{"id":"https://openalex.org/W2296404378","doi":"https://doi.org/10.1109/icip.2015.7351319","title":"Coupled ensemble graph cuts and object verification for animal segmentation from highly cluttered videos","display_name":"Coupled ensemble graph cuts and object verification for animal segmentation from highly cluttered videos","publication_year":2015,"publication_date":"2015-09-01","ids":{"openalex":"https://openalex.org/W2296404378","doi":"https://doi.org/10.1109/icip.2015.7351319","mag":"2296404378"},"language":"en","primary_location":{"id":"doi:10.1109/icip.2015.7351319","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icip.2015.7351319","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2015 IEEE International Conference on Image Processing (ICIP)","raw_type":"proceedings-article"},"type":"conference-paper","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/A5100410702","display_name":"Zhi Zhang","orcid":"https://orcid.org/0000-0003-0249-1678"},"institutions":[{"id":"https://openalex.org/I76835614","display_name":"University of Missouri","ror":"https://ror.org/02ymw8z06","country_code":"US","type":"education","lineage":["https://openalex.org/I76835614"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Zhi Zhang","raw_affiliation_strings":["Department of Electrical and Computer Engineering, University of Missouri, MO, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Electrical and Computer Engineering, University of Missouri, MO, USA","institution_ids":["https://openalex.org/I76835614"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5112456575","display_name":"Tony Xiao Han","orcid":null},"institutions":[{"id":"https://openalex.org/I76835614","display_name":"University of Missouri","ror":"https://ror.org/02ymw8z06","country_code":"US","type":"education","lineage":["https://openalex.org/I76835614"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Tony X. Han","raw_affiliation_strings":["Department of Electrical and Computer Engineering, University of Missouri, MO, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Electrical and Computer Engineering, University of Missouri, MO, USA","institution_ids":["https://openalex.org/I76835614"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5110252153","display_name":"Zhihai He","orcid":"https://orcid.org/0000-0003-2255-4293"},"institutions":[{"id":"https://openalex.org/I4777552","display_name":"University of Missouri System","ror":"https://ror.org/032va1j59","country_code":"US","type":"education","lineage":["https://openalex.org/I4777552"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Zhihai He","raw_affiliation_strings":["Department of Electrical and Computer Engineering, University of Missouri System, Columbia, MO, US"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Electrical and Computer Engineering, University of Missouri System, Columbia, MO, US","institution_ids":["https://openalex.org/I4777552"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":1.0813,"has_fulltext":false,"cited_by_count":16,"citation_normalized_percentile":{"value":0.87673533,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":98},"biblio":{"volume":null,"issue":null,"first_page":"2830","last_page":"2834"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10331","display_name":"Video Surveillance and Tracking Methods","score":0.9998000264167786,"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/T10331","display_name":"Video Surveillance and Tracking Methods","score":0.9998000264167786,"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":"Advanced Image and Video Retrieval Techniques","score":0.9962000250816345,"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/T10812","display_name":"Human Pose and Action Recognition","score":0.9840999841690063,"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":0.7986158132553101},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.7665857076644897},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.7143451571464539},{"id":"https://openalex.org/keywords/segmentation","display_name":"Segmentation","score":0.6773489713668823},{"id":"https://openalex.org/keywords/object-detection","display_name":"Object detection","score":0.62684166431427},{"id":"https://openalex.org/keywords/benchmark","display_name":"Benchmark (surveying)","score":0.620040774345398},{"id":"https://openalex.org/keywords/image-segmentation","display_name":"Image segmentation","score":0.5614656209945679},{"id":"https://openalex.org/keywords/false-positive-paradox","display_name":"False positive paradox","score":0.5574964284896851},{"id":"https://openalex.org/keywords/object","display_name":"Object (grammar)","score":0.4741843640804291},{"id":"https://openalex.org/keywords/cut","display_name":"Cut","score":0.4700687527656555},{"id":"https://openalex.org/keywords/graph","display_name":"Graph","score":0.4145069718360901},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.4114660322666168}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7986158132553101},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7665857076644897},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.7143451571464539},{"id":"https://openalex.org/C89600930","wikidata":"https://www.wikidata.org/wiki/Q1423946","display_name":"Segmentation","level":2,"score":0.6773489713668823},{"id":"https://openalex.org/C2776151529","wikidata":"https://www.wikidata.org/wiki/Q3045304","display_name":"Object detection","level":3,"score":0.62684166431427},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.620040774345398},{"id":"https://openalex.org/C124504099","wikidata":"https://www.wikidata.org/wiki/Q56933","display_name":"Image segmentation","level":3,"score":0.5614656209945679},{"id":"https://openalex.org/C64869954","wikidata":"https://www.wikidata.org/wiki/Q1859747","display_name":"False positive paradox","level":2,"score":0.5574964284896851},{"id":"https://openalex.org/C2781238097","wikidata":"https://www.wikidata.org/wiki/Q175026","display_name":"Object (grammar)","level":2,"score":0.4741843640804291},{"id":"https://openalex.org/C5134670","wikidata":"https://www.wikidata.org/wiki/Q1626444","display_name":"Cut","level":4,"score":0.4700687527656555},{"id":"https://openalex.org/C132525143","wikidata":"https://www.wikidata.org/wiki/Q141488","display_name":"Graph","level":2,"score":0.4145069718360901},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.4114660322666168},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.0},{"id":"https://openalex.org/C13280743","wikidata":"https://www.wikidata.org/wiki/Q131089","display_name":"Geodesy","level":1,"score":0.0},{"id":"https://openalex.org/C205649164","wikidata":"https://www.wikidata.org/wiki/Q1071","display_name":"Geography","level":0,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/icip.2015.7351319","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icip.2015.7351319","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2015 IEEE International Conference on Image Processing (ICIP)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/15","display_name":"Life in Land","score":0.5699999928474426}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":19,"referenced_works":["https://openalex.org/W1527357463","https://openalex.org/W1964127768","https://openalex.org/W1976921161","https://openalex.org/W2018882088","https://openalex.org/W2035866593","https://openalex.org/W2040441737","https://openalex.org/W2052524720","https://openalex.org/W2072145684","https://openalex.org/W2113609219","https://openalex.org/W2118585731","https://openalex.org/W2124351162","https://openalex.org/W2130736843","https://openalex.org/W2135910427","https://openalex.org/W2140235142","https://openalex.org/W2157526871","https://openalex.org/W2161542313","https://openalex.org/W2164720308","https://openalex.org/W2166978545","https://openalex.org/W6679883415"],"related_works":["https://openalex.org/W2378211422","https://openalex.org/W1557094818","https://openalex.org/W4390721878","https://openalex.org/W2109407305","https://openalex.org/W2032319136","https://openalex.org/W1582388844","https://openalex.org/W2088651901","https://openalex.org/W2897997384","https://openalex.org/W1544828638","https://openalex.org/W2049858394"],"abstract_inverted_index":{"In":[0],"this":[1,37],"paper,":[2],"we":[3,39,93],"consider":[4,54],"animal":[5,89],"object":[6,51,56,64,96],"segmentation":[7,74],"from":[8],"wildlife":[9,26],"monitoring":[10,27],"videos":[11],"captured":[12],"by":[13],"motion-triggered":[14],"cameras,":[15],"called":[16],"camera-traps.":[17],"This":[18],"is":[19],"a":[20,77,122,160],"very":[21],"challenging":[22,126,157],"task":[23],"because":[24],"the":[25,43,103,109,112,132,141,150,155],"scenes":[28],"are":[29],"often":[30],"highly":[31],"cluttered":[32],"and":[33,50,71,79,91,118,148],"dynamic.":[34],"To":[35,82],"address":[36],"issue,":[38],"propose":[40],"to":[41,99,108,152],"explore":[42],"ideas":[44],"of":[45,61,125,163],"coupled":[46],"ensemble":[47,60],"graph":[48],"cuts":[49],"verification.":[52],"We":[53],"video":[55,164],"cut":[57],"as":[58,129,131],"an":[59,95],"frame-level":[62],"background-foreground":[63],"classifiers":[65],"which":[66],"fuse":[67],"information":[68],"across":[69],"frames":[70],"refine":[72],"their":[73],"results":[75,117],"in":[76,87,159],"collaborative":[78],"iterative":[80],"manner.":[81],"significantly":[83],"reduce":[84],"false":[85],"positives":[86],"foreground":[88],"detection":[90],"segmentation,":[92],"learn":[94],"verification":[97],"model":[98],"further":[100],"classify":[101],"if":[102],"segmented":[104],"image":[105],"patch":[106],"belongs":[107],"background":[110],"or":[111],"animal.":[113],"Our":[114],"extensive":[115],"experimental":[116],"performance":[119],"comparisons":[120],"over":[121],"diverse":[123],"set":[124],"camera-trap":[127],"data,":[128],"well":[130],"new":[133],"Change":[134],"Detection":[135],"2014":[136],"benchmark":[137],"dataset,":[138],"demonstrate":[139],"that":[140],"proposed":[142],"framework":[143],"outperforms":[144],"various":[145],"state-of-the-art":[146],"algorithms":[147],"has":[149],"capability":[151],"handle":[153],"even":[154],"most":[156],"objects":[158],"wide":[161],"variety":[162],"sequences.":[165]},"counts_by_year":[{"year":2023,"cited_by_count":1},{"year":2021,"cited_by_count":2},{"year":2020,"cited_by_count":1},{"year":2019,"cited_by_count":4},{"year":2018,"cited_by_count":5},{"year":2017,"cited_by_count":2},{"year":2016,"cited_by_count":1}],"updated_date":"2026-07-29T14:22:42.915294","created_date":"2025-10-10T00:00:00"}
