki/Q926129","display_name":"Normalization (sociology)","level":2,"score":0.5182892680168152},{"id":"https://openalex.org/C193415008","wikidata":"https://www.wikidata.org/wiki/Q639681","display_name":"Network architecture","level":2,"score":0.4572053551673889},{"id":"https://openalex.org/C47702885","wikidata":"https://www.wikidata.org/wiki/Q5441227","display_name":"Feedforward neural network","level":3,"score":0.43456417322158813},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.1183023750782013},{"id":"https://openalex.org/C19165224","wikidata":"https://www.wikidata.org/wiki/Q23404","display_name":"Anthropology","level":1,"score":0.0},{"id":"https://openalex.org/C38652104","wikidata":"https://www.wikidata.org/wiki/Q3510521","display_name":"Computer security","level":1,"score":0.0},{"id":"https://openalex.org/C144024400","wikidata":"https://www.wikidata.org/wiki/Q21201","display_name":"Sociology","level":0,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/icip.2017.8296550","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icip.2017.8296550","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2017 IEEE International Conference on Image Processing (ICIP)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/4","score":0.7699999809265137,"display_name":"Quality Education"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":41,"referenced_works":["https://openalex.org/W28412257","https://openalex.org/W60337842","https://openalex.org/W177847060","https://openalex.org/W1136074057","https://openalex.org/W1502160187","https://openalex.org/W1614900577","https://openalex.org/W1616262590","https://openalex.org/W1994935303","https://openalex.org/W2019607443","https://openalex.org/W2033419168","https://openalex.org/W2074818733","https://openalex.org/W2109779438","https://openalex.org/W2109779985","https://openalex.org/W2112796928","https://openalex.org/W2123045220","https://openalex.org/W2123131857","https://openalex.org/W2130325614","https://openalex.org/W2133319764","https://openalex.org/W2136922672","https://openalex.org/W2149600041","https://openalex.org/W2179352600","https://openalex.org/W2342578201","https://openalex.org/W2507989420","https://openalex.org/W2997574889","https://openalex.org/W3100344990","https://openalex.org/W3102431071","https://openalex.org/W3118608800","https://openalex.org/W4240294902","https://openalex.org/W4255455317","https://openalex.org/W4300537377","https://openalex.org/W6602429385","https://openalex.org/W6607232846","https://openalex.org/W6630005630","https://openalex.org/W6676338569","https://openalex.org/W6676553272","https://openalex.org/W6677995690","https://openalex.org/W6681096077","https://openalex.org/W6685315510","https://openalex.org/W6785734925","https://openalex.org/W6787972765","https://openalex.org/W6989699103"],"related_works":["https://openalex.org/W4386603768","https://openalex.org/W2950475743","https://openalex.org/W2886711096","https://openalex.org/W2750384547","https://openalex.org/W4380078352","https://openalex.org/W3046591097","https://openalex.org/W4389249638","https://openalex.org/W2733410219","https://openalex.org/W2734358244","https://openalex.org/W4388700941"],"abstract_inverted_index":{"Stacking-based":[0],"deep":[1,9,55,122],"neural":[2,10],"network":[3,11,21,57],"(S-DNN),":[4],"in":[5,14,34],"general,":[6],"denotes":[7],"a":[8,27,38,120],"(DNN)":[12],"resemblance":[13],"terms":[15],"of":[16,30,61,95],"its":[17],"very":[18],"deep,":[19],"feedforward":[20],"architecture.":[22],"The":[23,67,87,108],"typical":[24],"S-DNN":[25,52],"aggregates":[26],"variable":[28],"number":[29],"individually":[31],"learnable":[32],"modules":[33],"series":[35],"to":[36,41],"assemble":[37],"DNN-alike":[39],"alternative":[40],"the":[42,62,115],"targeted":[43],"object":[44],"recognition":[45],"tasks.":[46],"This":[47],"work":[48],"likewise":[49],"devises":[50],"an":[51],"instantiation,":[53],"dubbed":[54],"analytic":[56],"(DAN),":[58],"on":[59,72,92],"top":[60],"spectral":[63],"histogram":[64],"(SH)":[65],"features.":[66],"DAN":[68,88,113],"learning":[69],"principle":[70],"relies":[71],"ridge":[73],"regression,":[74],"and":[75,85,104],"some":[76],"key":[77],"DNN":[78],"constituents,":[79],"specifically,":[80],"rectified":[81],"linear":[82],"unit,":[83],"fine-tuning,":[84],"normalization.":[86],"aptitude":[89],"is":[90],"scrutinized":[91],"three":[93],"repositories":[94],"varying":[96],"domains,":[97],"including":[98],"FERET":[99],"(faces),":[100],"MNIST":[101],"(handwritten":[102],"digits),":[103],"CIFAR10":[105],"(natural":[106],"objects).":[107],"empirical":[109],"results":[110],"unveil":[111],"that":[112],"escalates":[114],"SH":[116],"baseline":[117],"performance":[118],"over":[119],"sufficiently":[121],"layer.":[123]},"counts_by_year":[{"year":2025,"cited_by_count":1},{"year":2024,"cited_by_count":1},{"year":2021,"cited_by_count":3},{"year":2020,"cited_by_count":1},{"year":2019,"cited_by_count":3},{"year":2018,"cited_by_count":2},{"year":2017,"cited_by_count":1}],"updated_date":"2026-07-14T23:27:15.235271","created_date":"2025-10-10T00:00:00"}