{"id":"https://openalex.org/W2970321521","doi":"https://doi.org/10.1109/icip.2019.8803592","title":"Recognizing Fish Species Captured Live on Wild Sea Surface in Videos by Deep Metric Learning with a Temporal Constraint","display_name":"Recognizing Fish Species Captured Live on Wild Sea Surface in Videos by Deep Metric Learning with a Temporal Constraint","publication_year":2019,"publication_date":"2019-08-26","ids":{"openalex":"https://openalex.org/W2970321521","doi":"https://doi.org/10.1109/icip.2019.8803592","mag":"2970321521"},"language":"en","primary_location":{"id":"doi:10.1109/icip.2019.8803592","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icip.2019.8803592","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2019 IEEE International Conference on Image Processing (ICIP)","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/A5038595095","display_name":"Tsung\u2013Wei Huang","orcid":"https://orcid.org/0000-0002-1478-2678"},"institutions":[{"id":"https://openalex.org/I201448701","display_name":"University of Washington","ror":"https://ror.org/00cvxb145","country_code":"US","type":"education","lineage":["https://openalex.org/I201448701"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Tsung-Wei Huang","raw_affiliation_strings":["University of Washington, Seattle, WA, USA"],"affiliations":[{"raw_affiliation_string":"University of Washington, Seattle, WA, USA","institution_ids":["https://openalex.org/I201448701"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101702810","display_name":"Jenq\u2013Neng Hwang","orcid":"https://orcid.org/0000-0002-8877-2421"},"institutions":[{"id":"https://openalex.org/I201448701","display_name":"University of Washington","ror":"https://ror.org/00cvxb145","country_code":"US","type":"education","lineage":["https://openalex.org/I201448701"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Jenq-Neng Hwang","raw_affiliation_strings":["University of Washington, Seattle, WA, USA"],"affiliations":[{"raw_affiliation_string":"University of Washington, Seattle, WA, USA","institution_ids":["https://openalex.org/I201448701"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5086579672","display_name":"Suzanne Romain","orcid":null},"institutions":[{"id":"https://openalex.org/I1308126019","display_name":"National Oceanic and Atmospheric Administration","ror":"https://ror.org/02z5nhe81","country_code":"US","type":"funder","lineage":["https://openalex.org/I1308126019","https://openalex.org/I1343035065"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Suzanne Romain","raw_affiliation_strings":["National Oceanic and Atmospheric Administration, Boulder, CO, USA"],"affiliations":[{"raw_affiliation_string":"National Oceanic and Atmospheric Administration, Boulder, CO, USA","institution_ids":["https://openalex.org/I1308126019"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5017289206","display_name":"Farron Wallace","orcid":"https://orcid.org/0000-0002-3690-9588"},"institutions":[{"id":"https://openalex.org/I1308126019","display_name":"National Oceanic and Atmospheric Administration","ror":"https://ror.org/02z5nhe81","country_code":"US","type":"funder","lineage":["https://openalex.org/I1308126019","https://openalex.org/I1343035065"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Farron Wallace","raw_affiliation_strings":["National Oceanic and Atmospheric Administration, Boulder, CO, USA"],"affiliations":[{"raw_affiliation_string":"National Oceanic and Atmospheric Administration, Boulder, CO, USA","institution_ids":["https://openalex.org/I1308126019"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5038595095"],"corresponding_institution_ids":["https://openalex.org/I201448701"],"apc_list":null,"apc_paid":null,"fwci":0.2176,"has_fulltext":false,"cited_by_count":4,"citation_normalized_percentile":{"value":0.60290412,"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":"3407","last_page":"3411"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12697","display_name":"Water Quality Monitoring Technologies","score":0.9947999715805054,"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"}},"topics":[{"id":"https://openalex.org/T12697","display_name":"Water Quality Monitoring Technologies","score":0.9947999715805054,"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"}},{"id":"https://openalex.org/T11019","display_name":"Image Enhancement Techniques","score":0.9631999731063843,"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/T10331","display_name":"Video Surveillance and Tracking Methods","score":0.9629999995231628,"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/softmax-function","display_name":"Softmax function","score":0.8079778552055359},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.7535169124603271},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7311345338821411},{"id":"https://openalex.org/keywords/classifier","display_name":"Classifier (UML)","score":0.625778079032898},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.5759977698326111},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.5542697310447693},{"id":"https://openalex.org/keywords/embedding","display_name":"Embedding","score":0.4964645504951477},{"id":"https://openalex.org/keywords/feature-vector","display_name":"Feature vector","score":0.48851144313812256},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.48638418316841125},{"id":"https://openalex.org/keywords/feature-extraction","display_name":"Feature extraction","score":0.4653172492980957},{"id":"https://openalex.org/keywords/constraint","display_name":"Constraint (computer-aided design)","score":0.42430436611175537},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.3274378776550293},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.14783406257629395}],"concepts":[{"id":"https://openalex.org/C188441871","wikidata":"https://www.wikidata.org/wiki/Q7554146","display_name":"Softmax function","level":3,"score":0.8079778552055359},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7535169124603271},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7311345338821411},{"id":"https://openalex.org/C95623464","wikidata":"https://www.wikidata.org/wiki/Q1096149","display_name":"Classifier (UML)","level":2,"score":0.625778079032898},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.5759977698326111},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.5542697310447693},{"id":"https://openalex.org/C41608201","wikidata":"https://www.wikidata.org/wiki/Q980509","display_name":"Embedding","level":2,"score":0.4964645504951477},{"id":"https://openalex.org/C83665646","wikidata":"https://www.wikidata.org/wiki/Q42139305","display_name":"Feature vector","level":2,"score":0.48851144313812256},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.48638418316841125},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.4653172492980957},{"id":"https://openalex.org/C2776036281","wikidata":"https://www.wikidata.org/wiki/Q48769818","display_name":"Constraint (computer-aided design)","level":2,"score":0.42430436611175537},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.3274378776550293},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.14783406257629395},{"id":"https://openalex.org/C2524010","wikidata":"https://www.wikidata.org/wiki/Q8087","display_name":"Geometry","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/icip.2019.8803592","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icip.2019.8803592","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2019 IEEE International Conference on Image Processing (ICIP)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"score":0.550000011920929,"id":"https://metadata.un.org/sdg/10","display_name":"Reduced inequalities"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":39,"referenced_works":["https://openalex.org/W56385144","https://openalex.org/W1686810756","https://openalex.org/W2050904803","https://openalex.org/W2063422727","https://openalex.org/W2096733369","https://openalex.org/W2104657103","https://openalex.org/W2163605009","https://openalex.org/W2194775991","https://openalex.org/W2202499615","https://openalex.org/W2291140369","https://openalex.org/W2397719693","https://openalex.org/W2440599146","https://openalex.org/W2480418144","https://openalex.org/W2577024939","https://openalex.org/W2579052148","https://openalex.org/W2609575245","https://openalex.org/W2728026583","https://openalex.org/W2737725206","https://openalex.org/W2740620254","https://openalex.org/W2773003563","https://openalex.org/W2780838211","https://openalex.org/W2798365843","https://openalex.org/W2810894210","https://openalex.org/W2895039767","https://openalex.org/W2962835968","https://openalex.org/W2962887033","https://openalex.org/W2962898354","https://openalex.org/W2963466847","https://openalex.org/W2964036919","https://openalex.org/W2964189431","https://openalex.org/W2964350391","https://openalex.org/W3099206234","https://openalex.org/W3101227480","https://openalex.org/W3141303761","https://openalex.org/W6602324145","https://openalex.org/W6637373629","https://openalex.org/W6684191040","https://openalex.org/W6693735323","https://openalex.org/W6906546961"],"related_works":["https://openalex.org/W2949106576","https://openalex.org/W2795041432","https://openalex.org/W2770892783","https://openalex.org/W2990715442","https://openalex.org/W1616462885","https://openalex.org/W2300169576","https://openalex.org/W2964015640","https://openalex.org/W2896918494","https://openalex.org/W2294271935","https://openalex.org/W3047400341","https://openalex.org/W2972052070","https://openalex.org/W3209908406","https://openalex.org/W3018052340","https://openalex.org/W2921985587","https://openalex.org/W3201090325","https://openalex.org/W2823013781","https://openalex.org/W3132087808","https://openalex.org/W3098055474","https://openalex.org/W2980792518","https://openalex.org/W2903882939"],"abstract_inverted_index":{"Recognizing":[0],"fish":[1,20,31,89,123],"species":[2],"captured":[3],"live":[4],"on":[5,45,162],"wild":[6],"sea":[7],"surface":[8],"in":[9,68,107,130],"videos":[10],"is":[11],"a":[12,39,46,60,77,122],"challenging":[13],"task":[14],"due":[15],"to":[16,75,137],"the":[17,65,73,83,98,108,111,119,131,135,138,147,158],"deformation":[18],"of":[19,23,88,121],"shape,":[21],"self-occlusion":[22],"body":[24],"parts":[25],"and":[26,85,140],"similar":[27],"texture":[28],"between":[29],"different":[30],"classes.":[32],"To":[33],"address":[34],"these":[35],"issues,":[36],"we":[37,71,96,116],"propose":[38],"fine-grained":[40],"image":[41],"classification":[42,161],"method":[43],"based":[44],"deep":[47],"convolution":[48],"neural":[49],"network":[50,74],"(CNN)":[51],"trained":[52],"by":[53,102],"an":[54,104],"innovative":[55],"metric":[56,69],"learning":[57],"scheme":[58],"with":[59],"temporal":[61,66],"constraint.":[62],"By":[63],"introducing":[64,103],"constraint":[67],"learning,":[70],"help":[72],"learn":[76,97],"feature":[78,132],"embedding":[79],"which":[80],"implicitly":[81],"takes":[82],"shape":[84],"pose":[86],"changes":[87],"into":[90,127],"account.":[91],"Besides,":[92],"for":[93,146],"each":[94,125],"class,":[95],"representative":[99],"features":[100,120],"discriminatively":[101],"intermediate":[105],"layer":[106],"CNN":[109],"before":[110],"classifier.":[112],"In":[113],"testing":[114],"stage,":[115],"first":[117],"aggregate":[118],"from":[124],"frame":[126],"several":[128],"clips":[129,136],"space,":[133],"send":[134],"classifier":[139],"then":[141],"perform":[142],"weighted":[143],"majority":[144],"vote":[145],"final":[148],"classification.":[149],"The":[150],"experimental":[151],"results":[152],"show":[153],"that":[154],"our":[155,163],"approach":[156],"outperforms":[157],"conventional":[159],"softmax":[160],"rail-fishing":[164],"dataset.":[165]},"counts_by_year":[{"year":2025,"cited_by_count":1},{"year":2024,"cited_by_count":1},{"year":2021,"cited_by_count":2}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
