iBet uBet web content aggregator. Adding the entire web to your favor.
iBet uBet web content aggregator. Adding the entire web to your favor.



Link to original content: https://api.openalex.org/works/doi:10.1109/ICMEW.2015.7169768
{"id":"https://openalex.org/W4234121492","doi":"https://doi.org/10.1109/icmew.2015.7169768","title":"Compact deep neural networks for device based image classification","display_name":"Compact deep neural networks for device based image classification","publication_year":2015,"publication_date":"2015-06-01","ids":{"openalex":"https://openalex.org/W4234121492","doi":"https://doi.org/10.1109/icmew.2015.7169768"},"language":"en","primary_location":{"is_oa":false,"landing_page_url":"https://doi.org/10.1109/icmew.2015.7169768","pdf_url":null,"source":null,"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":false},"type":"article","type_crossref":"proceedings-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/A5031713387","display_name":"Zejia Zheng","orcid":"https://orcid.org/0000-0002-6527-6003"},"institutions":[{"id":"https://openalex.org/I87216513","display_name":"Michigan State University","ror":"https://ror.org/05hs6h993","country_code":"US","type":"education","lineage":["https://openalex.org/I87216513"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"None Zejia Zheng","raw_affiliation_strings":["Michigan State University"],"affiliations":[{"raw_affiliation_string":"Michigan State University","institution_ids":["https://openalex.org/I87216513"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100380611","display_name":"Zhu Li","orcid":"https://orcid.org/0000-0002-1616-4240"},"institutions":[{"id":"https://openalex.org/I4210133173","display_name":"Research!America (United States)","ror":"https://ror.org/044pgyv50","country_code":"US","type":"company","lineage":["https://openalex.org/I4210133173"]},{"id":"https://openalex.org/I4210101778","display_name":"Samsung (United States)","ror":"https://ror.org/01bfbvm65","country_code":"US","type":"company","lineage":["https://openalex.org/I2250650973","https://openalex.org/I4210101778"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"None Zhu Li","raw_affiliation_strings":["Samsung Research America"],"affiliations":[{"raw_affiliation_string":"Samsung Research America","institution_ids":["https://openalex.org/I4210133173","https://openalex.org/I4210101778"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5014849352","display_name":"Abhishek Nagar","orcid":null},"institutions":[{"id":"https://openalex.org/I4210101778","display_name":"Samsung (United States)","ror":"https://ror.org/01bfbvm65","country_code":"US","type":"company","lineage":["https://openalex.org/I2250650973","https://openalex.org/I4210101778"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Abhishek Nagar","raw_affiliation_strings":["Samsung Electronics America"],"affiliations":[{"raw_affiliation_string":"Samsung Electronics America","institution_ids":["https://openalex.org/I4210101778"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5022821908","display_name":"Kyungmo Park","orcid":"https://orcid.org/0000-0002-7689-8967"},"institutions":[{"id":"https://openalex.org/I2250650973","display_name":"Samsung (South Korea)","ror":"https://ror.org/04w3jy968","country_code":"KR","type":"company","lineage":["https://openalex.org/I2250650973"]}],"countries":["KR"],"is_corresponding":false,"raw_author_name":"None Kyungmo Park","raw_affiliation_strings":["Samsung Electronics, Korea"],"affiliations":[{"raw_affiliation_string":"Samsung Electronics, Korea","institution_ids":["https://openalex.org/I2250650973"]}]}],"institution_assertions":[],"countries_distinct_count":2,"institutions_distinct_count":4,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":true,"fulltext_origin":"ngrams","cited_by_count":3,"citation_normalized_percentile":{"value":0.54343,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":77,"max":79},"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"6"},"is_retracted":false,"is_paratext":false,"primary_topic":{"id":"https://openalex.org/T10036","display_name":"Deep Learning in Computer Vision and Image Recognition","score":0.9997,"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":"Deep Learning in Computer Vision and Image Recognition","score":0.9997,"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/T10627","display_name":"Image Feature Retrieval and Recognition Techniques","score":0.9995,"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/T12702","display_name":"Classification of Brain Tumor Type and Grade","score":0.9978,"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/contextual-image-classification","display_name":"Contextual image classification","score":0.5495875},{"id":"https://openalex.org/keywords/cmos-image-sensors","display_name":"CMOS Image Sensors","score":0.485578}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8430596},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.7813391},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6507586},{"id":"https://openalex.org/C53533937","wikidata":"https://www.wikidata.org/wiki/Q185020","display_name":"Histogram","level":3,"score":0.5677534},{"id":"https://openalex.org/C75294576","wikidata":"https://www.wikidata.org/wiki/Q5165192","display_name":"Contextual image classification","level":3,"score":0.5495875},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.5303291},{"id":"https://openalex.org/C78201319","wikidata":"https://www.wikidata.org/wiki/Q685727","display_name":"Grayscale","level":3,"score":0.52351505},{"id":"https://openalex.org/C186967261","wikidata":"https://www.wikidata.org/wiki/Q5082128","display_name":"Mobile device","level":2,"score":0.5133278},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.5111521},{"id":"https://openalex.org/C95623464","wikidata":"https://www.wikidata.org/wiki/Q1096149","display_name":"Classifier (UML)","level":2,"score":0.5040476},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.48721665},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.3751577},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.36857304},{"id":"https://openalex.org/C111919701","wikidata":"https://www.wikidata.org/wiki/Q9135","display_name":"Operating system","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"is_oa":false,"landing_page_url":"https://doi.org/10.1109/icmew.2015.7169768","pdf_url":null,"source":null,"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":false}],"best_oa_location":null,"sustainable_development_goals":[],"grants":[],"datasets":[],"versions":[],"referenced_works_count":14,"referenced_works":["https://openalex.org/W1904365287","https://openalex.org/W2017814585","https://openalex.org/W2109325523","https://openalex.org/W2112796928","https://openalex.org/W2116456623","https://openalex.org/W2126326837","https://openalex.org/W2141125852","https://openalex.org/W2154579312","https://openalex.org/W2163605009","https://openalex.org/W2206858481","https://openalex.org/W2963542991","https://openalex.org/W3118608800","https://openalex.org/W4230377813","https://openalex.org/W4919037"],"related_works":["https://openalex.org/W4362659915","https://openalex.org/W2768918307","https://openalex.org/W2339449039","https://openalex.org/W2113071088","https://openalex.org/W2110031805","https://openalex.org/W2040020606","https://openalex.org/W2005234362","https://openalex.org/W1997235926","https://openalex.org/W1555939286","https://openalex.org/W115686965"],"abstract_inverted_index":{"Convolutional":[0],"Neural":[1],"Network":[2],"(CNN)":[3],"is":[4,79,87,141,153,225],"efficient":[5],"in":[6,125,215],"learning":[7,64],"hierarchical":[8],"features":[9,160],"from":[10,26,133,162],"large":[11,19],"image":[12,68,172,193],"datasets,":[13],"but":[14],"its":[15,84],"model":[16,216,224],"complexity":[17,120],"and":[18,89,119,178],"memory":[20,217],"foot":[21,218],"prints":[22],"are":[23,38,103],"preventing":[24],"it":[25],"being":[27,226],"deployed":[28,227],"to":[29,53,143,156,187,228],"devices":[30],"without":[31],"a":[32,111,126,199],"server":[33,77],"back-end":[34,78],"support.":[35],"Modern":[36],"CNNs":[37],"always":[39],"trained":[40,168],"on":[41,92,99,170,202],"GPUs":[42],"or":[43,83],"even":[44],"GPU":[45],"clusters":[46],"with":[47,173,204],"high":[48,150],"speed":[49],"computation":[50],"capability":[51],"due":[52],"the":[54,58,94,97,100,117,130,134,145,158,163,179,205,208,229],"immense":[55],"size":[56,95],"of":[57,96,121,176,207],"network.":[59],"A":[60,137,149],"device":[61],"based":[62],"deep":[63],"CNN":[65,127,165],"engine":[66],"for":[67,74,192],"classification":[69],"can":[70],"be":[71],"very":[72],"useful":[73],"situations":[75],"where":[76],"either":[80],"not":[81],"available,":[82],"communication":[85],"link":[86],"weak":[88],"unreliable.":[90],"Methods":[91],"regulating":[93],"network,":[98],"other":[101],"hand,":[102],"rarely":[104],"studied.":[105],"In":[106],"this":[107,221],"paper":[108],"we":[109],"present":[110],"novel":[112],"compact":[113,164,185],"architecture":[114,186],"that":[115,166],"minimizes":[116],"number":[118,175],"lower":[122],"level":[123,151],"kernels":[124],"by":[128],"separating":[129],"color":[131,147],"information":[132],"original":[135],"image.":[136],"9-patch":[138],"histogram":[139,180],"extractor":[140],"built":[142],"exploit":[144],"unused":[146],"information.":[148],"classifier":[152],"then":[154],"used":[155],"learn":[157],"combined":[159],"obtained":[161],"was":[167],"only":[169],"grayscale":[171],"limited":[174],"kernels,":[177],"extractor.":[181],"We":[182],"apply":[183],"our":[184,223],"Samsung":[188],"Mobile":[189],"Image":[190],"Dataset":[191],"classification.":[194],"The":[195],"proposed":[196],"solution":[197],"has":[198],"recognition":[200],"accuracy":[201],"par":[203],"state":[206],"art":[209],"CNNs,":[210],"while":[211],"achieving":[212],"significant":[213],"reduction":[214],"print.":[219],"With":[220],"advantage,":[222],"mobile":[230],"devices.":[231]},"cited_by_api_url":"https://api.openalex.org/works?filter=cites:W4234121492","counts_by_year":[{"year":2022,"cited_by_count":1},{"year":2021,"cited_by_count":2},{"year":2017,"cited_by_count":1}],"updated_date":"2024-11-12T08:34:54.926251","created_date":"2022-05-12"}