{"id":"https://openalex.org/W2910259494","doi":"https://doi.org/10.1109/apccas.2018.8605718","title":"Dolphin Recognition with Adaptive Hybrid Saliency Detection for Deep Learning Based on DenseNet Recognition","display_name":"Dolphin Recognition with Adaptive Hybrid Saliency Detection for Deep Learning Based on DenseNet Recognition","publication_year":2018,"publication_date":"2018-10-01","ids":{"openalex":"https://openalex.org/W2910259494","doi":"https://doi.org/10.1109/apccas.2018.8605718","mag":"2910259494"},"language":"en","primary_location":{"id":"doi:10.1109/apccas.2018.8605718","is_oa":false,"landing_page_url":"https://doi.org/10.1109/apccas.2018.8605718","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2018 IEEE Asia Pacific Conference on Circuits and Systems (APCCAS)","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/A5108189472","display_name":"Hung-Wei Hsu","orcid":null},"institutions":[{"id":"https://openalex.org/I16733864","display_name":"National Taiwan University","ror":"https://ror.org/05bqach95","country_code":"TW","type":"education","lineage":["https://openalex.org/I16733864"]}],"countries":["TW"],"is_corresponding":true,"raw_author_name":"Hung-Wei Hsu","raw_affiliation_strings":["Graduate Institute of Communication Engineering, National Taiwan University, Taipei, Taiwan"],"affiliations":[{"raw_affiliation_string":"Graduate Institute of Communication Engineering, National Taiwan University, Taipei, Taiwan","institution_ids":["https://openalex.org/I16733864"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5072359983","display_name":"Yih-Cherng Lee","orcid":"https://orcid.org/0000-0002-5905-1967"},"institutions":[{"id":"https://openalex.org/I16733864","display_name":"National Taiwan University","ror":"https://ror.org/05bqach95","country_code":"TW","type":"education","lineage":["https://openalex.org/I16733864"]}],"countries":["TW"],"is_corresponding":false,"raw_author_name":"Yih-Cherng Lee","raw_affiliation_strings":["Graduate Institute of Communication Engineering, National Taiwan University, Taipei, Taiwan"],"affiliations":[{"raw_affiliation_string":"Graduate Institute of Communication Engineering, National Taiwan University, Taipei, Taiwan","institution_ids":["https://openalex.org/I16733864"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5022371410","display_name":"Jian\u2013Jiun Ding","orcid":"https://orcid.org/0000-0003-4510-2273"},"institutions":[{"id":"https://openalex.org/I16733864","display_name":"National Taiwan University","ror":"https://ror.org/05bqach95","country_code":"TW","type":"education","lineage":["https://openalex.org/I16733864"]}],"countries":["TW"],"is_corresponding":false,"raw_author_name":"Jian-Jiun Ding","raw_affiliation_strings":["Graduate Institute of Communication Engineering, National Taiwan University, Taipei, Taiwan"],"affiliations":[{"raw_affiliation_string":"Graduate Institute of Communication Engineering, National Taiwan University, Taipei, Taiwan","institution_ids":["https://openalex.org/I16733864"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5052576795","display_name":"Ronald Y. Chang","orcid":"https://orcid.org/0000-0003-4620-6824"},"institutions":[{"id":"https://openalex.org/I4210086894","display_name":"Research Center for Information Technology Innovation, Academia Sinica","ror":"https://ror.org/000zgvm20","country_code":"TW","type":"facility","lineage":["https://openalex.org/I4210086894","https://openalex.org/I84653119"]}],"countries":["TW"],"is_corresponding":false,"raw_author_name":"Ronald Y. Chang","raw_affiliation_strings":["Research Center for Information Technology Innovation, Academia Sinica, Taipei, Taiwan"],"affiliations":[{"raw_affiliation_string":"Research Center for Information Technology Innovation, Academia Sinica, Taipei, Taiwan","institution_ids":["https://openalex.org/I4210086894"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5108189472"],"corresponding_institution_ids":["https://openalex.org/I16733864"],"apc_list":null,"apc_paid":null,"fwci":0.1906,"has_fulltext":false,"cited_by_count":2,"citation_normalized_percentile":{"value":0.62042964,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":94},"biblio":{"volume":"24","issue":null,"first_page":"455","last_page":"458"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10659","display_name":"Marine animal studies overview","score":0.9898999929428101,"subfield":{"id":"https://openalex.org/subfields/2303","display_name":"Ecology"},"field":{"id":"https://openalex.org/fields/23","display_name":"Environmental Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T10659","display_name":"Marine animal studies overview","score":0.9898999929428101,"subfield":{"id":"https://openalex.org/subfields/2303","display_name":"Ecology"},"field":{"id":"https://openalex.org/fields/23","display_name":"Environmental Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T11605","display_name":"Visual Attention and Saliency Detection","score":0.9775999784469604,"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/T12697","display_name":"Water Quality Monitoring Technologies","score":0.973800003528595,"subfield":{"id":"https://openalex.org/subfields/2312","display_name":"Water Science and Technology"},"field":{"id":"https://openalex.org/fields/23","display_name":"Environmental Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7913134694099426},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.7416509985923767},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.717251181602478},{"id":"https://openalex.org/keywords/identification","display_name":"Identification (biology)","score":0.6592339873313904},{"id":"https://openalex.org/keywords/feature-extraction","display_name":"Feature extraction","score":0.5809068083763123},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.5433031916618347},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.535138726234436},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.47318363189697266},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.4438709020614624}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7913134694099426},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.7416509985923767},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.717251181602478},{"id":"https://openalex.org/C116834253","wikidata":"https://www.wikidata.org/wiki/Q2039217","display_name":"Identification (biology)","level":2,"score":0.6592339873313904},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.5809068083763123},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.5433031916618347},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.535138726234436},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.47318363189697266},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.4438709020614624},{"id":"https://openalex.org/C59822182","wikidata":"https://www.wikidata.org/wiki/Q441","display_name":"Botany","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},{"id":"https://openalex.org/C41895202","wikidata":"https://www.wikidata.org/wiki/Q8162","display_name":"Linguistics","level":1,"score":0.0},{"id":"https://openalex.org/C86803240","wikidata":"https://www.wikidata.org/wiki/Q420","display_name":"Biology","level":0,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/apccas.2018.8605718","is_oa":false,"landing_page_url":"https://doi.org/10.1109/apccas.2018.8605718","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2018 IEEE Asia Pacific Conference on Circuits and Systems (APCCAS)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"score":0.699999988079071,"display_name":"Life below water","id":"https://metadata.un.org/sdg/14"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":17,"referenced_works":["https://openalex.org/W2005122839","https://openalex.org/W2022945913","https://openalex.org/W2135957164","https://openalex.org/W2160398355","https://openalex.org/W2179140302","https://openalex.org/W2520945814","https://openalex.org/W2569469748","https://openalex.org/W2572966641","https://openalex.org/W2634948901","https://openalex.org/W2744613561","https://openalex.org/W2962851944","https://openalex.org/W2963299740","https://openalex.org/W2963430954","https://openalex.org/W2963446712","https://openalex.org/W6680437723","https://openalex.org/W6685133223","https://openalex.org/W6727249260"],"related_works":["https://openalex.org/W4293226380","https://openalex.org/W4375867731","https://openalex.org/W2611989081","https://openalex.org/W4226493464","https://openalex.org/W4312417841","https://openalex.org/W3133861977","https://openalex.org/W2951211570","https://openalex.org/W3103566983","https://openalex.org/W3167935049","https://openalex.org/W3029198973"],"abstract_inverted_index":{"Dolphin":[0],"identification":[1,26,38,81],"is":[2,19,40,84],"important":[3,20],"for":[4,51],"wildlife":[5],"conservation.":[6],"Since":[7],"identifying":[8],"dolphins":[9,62,123],"from":[10],"thousands":[11],"of":[12,88,116],"images":[13],"manually":[14],"takes":[15],"tremendous":[16],"time,":[17],"it":[18,110],"to":[21,60,78,124],"develop":[22],"an":[23,44],"automatic":[24],"dolphin":[25,37,105],"algorithm.":[27],"In":[28],"this":[29],"paper,":[30],"a":[31,86,104],"high":[32],"accurate":[33],"deep":[34],"learning":[35],"based":[36,93],"algorithm":[39,100],"proposed.":[41],"We":[42],"presented":[43],"advanced":[45],"approach,":[46],"called":[47],"hybrid":[48],"saliency":[49],"method,":[50],"feature":[52],"extraction":[53],"and":[54,109],"efficiently":[55],"integrate":[56],"several":[57],"well-known":[58],"techniques":[59],"make":[61],"distinguishable.":[63],"With":[64],"the":[65,71,75,80,98,113],"proposed":[66,99],"techniques,":[67],"we":[68],"can":[69,101,111],"avoid":[70],"background":[72],"part":[73],"(e.g.":[74],"sea":[76],"water)":[77],"affect":[79],"results,":[82],"which":[83],"usually":[85],"problem":[87],"most":[89,107],"convolutional":[90],"neural":[91],"network":[92],"methods.":[94],"Simulations":[95],"show":[96],"that":[97],"well":[102],"identify":[103],"in":[106],"cases":[108],"achieve":[112],"accuracy":[114],"rate":[115],"85%":[117],"even":[118],"if":[119],"there":[120],"are":[121],"40":[122],"be":[125],"distinguished.":[126]},"counts_by_year":[{"year":2022,"cited_by_count":1},{"year":2021,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}