{"id":"https://openalex.org/W2396526215","doi":"https://doi.org/10.1109/wacv.2016.7477600","title":"Learning deep-sea substrate types with visual topic models","display_name":"Learning deep-sea substrate types with visual topic models","publication_year":2016,"publication_date":"2016-03-01","ids":{"openalex":"https://openalex.org/W2396526215","doi":"https://doi.org/10.1109/wacv.2016.7477600","mag":"2396526215"},"language":"en","primary_location":{"id":"doi:10.1109/wacv.2016.7477600","is_oa":false,"landing_page_url":"https://doi.org/10.1109/wacv.2016.7477600","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2016 IEEE Winter Conference on Applications of Computer Vision (WACV)","raw_type":"proceedings-article"},"type":"preprint","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/A5036772374","display_name":"Arnold Kalmbach","orcid":null},"institutions":[{"id":"https://openalex.org/I5023651","display_name":"McGill University","ror":"https://ror.org/01pxwe438","country_code":"CA","type":"education","lineage":["https://openalex.org/I5023651"]}],"countries":["CA"],"is_corresponding":false,"raw_author_name":"Arnold Kalmbach","raw_affiliation_strings":["McGill University"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"McGill University","institution_ids":["https://openalex.org/I5023651"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5088432811","display_name":"Maia Hoeberechts","orcid":null},"institutions":[{"id":"https://openalex.org/I2802200126","display_name":"Ocean Networks Canada Society","ror":"https://ror.org/05gknh003","country_code":"CA","type":"facility","lineage":["https://openalex.org/I212119943","https://openalex.org/I2802200126"]}],"countries":["CA"],"is_corresponding":false,"raw_author_name":"Maia Hoeberechts","raw_affiliation_strings":["Ocean Networks Canada"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Ocean Networks Canada","institution_ids":["https://openalex.org/I2802200126"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5036029722","display_name":"Alexandra Branzan Albu","orcid":"https://orcid.org/0000-0001-8991-0999"},"institutions":[{"id":"https://openalex.org/I212119943","display_name":"University of Victoria","ror":"https://ror.org/04s5mat29","country_code":"CA","type":"education","lineage":["https://openalex.org/I212119943"]}],"countries":["CA"],"is_corresponding":false,"raw_author_name":"Alexandra Branzan Albu","raw_affiliation_strings":["University of Victoria"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of Victoria","institution_ids":["https://openalex.org/I212119943"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5105836701","display_name":"Herv\u00e9 Glotin","orcid":null},"institutions":[{"id":"https://openalex.org/I143002897","display_name":"Universit\u00e9 de Toulon","ror":"https://ror.org/02m9kbe37","country_code":"FR","type":"education","lineage":["https://openalex.org/I143002897"]},{"id":"https://openalex.org/I190861549","display_name":"Arts et M\u00e9tiers","ror":"https://ror.org/018pp1107","country_code":"FR","type":"education","lineage":["https://openalex.org/I190752583","https://openalex.org/I190861549","https://openalex.org/I4210134562"]}],"countries":["FR"],"is_corresponding":false,"raw_author_name":"Herve Glotin","raw_affiliation_strings":["LSIS AMU ENSAM CNRS Univ. Toulon"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"LSIS AMU ENSAM CNRS Univ. Toulon","institution_ids":["https://openalex.org/I143002897","https://openalex.org/I190861549"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5012076785","display_name":"S\u00e9bastien Paris","orcid":"https://orcid.org/0000-0001-8104-0431"},"institutions":[{"id":"https://openalex.org/I143002897","display_name":"Universit\u00e9 de Toulon","ror":"https://ror.org/02m9kbe37","country_code":"FR","type":"education","lineage":["https://openalex.org/I143002897"]},{"id":"https://openalex.org/I190861549","display_name":"Arts et M\u00e9tiers","ror":"https://ror.org/018pp1107","country_code":"FR","type":"education","lineage":["https://openalex.org/I190752583","https://openalex.org/I190861549","https://openalex.org/I4210134562"]}],"countries":["FR"],"is_corresponding":false,"raw_author_name":"Sebastien Paris","raw_affiliation_strings":["LSIS AMU ENSAM CNRS Univ. Toulon"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"LSIS AMU ENSAM CNRS Univ. Toulon","institution_ids":["https://openalex.org/I143002897","https://openalex.org/I190861549"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5072400128","display_name":"Yogesh Girdhar","orcid":"https://orcid.org/0000-0001-9510-9639"},"institutions":[{"id":"https://openalex.org/I66958751","display_name":"Woods Hole Oceanographic Institution","ror":"https://ror.org/03zbnzt98","country_code":"US","type":"funder","lineage":["https://openalex.org/I66958751"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Yogesh Girdhar","raw_affiliation_strings":["Woods Hole Oceanographic Inst"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Woods Hole Oceanographic Inst","institution_ids":["https://openalex.org/I66958751"]}]}],"institutions":[],"countries_distinct_count":3,"institutions_distinct_count":6,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.169,"has_fulltext":false,"cited_by_count":4,"citation_normalized_percentile":{"value":0.55052001,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":94},"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"9"},"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":0.9991000294685364,"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":0.9991000294685364,"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/T10191","display_name":"Robotics and Sensor-Based Localization","score":0.9955999851226807,"subfield":{"id":"https://openalex.org/subfields/2202","display_name":"Aerospace 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/T11714","display_name":"Multimodal Machine Learning Applications","score":0.9891999959945679,"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/computer-science","display_name":"Computer science","score":0.7561661005020142},{"id":"https://openalex.org/keywords/remotely-operated-underwater-vehicle","display_name":"Remotely operated underwater vehicle","score":0.7253447771072388},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.7197024822235107},{"id":"https://openalex.org/keywords/classifier","display_name":"Classifier (UML)","score":0.5497208833694458},{"id":"https://openalex.org/keywords/feature-extraction","display_name":"Feature extraction","score":0.5265833735466003},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.5013587474822998},{"id":"https://openalex.org/keywords/latent-dirichlet-allocation","display_name":"Latent Dirichlet allocation","score":0.4248826503753662},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.3900741636753082},{"id":"https://openalex.org/keywords/remote-sensing","display_name":"Remote sensing","score":0.3539891541004181},{"id":"https://openalex.org/keywords/topic-model","display_name":"Topic model","score":0.21888437867164612},{"id":"https://openalex.org/keywords/geology","display_name":"Geology","score":0.19696012139320374},{"id":"https://openalex.org/keywords/mobile-robot","display_name":"Mobile robot","score":0.08766967058181763}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7561661005020142},{"id":"https://openalex.org/C145424490","wikidata":"https://www.wikidata.org/wiki/Q618465","display_name":"Remotely operated underwater vehicle","level":4,"score":0.7253447771072388},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7197024822235107},{"id":"https://openalex.org/C95623464","wikidata":"https://www.wikidata.org/wiki/Q1096149","display_name":"Classifier (UML)","level":2,"score":0.5497208833694458},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.5265833735466003},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.5013587474822998},{"id":"https://openalex.org/C500882744","wikidata":"https://www.wikidata.org/wiki/Q269236","display_name":"Latent Dirichlet allocation","level":3,"score":0.4248826503753662},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.3900741636753082},{"id":"https://openalex.org/C62649853","wikidata":"https://www.wikidata.org/wiki/Q199687","display_name":"Remote sensing","level":1,"score":0.3539891541004181},{"id":"https://openalex.org/C171686336","wikidata":"https://www.wikidata.org/wiki/Q3532085","display_name":"Topic model","level":2,"score":0.21888437867164612},{"id":"https://openalex.org/C127313418","wikidata":"https://www.wikidata.org/wiki/Q1069","display_name":"Geology","level":0,"score":0.19696012139320374},{"id":"https://openalex.org/C19966478","wikidata":"https://www.wikidata.org/wiki/Q4810574","display_name":"Mobile robot","level":3,"score":0.08766967058181763},{"id":"https://openalex.org/C90509273","wikidata":"https://www.wikidata.org/wiki/Q11012","display_name":"Robot","level":2,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1109/wacv.2016.7477600","is_oa":false,"landing_page_url":"https://doi.org/10.1109/wacv.2016.7477600","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2016 IEEE Winter Conference on Applications of Computer Vision (WACV)","raw_type":"proceedings-article"},{"id":"pmh:oai:HAL:hal-01490123v1","is_oa":false,"landing_page_url":"https://amu.hal.science/hal-01490123","pdf_url":null,"source":{"id":"https://openalex.org/S4306402512","display_name":"HAL (Le Centre pour la Communication Scientifique Directe)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I1294671590","host_organization_name":"Centre National de la Recherche Scientifique","host_organization_lineage":["https://openalex.org/I1294671590"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"2016 IEEE Winter Conference on Applications of Computer Vision, WACV 2016, Lake Placid, NY, USA, March 7-10, 2016, 2016, Unknown, Unknown Region. pp.1--9","raw_type":"Conference papers"}],"best_oa_location":null,"sustainable_development_goals":[{"display_name":"Life below water","id":"https://metadata.un.org/sdg/14","score":0.8799999952316284}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":24,"referenced_works":["https://openalex.org/W1566135517","https://openalex.org/W1742311587","https://openalex.org/W2006368361","https://openalex.org/W2019872719","https://openalex.org/W2112910373","https://openalex.org/W2114915132","https://openalex.org/W2116047352","https://openalex.org/W2139977933","https://openalex.org/W2142704643","https://openalex.org/W2161050705","https://openalex.org/W2168359206","https://openalex.org/W2174706414","https://openalex.org/W2222512263","https://openalex.org/W2239469776","https://openalex.org/W2398769936","https://openalex.org/W2962947528","https://openalex.org/W3083508041","https://openalex.org/W3099932909","https://openalex.org/W4252381709","https://openalex.org/W6677265841","https://openalex.org/W6688975714","https://openalex.org/W6690265313","https://openalex.org/W6712605650","https://openalex.org/W6834357723"],"related_works":["https://openalex.org/W3126382579","https://openalex.org/W3107650560","https://openalex.org/W4315588616","https://openalex.org/W4317422773","https://openalex.org/W2810542905","https://openalex.org/W2129350855","https://openalex.org/W2888805565","https://openalex.org/W4243064001","https://openalex.org/W3123667230","https://openalex.org/W3096554474"],"abstract_inverted_index":{"We":[0],"propose":[1],"and":[2,43,61,108,128,164,204,216],"evaluate":[3],"a":[4,15,28,70,89,125,155,169],"method":[5,51,190],"for":[6],"learning":[7],"deep-sea":[8,112],"substrate":[9,33,101,183,219],"types":[10,34,62,184],"using":[11],"video":[12,37],"recorded":[13],"with":[14],"remotely":[16],"operated":[17],"vehicle":[18],"(ROV).":[19],"The":[20,47,79,116],"goal":[21],"of":[22,32,49,55,63,72,82,88,111,157,161,171,182],"this":[23,83],"work":[24,84],"is":[25,85,96,131,151,176],"to":[26,40,98,133,153,178],"create":[27],"labelled":[29],"spatial":[30,129],"map":[31],"from":[35,75,137],"ROV":[36,77,113],"in":[38,66,123,142,167],"order":[39],"support":[41],"biological":[42],"geological":[44,56],"domain":[45],"research.":[46],"output":[48],"our":[50,211],"describes":[52],"the":[53,86,105,138,144,162,180],"mixtures":[54,103],"features":[57,198],"such":[58],"as":[59],"sediment":[60],"lava":[64],"flow":[65],"images":[67],"taken":[68],"at":[69],"set":[71],"points":[73],"chosen":[74],"an":[76],"dive.":[78],"main":[80,120],"contribution":[81],"assembly":[87],"pipeline":[90,117,213],"combining":[91],"several":[92],"unique":[93],"approaches":[94],"which":[95,124,143,168],"able":[97],"robustly":[99],"generate":[100,154],"type":[102],"under":[104],"varying":[106],"lighting":[107],"perspective":[109],"conditions":[110],"dive":[114],"videos.":[115],"comprises":[118],"three":[119],"components:":[121],"sampling,":[122],"trained":[126],"classifier":[127],"sampling":[130],"used":[132,152,177],"select":[134],"relevant":[135],"frames":[136],"dataset;":[139,163],"feature":[140],"extraction,":[141],"improved":[145],"local":[146],"binary":[147],"pattern":[148],"descriptor":[149],"(ILBP)":[150],"Bag":[156],"Words":[158],"(BoW)":[159],"representation":[160],"topic":[165],"modelling":[166],"variant":[170],"Latent":[172],"Dirichlet":[173],"Allocation":[174],"(LDA),":[175],"infer":[179],"mixture":[181],"represented":[185],"by":[186],"each":[187],"BoW.":[188],"Our":[189],"significantly":[191],"outperforms":[192],"techniques":[193],"relying":[194],"on":[195],"keypoint":[196],"based":[197,202],"rather":[199,206],"than":[200,207],"texture":[201],"features,":[203],"k-means":[205],"LDA,":[208],"demonstrating":[209],"that":[210],"proposed":[212],"accurately":[214],"learns":[215],"identifies":[217],"visible":[218],"types.":[220]},"counts_by_year":[{"year":2023,"cited_by_count":1},{"year":2022,"cited_by_count":1},{"year":2021,"cited_by_count":1},{"year":2017,"cited_by_count":1}],"updated_date":"2026-06-11T09:08:48.828518","created_date":"2025-10-10T00:00:00"}