{"id":"https://openalex.org/W7133495929","doi":"https://doi.org/10.7717/peerj-cs.3644","title":"Enhancing deep hashing with graph filters and autoencoder-based embeddings","display_name":"Enhancing deep hashing with graph filters and autoencoder-based embeddings","publication_year":2026,"publication_date":"2026-03-04","ids":{"openalex":"https://openalex.org/W7133495929","doi":"https://doi.org/10.7717/peerj-cs.3644"},"language":"en","primary_location":{"id":"doi:10.7717/peerj-cs.3644","is_oa":true,"landing_page_url":"https://doi.org/10.7717/peerj-cs.3644","pdf_url":null,"source":{"id":"https://openalex.org/S4210178049","display_name":"PeerJ Computer Science","issn_l":"2376-5992","issn":["2376-5992"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310320104","host_organization_name":"PeerJ, Inc.","host_organization_lineage":["https://openalex.org/P4310320104"],"host_organization_lineage_names":["PeerJ, Inc."],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"PeerJ Computer Science","raw_type":"journal-article"},"type":"article","indexed_in":["crossref","doaj"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://doi.org/10.7717/peerj-cs.3644","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5101690799","display_name":"Sooin Kim","orcid":"https://orcid.org/0000-0002-7003-8296"},"institutions":[{"id":"https://openalex.org/I89015989","display_name":"Dankook University","ror":"https://ror.org/058pdbn81","country_code":"KR","type":"education","lineage":["https://openalex.org/I89015989"]}],"countries":["KR"],"is_corresponding":false,"raw_author_name":"Sooin Kim","raw_affiliation_strings":["Department of AI-based Convergence, Dankook University, Yongin-si, Gyeonggi-do, Republic of South Korea"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of AI-based Convergence, Dankook University, Yongin-si, Gyeonggi-do, Republic of South Korea","institution_ids":["https://openalex.org/I89015989"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5128098469","display_name":"Kyungtae Kim","orcid":null},"institutions":[{"id":"https://openalex.org/I125956826","display_name":"Arkansas State University","ror":"https://ror.org/006pyvd89","country_code":"US","type":"education","lineage":["https://openalex.org/I125956826"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Kyungtae Kim","raw_affiliation_strings":["Department of Computer Science, Arkansas State University, Jonesboro, AR, United States"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Computer Science, Arkansas State University, Jonesboro, AR, United States","institution_ids":["https://openalex.org/I125956826"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5128118562","display_name":"Seonghyun Min","orcid":null},"institutions":[{"id":"https://openalex.org/I4210109772","display_name":"KG Chemical (South Korea)","ror":"https://ror.org/01pgkb997","country_code":"KR","type":"company","lineage":["https://openalex.org/I4210109772"]}],"countries":["KR"],"is_corresponding":false,"raw_author_name":"Seonghyun Min","raw_affiliation_strings":["Research and Development Center, AIDEEP Company Ltd., Gwacheon-si, Gyeonggi-do, Republic of South Korea"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Research and Development Center, AIDEEP Company Ltd., Gwacheon-si, Gyeonggi-do, Republic of South Korea","institution_ids":["https://openalex.org/I4210109772"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5004416582","display_name":"Wongyum Kim","orcid":"https://orcid.org/0000-0003-3022-6230"},"institutions":[{"id":"https://openalex.org/I4210109772","display_name":"KG Chemical (South Korea)","ror":"https://ror.org/01pgkb997","country_code":"KR","type":"company","lineage":["https://openalex.org/I4210109772"]}],"countries":["KR"],"is_corresponding":false,"raw_author_name":"Wongyum Kim","raw_affiliation_strings":["Research and Development Center, AIDEEP Company Ltd., Gwacheon-si, Gyeonggi-do, Republic of South Korea"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Research and Development Center, AIDEEP Company Ltd., Gwacheon-si, Gyeonggi-do, Republic of South Korea","institution_ids":["https://openalex.org/I4210109772"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5014175226","display_name":"Doosung Hwang","orcid":"https://orcid.org/0000-0003-1840-9296"},"institutions":[{"id":"https://openalex.org/I89015989","display_name":"Dankook University","ror":"https://ror.org/058pdbn81","country_code":"KR","type":"education","lineage":["https://openalex.org/I89015989"]}],"countries":["KR"],"is_corresponding":false,"raw_author_name":"Doosung Hwang","raw_affiliation_strings":["Department of Software Science, Dankook University, Yongin-si, Gyeonggi-do, South Korea"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Software Science, Dankook University, Yongin-si, Gyeonggi-do, South Korea","institution_ids":["https://openalex.org/I89015989"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5128070769","display_name":"Donghoon Kim","orcid":null},"institutions":[{"id":"https://openalex.org/I125956826","display_name":"Arkansas State University","ror":"https://ror.org/006pyvd89","country_code":"US","type":"education","lineage":["https://openalex.org/I125956826"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Donghoon Kim","raw_affiliation_strings":["Department of Computer Science, Arkansas State University, Jonesboro, AR, United States"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Computer Science, Arkansas State University, Jonesboro, AR, United States","institution_ids":["https://openalex.org/I125956826"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":3,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":{"value":1395,"currency":"USD","value_usd":1395},"apc_paid":{"value":1395,"currency":"USD","value_usd":1395},"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.20890205,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":"12","issue":null,"first_page":"e3644","last_page":"e3644"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10627","display_name":"Advanced Image and Video Retrieval Techniques","score":0.6306999921798706,"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/T10627","display_name":"Advanced Image and Video Retrieval Techniques","score":0.6306999921798706,"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/T10036","display_name":"Advanced Neural Network Applications","score":0.02969999983906746,"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":0.025699999183416367,"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/hash-function","display_name":"Hash function","score":0.6841999888420105},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.6021000146865845},{"id":"https://openalex.org/keywords/graph","display_name":"Graph","score":0.5724999904632568},{"id":"https://openalex.org/keywords/autoencoder","display_name":"Autoencoder","score":0.54830002784729},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.5317000150680542},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.4691999852657318},{"id":"https://openalex.org/keywords/transfer-of-learning","display_name":"Transfer of learning","score":0.4690000116825104},{"id":"https://openalex.org/keywords/locality-sensitive-hashing","display_name":"Locality-sensitive hashing","score":0.3952000141143799}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6934999823570251},{"id":"https://openalex.org/C99138194","wikidata":"https://www.wikidata.org/wiki/Q183427","display_name":"Hash function","level":2,"score":0.6841999888420105},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.6021000146865845},{"id":"https://openalex.org/C132525143","wikidata":"https://www.wikidata.org/wiki/Q141488","display_name":"Graph","level":2,"score":0.5724999904632568},{"id":"https://openalex.org/C101738243","wikidata":"https://www.wikidata.org/wiki/Q786435","display_name":"Autoencoder","level":3,"score":0.54830002784729},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.5317000150680542},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5078999996185303},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.4691999852657318},{"id":"https://openalex.org/C150899416","wikidata":"https://www.wikidata.org/wiki/Q1820378","display_name":"Transfer of learning","level":2,"score":0.4690000116825104},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.44209998846054077},{"id":"https://openalex.org/C74270461","wikidata":"https://www.wikidata.org/wiki/Q1625299","display_name":"Locality-sensitive hashing","level":4,"score":0.3952000141143799},{"id":"https://openalex.org/C147224247","wikidata":"https://www.wikidata.org/wiki/Q885373","display_name":"Bloom filter","level":2,"score":0.3894999921321869},{"id":"https://openalex.org/C122907437","wikidata":"https://www.wikidata.org/wiki/Q5318999","display_name":"Dynamic perfect hashing","level":5,"score":0.38019999861717224},{"id":"https://openalex.org/C106131492","wikidata":"https://www.wikidata.org/wiki/Q3072260","display_name":"Filter (signal processing)","level":2,"score":0.3610999882221222},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.33889999985694885},{"id":"https://openalex.org/C1667742","wikidata":"https://www.wikidata.org/wiki/Q10927554","display_name":"Image retrieval","level":3,"score":0.3346000015735626},{"id":"https://openalex.org/C187062812","wikidata":"https://www.wikidata.org/wiki/Q6322840","display_name":"K-independent hashing","level":5,"score":0.32109999656677246},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.31310001015663147},{"id":"https://openalex.org/C133667856","wikidata":"https://www.wikidata.org/wiki/Q5439682","display_name":"Feature hashing","level":5,"score":0.31200000643730164},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3050999939441681},{"id":"https://openalex.org/C59404180","wikidata":"https://www.wikidata.org/wiki/Q17013334","display_name":"Feature learning","level":2,"score":0.2987000048160553},{"id":"https://openalex.org/C88230418","wikidata":"https://www.wikidata.org/wiki/Q131476","display_name":"Graph theory","level":2,"score":0.2770000100135803},{"id":"https://openalex.org/C2776359362","wikidata":"https://www.wikidata.org/wiki/Q2145286","display_name":"Representation (politics)","level":3,"score":0.273499995470047},{"id":"https://openalex.org/C162319229","wikidata":"https://www.wikidata.org/wiki/Q175263","display_name":"Data structure","level":2,"score":0.2547999918460846},{"id":"https://openalex.org/C116058348","wikidata":"https://www.wikidata.org/wiki/Q846912","display_name":"Universal hashing","level":5,"score":0.25130000710487366}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.7717/peerj-cs.3644","is_oa":true,"landing_page_url":"https://doi.org/10.7717/peerj-cs.3644","pdf_url":null,"source":{"id":"https://openalex.org/S4210178049","display_name":"PeerJ Computer Science","issn_l":"2376-5992","issn":["2376-5992"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310320104","host_organization_name":"PeerJ, Inc.","host_organization_lineage":["https://openalex.org/P4310320104"],"host_organization_lineage_names":["PeerJ, Inc."],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"PeerJ Computer Science","raw_type":"journal-article"},{"id":"pmh:oai:doaj.org/article:200751e422a84a4e8d50808536ccafc6","is_oa":true,"landing_page_url":"https://doaj.org/article/200751e422a84a4e8d50808536ccafc6","pdf_url":null,"source":{"id":"https://openalex.org/S4306401280","display_name":"DOAJ (DOAJ: Directory of Open Access Journals)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by-sa","license_id":"https://openalex.org/licenses/cc-by-sa","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"PeerJ Computer Science, Vol 12, p e3644 (2026)","raw_type":"article"}],"best_oa_location":{"id":"doi:10.7717/peerj-cs.3644","is_oa":true,"landing_page_url":"https://doi.org/10.7717/peerj-cs.3644","pdf_url":null,"source":{"id":"https://openalex.org/S4210178049","display_name":"PeerJ Computer Science","issn_l":"2376-5992","issn":["2376-5992"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310320104","host_organization_name":"PeerJ, Inc.","host_organization_lineage":["https://openalex.org/P4310320104"],"host_organization_lineage_names":["PeerJ, Inc."],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"PeerJ Computer Science","raw_type":"journal-article"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":29,"referenced_works":["https://openalex.org/W2101491865","https://openalex.org/W2887280559","https://openalex.org/W2904458925","https://openalex.org/W2907079035","https://openalex.org/W2907492528","https://openalex.org/W2979997596","https://openalex.org/W3009806461","https://openalex.org/W3159460504","https://openalex.org/W3188279665","https://openalex.org/W3196019032","https://openalex.org/W3203778378","https://openalex.org/W4213019189","https://openalex.org/W4224241592","https://openalex.org/W4226048133","https://openalex.org/W4287326768","https://openalex.org/W4293651439","https://openalex.org/W4295106814","https://openalex.org/W4319659976","https://openalex.org/W4321793523","https://openalex.org/W4328031114","https://openalex.org/W4328114448","https://openalex.org/W4366308846","https://openalex.org/W4372260281","https://openalex.org/W4388152645","https://openalex.org/W4389098833","https://openalex.org/W4389319187","https://openalex.org/W4389936799","https://openalex.org/W4400733246","https://openalex.org/W4404102180"],"related_works":[],"abstract_inverted_index":{"Deep":[0],"hashing":[1,21,47,142],"has":[2],"emerged":[3],"as":[4],"an":[5,65,177],"efficient":[6],"and":[7,91,116,122,139,187,202,212],"robust":[8,220],"solution":[9],"for":[10,161],"image":[11,225],"retrieval":[12],"through":[13,95,119],"representation":[14,114],"learning.":[15,124],"However,":[16],"convolutional":[17,54,69],"neural":[18],"network":[19],"(CNN)-based":[20],"methods":[22],"are":[23],"constrained":[24],"by":[25,50],"their":[26,32],"reliance":[27],"on":[28,127],"grid":[29],"structures,":[30],"limiting":[31],"capacity":[33],"to":[34,104,171,183,191,199],"model":[35,73],"complex":[36],"or":[37],"unstructured":[38,110],"data":[39],"relationships.":[40],"This":[41],"article":[42],"proposes":[43],"a":[44,82],"novel":[45],"deep":[46,141,162],"model,":[48,84],"Hashing":[49],"Autoencoder":[51],"with":[52,67],"graph":[53,68,97,210],"networks":[55,70],"(HAGCN),":[56],"that":[57,132],"integrates":[58],"transfer":[59,83,123,213],"learning\u2013based":[60],"visual":[61],"embeddings,":[62],"obtained":[63],"via":[64],"autoencoder,":[66],"(GCNs).":[71],"The":[72],"dynamically":[74],"constructs":[75],"local":[76,92,107],"subgraphs":[77],"from":[78,223],"the":[79,86,96,128,133,145,152,172,184,192,206,216],"output":[80],"of":[81,88,147,166,179,208,218],"enabling":[85],"learning":[87,117,214],"both":[89,113],"global":[90],"structural":[93,200],"relationships":[94],"Laplacian.":[98],"A":[99],"GCN":[100,149,196],"layer":[101],"is":[102],"employed":[103],"effectively":[105],"capture":[106],"topologies":[108],"in":[109],"data,":[111],"enhancing":[112],"quality":[115],"efficiency":[118],"parameter":[120],"sharing":[121],"Experiments":[125],"conducted":[126],"evaluation":[129],"datasets":[130],"demonstrate":[131],"proposed":[134,153],"method":[135],"outperforms":[136],"existing":[137],"CNN-based":[138],"GCN-based":[140],"approaches.":[143],"Furthermore,":[144],"analysis":[146],"various":[148],"filters":[150,197],"under":[151],"framework":[154],"offers":[155],"valuable":[156],"insights":[157],"into":[158],"filter":[159],"selection":[160],"hashing.":[163],"In":[164],"terms":[165],"average":[167],"performance":[168,186],"improvement":[169],"compared":[170,182,190],"comparison":[173],"models,":[174],"HAGCN":[175],"shows":[176],"increase":[178],"about":[180,188],"7.4%":[181],"minimum":[185],"2.7%":[189],"maximum":[193],"performance.":[194],"Ultimately,":[195],"contribute":[198],"preservation":[201],"improved":[203],"expressiveness,":[204],"while":[205],"combination":[207],"dynamic":[209],"construction":[211],"facilitates":[215],"generation":[217],"compact,":[219],"hash":[221],"codes":[222],"high-dimensional":[224],"data.":[226]},"counts_by_year":[],"updated_date":"2026-05-21T06:26:12.895304","created_date":"2026-03-05T00:00:00"}