{"id":"https://openalex.org/W2085774630","doi":"https://doi.org/10.1109/fcv.2013.6485485","title":"Automatic object segmentation using mean shift and growcut","display_name":"Automatic object segmentation using mean shift and growcut","publication_year":2013,"publication_date":"2013-01-01","ids":{"openalex":"https://openalex.org/W2085774630","doi":"https://doi.org/10.1109/fcv.2013.6485485","mag":"2085774630"},"language":"en","primary_location":{"id":"doi:10.1109/fcv.2013.6485485","is_oa":false,"landing_page_url":"https://doi.org/10.1109/fcv.2013.6485485","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"The 19th Korea-Japan Joint Workshop on Frontiers of Computer Vision","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/A5016465304","display_name":"Elyor Kodirov","orcid":null},"institutions":[],"countries":[],"is_corresponding":true,"raw_author_name":"Kodirov Elyor","raw_affiliation_strings":["Department of Electronics and Computer Engineering, CNU, Kwangju, South Korea","Dept. of Electron. & Comput. Eng., CNU, Kwangju, South Korea"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Electronics and Computer Engineering, CNU, Kwangju, South Korea","institution_ids":[]},{"raw_affiliation_string":"Dept. of Electron. & Comput. Eng., CNU, Kwangju, South Korea","institution_ids":[]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5070936425","display_name":"Guee-Sang Lee","orcid":"https://orcid.org/0000-0002-8756-1382"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Gueesang Lee","raw_affiliation_strings":["Department of Electronics and Computer Engineering, CNU, Kwangju, South Korea","Dept. of Electron. & Comput. Eng., CNU, Kwangju, South Korea"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Electronics and Computer Engineering, CNU, Kwangju, South Korea","institution_ids":[]},{"raw_affiliation_string":"Dept. of Electron. & Comput. Eng., CNU, Kwangju, South Korea","institution_ids":[]}]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5016465304"],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.5523,"has_fulltext":false,"cited_by_count":7,"citation_normalized_percentile":{"value":0.71974897,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":96},"biblio":{"volume":null,"issue":null,"first_page":"184","last_page":"189"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10052","display_name":"Medical Image Segmentation 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/T10052","display_name":"Medical Image Segmentation 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/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/T10824","display_name":"Image Retrieval and Classification Techniques","score":0.998199999332428,"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/mean-shift","display_name":"Mean-shift","score":0.8475334644317627},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.7913558483123779},{"id":"https://openalex.org/keywords/mahalanobis-distance","display_name":"Mahalanobis distance","score":0.7364387512207031},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7018247842788696},{"id":"https://openalex.org/keywords/image-segmentation","display_name":"Image segmentation","score":0.6991779804229736},{"id":"https://openalex.org/keywords/segmentation","display_name":"Segmentation","score":0.6762948632240295},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.6528254151344299},{"id":"https://openalex.org/keywords/scale-space-segmentation","display_name":"Scale-space segmentation","score":0.6319683194160461},{"id":"https://openalex.org/keywords/segmentation-based-object-categorization","display_name":"Segmentation-based object categorization","score":0.5771052837371826},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.5506052374839783},{"id":"https://openalex.org/keywords/computation","display_name":"Computation","score":0.5459446310997009},{"id":"https://openalex.org/keywords/object","display_name":"Object (grammar)","score":0.5109466910362244},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.16122710704803467}],"concepts":[{"id":"https://openalex.org/C48548287","wikidata":"https://www.wikidata.org/wiki/Q6803557","display_name":"Mean-shift","level":3,"score":0.8475334644317627},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7913558483123779},{"id":"https://openalex.org/C1921717","wikidata":"https://www.wikidata.org/wiki/Q1334846","display_name":"Mahalanobis distance","level":2,"score":0.7364387512207031},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7018247842788696},{"id":"https://openalex.org/C124504099","wikidata":"https://www.wikidata.org/wiki/Q56933","display_name":"Image segmentation","level":3,"score":0.6991779804229736},{"id":"https://openalex.org/C89600930","wikidata":"https://www.wikidata.org/wiki/Q1423946","display_name":"Segmentation","level":2,"score":0.6762948632240295},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.6528254151344299},{"id":"https://openalex.org/C65885262","wikidata":"https://www.wikidata.org/wiki/Q7429708","display_name":"Scale-space segmentation","level":4,"score":0.6319683194160461},{"id":"https://openalex.org/C25694479","wikidata":"https://www.wikidata.org/wiki/Q7446278","display_name":"Segmentation-based object categorization","level":5,"score":0.5771052837371826},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.5506052374839783},{"id":"https://openalex.org/C45374587","wikidata":"https://www.wikidata.org/wiki/Q12525525","display_name":"Computation","level":2,"score":0.5459446310997009},{"id":"https://openalex.org/C2781238097","wikidata":"https://www.wikidata.org/wiki/Q175026","display_name":"Object (grammar)","level":2,"score":0.5109466910362244},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.16122710704803467}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/fcv.2013.6485485","is_oa":false,"landing_page_url":"https://doi.org/10.1109/fcv.2013.6485485","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"The 19th Korea-Japan Joint Workshop on Frontiers of Computer Vision","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":25,"referenced_works":["https://openalex.org/W1536218705","https://openalex.org/W1972969203","https://openalex.org/W2006144161","https://openalex.org/W2022686119","https://openalex.org/W2056583730","https://openalex.org/W2069223956","https://openalex.org/W2100339819","https://openalex.org/W2101744728","https://openalex.org/W2108704847","https://openalex.org/W2109407305","https://openalex.org/W2121743855","https://openalex.org/W2121927366","https://openalex.org/W2121947440","https://openalex.org/W2125637308","https://openalex.org/W2127218421","https://openalex.org/W2129004009","https://openalex.org/W2132001627","https://openalex.org/W2132103241","https://openalex.org/W2132603077","https://openalex.org/W2133059825","https://openalex.org/W2149107824","https://openalex.org/W2154985693","https://openalex.org/W2539530906","https://openalex.org/W2611684114","https://openalex.org/W6631941024"],"related_works":["https://openalex.org/W3144569342","https://openalex.org/W2945274617","https://openalex.org/W2370307590","https://openalex.org/W2185902295","https://openalex.org/W2365032270","https://openalex.org/W2103507220","https://openalex.org/W4205800335","https://openalex.org/W2055202857","https://openalex.org/W2371519352","https://openalex.org/W2386644571"],"abstract_inverted_index":{"In":[0,32],"this":[1],"paper,":[2],"we":[3],"propose":[4],"an":[5],"efficient":[6],"unsupervised":[7],"object":[8],"segmentation":[9,16,86],"algorithm":[10,45],"that":[11,111],"provides":[12],"effective":[13],"and":[14,28,46,77,96],"robust":[15],"of":[17,24,90,101],"color":[18,104],"images":[19],"by":[20],"incorporating":[21],"the":[22,33,36,47,56,62,67,91,112],"advantages":[23],"Mean":[25],"Shift":[26],"(MS)":[27],"GrowCut":[29],"(GC)":[30],"methods.":[31,120],"first":[34],"stage,":[35],"image":[37,85],"is":[38,78,94],"divided":[39],"into":[40],"different":[41],"segments":[42,49,58],"using":[43,52,103],"MS":[44],"generated":[48],"are":[50,59],"labeled":[51,57],"Mahalanobis":[53],"distance.":[54],"Then,":[55],"given":[60],"to":[61],"GC":[63],"method":[64,72,93,114],"for":[65,82],"grouping":[66],"clustered":[68],"segments.":[69],"The":[70,88],"proposed":[71,92,113],"requires":[73],"low":[74],"computation":[75],"complexity":[76],"therefore":[79],"very":[80],"feasible":[81],"real":[83],"time":[84],"processing.":[87],"superiority":[89],"examined":[95],"demonstrated":[97],"through":[98],"a":[99],"number":[100],"experiments":[102],"natural":[105],"scene":[106],"images.":[107],"Experimental":[108],"result":[109],"shows":[110],"gives":[115],"better":[116],"performance":[117],"than":[118],"other":[119]},"counts_by_year":[{"year":2025,"cited_by_count":1},{"year":2022,"cited_by_count":1},{"year":2019,"cited_by_count":1},{"year":2017,"cited_by_count":2},{"year":2015,"cited_by_count":1},{"year":2014,"cited_by_count":1}],"updated_date":"2026-05-05T08:41:31.759640","created_date":"2025-10-10T00:00:00"}