{"id":"https://openalex.org/W2124003413","doi":"https://doi.org/10.1109/icme.2002.1035810","title":"Event clustering of consumer pictures using foreground/background segmentation","display_name":"Event clustering of consumer pictures using foreground/background segmentation","publication_year":2003,"publication_date":"2003-06-25","ids":{"openalex":"https://openalex.org/W2124003413","doi":"https://doi.org/10.1109/icme.2002.1035810","mag":"2124003413"},"language":"en","primary_location":{"id":"doi:10.1109/icme.2002.1035810","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icme.2002.1035810","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings. IEEE International Conference on Multimedia and Expo","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/A5051767562","display_name":"Alexander C. Loui","orcid":"https://orcid.org/0000-0002-7427-1503"},"institutions":[{"id":"https://openalex.org/I175669267","display_name":"Carestream (United States)","ror":"https://ror.org/048m16q57","country_code":"US","type":"company","lineage":["https://openalex.org/I175669267"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"A. Loui","raw_affiliation_strings":["Electronic Imaging Products R&D, Eastman Kodak (Japan) Limited, Rochester, NY, USA","Electron. Imaging Products, R&D, Eastman Kodak Co., Rochester, NY, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Electronic Imaging Products R&D, Eastman Kodak (Japan) Limited, Rochester, NY, USA","institution_ids":["https://openalex.org/I175669267"]},{"raw_affiliation_string":"Electron. Imaging Products, R&D, Eastman Kodak Co., Rochester, NY, USA","institution_ids":["https://openalex.org/I175669267"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5022027085","display_name":"M. Jeanson","orcid":null},"institutions":[{"id":"https://openalex.org/I175669267","display_name":"Carestream (United States)","ror":"https://ror.org/048m16q57","country_code":"US","type":"company","lineage":["https://openalex.org/I175669267"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"M. Jeanson","raw_affiliation_strings":["Electronic Imaging Products R&D, Eastman Kodak (Japan) Limited, Rochester, NY, USA","Electron. Imaging Products, R&D, Eastman Kodak Co., Rochester, NY, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Electronic Imaging Products R&D, Eastman Kodak (Japan) Limited, Rochester, NY, USA","institution_ids":["https://openalex.org/I175669267"]},{"raw_affiliation_string":"Electron. Imaging Products, R&D, Eastman Kodak Co., Rochester, NY, USA","institution_ids":["https://openalex.org/I175669267"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":2,"citation_normalized_percentile":{"value":0.18846458,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":"2670","issue":null,"first_page":"429","last_page":"432"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10824","display_name":"Image Retrieval and Classification Techniques","score":0.9995999932289124,"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/T10824","display_name":"Image Retrieval and Classification Techniques","score":0.9995999932289124,"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":0.9991999864578247,"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/T11439","display_name":"Video Analysis and Summarization","score":0.9986000061035156,"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/segmentation","display_name":"Segmentation","score":0.7767372131347656},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7707982063293457},{"id":"https://openalex.org/keywords/cluster-analysis","display_name":"Cluster analysis","score":0.7479461431503296},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6571481227874756},{"id":"https://openalex.org/keywords/segmentation-based-object-categorization","display_name":"Segmentation-based object categorization","score":0.6308059692382812},{"id":"https://openalex.org/keywords/benchmark","display_name":"Benchmark (surveying)","score":0.609291672706604},{"id":"https://openalex.org/keywords/image-segmentation","display_name":"Image segmentation","score":0.6084675192832947},{"id":"https://openalex.org/keywords/block","display_name":"Block (permutation group theory)","score":0.593420147895813},{"id":"https://openalex.org/keywords/event","display_name":"Event (particle physics)","score":0.5881854295730591},{"id":"https://openalex.org/keywords/scale-space-segmentation","display_name":"Scale-space segmentation","score":0.5277324914932251},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.5121421813964844},{"id":"https://openalex.org/keywords/context","display_name":"Context (archaeology)","score":0.5067721009254456},{"id":"https://openalex.org/keywords/ground-truth","display_name":"Ground truth","score":0.436436265707016},{"id":"https://openalex.org/keywords/feature-extraction","display_name":"Feature extraction","score":0.43355658650398254},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.37389904260635376},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.10909777879714966},{"id":"https://openalex.org/keywords/geography","display_name":"Geography","score":0.1051071286201477}],"concepts":[{"id":"https://openalex.org/C89600930","wikidata":"https://www.wikidata.org/wiki/Q1423946","display_name":"Segmentation","level":2,"score":0.7767372131347656},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7707982063293457},{"id":"https://openalex.org/C73555534","wikidata":"https://www.wikidata.org/wiki/Q622825","display_name":"Cluster analysis","level":2,"score":0.7479461431503296},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6571481227874756},{"id":"https://openalex.org/C25694479","wikidata":"https://www.wikidata.org/wiki/Q7446278","display_name":"Segmentation-based object categorization","level":5,"score":0.6308059692382812},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.609291672706604},{"id":"https://openalex.org/C124504099","wikidata":"https://www.wikidata.org/wiki/Q56933","display_name":"Image segmentation","level":3,"score":0.6084675192832947},{"id":"https://openalex.org/C2777210771","wikidata":"https://www.wikidata.org/wiki/Q4927124","display_name":"Block (permutation group theory)","level":2,"score":0.593420147895813},{"id":"https://openalex.org/C2779662365","wikidata":"https://www.wikidata.org/wiki/Q5416694","display_name":"Event (particle physics)","level":2,"score":0.5881854295730591},{"id":"https://openalex.org/C65885262","wikidata":"https://www.wikidata.org/wiki/Q7429708","display_name":"Scale-space segmentation","level":4,"score":0.5277324914932251},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.5121421813964844},{"id":"https://openalex.org/C2779343474","wikidata":"https://www.wikidata.org/wiki/Q3109175","display_name":"Context (archaeology)","level":2,"score":0.5067721009254456},{"id":"https://openalex.org/C146849305","wikidata":"https://www.wikidata.org/wiki/Q370766","display_name":"Ground truth","level":2,"score":0.436436265707016},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.43355658650398254},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.37389904260635376},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.10909777879714966},{"id":"https://openalex.org/C205649164","wikidata":"https://www.wikidata.org/wiki/Q1071","display_name":"Geography","level":0,"score":0.1051071286201477},{"id":"https://openalex.org/C13280743","wikidata":"https://www.wikidata.org/wiki/Q131089","display_name":"Geodesy","level":1,"score":0.0},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0},{"id":"https://openalex.org/C2524010","wikidata":"https://www.wikidata.org/wiki/Q8087","display_name":"Geometry","level":1,"score":0.0},{"id":"https://openalex.org/C62520636","wikidata":"https://www.wikidata.org/wiki/Q944","display_name":"Quantum mechanics","level":1,"score":0.0},{"id":"https://openalex.org/C166957645","wikidata":"https://www.wikidata.org/wiki/Q23498","display_name":"Archaeology","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/icme.2002.1035810","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icme.2002.1035810","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings. IEEE International Conference on Multimedia and Expo","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"score":0.44999998807907104,"display_name":"Peace, Justice and strong institutions","id":"https://metadata.un.org/sdg/16"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":11,"referenced_works":["https://openalex.org/W1524408959","https://openalex.org/W1599851310","https://openalex.org/W2032301237","https://openalex.org/W2056325151","https://openalex.org/W2120603077","https://openalex.org/W2157203292","https://openalex.org/W2978587669","https://openalex.org/W3182456284","https://openalex.org/W6677585276","https://openalex.org/W6683400641","https://openalex.org/W6798334081"],"related_works":["https://openalex.org/W3144569342","https://openalex.org/W2945274617","https://openalex.org/W2185902295","https://openalex.org/W2103507220","https://openalex.org/W2055202857","https://openalex.org/W4205800335","https://openalex.org/W2371519352","https://openalex.org/W2386644571","https://openalex.org/W2372421320","https://openalex.org/W2901890255"],"abstract_inverted_index":{"This":[0,67],"paper":[1],"describes":[2],"a":[3,59,111],"new":[4],"algorithm":[5,62,124],"to":[6,32,51,100,140],"classify":[7],"consumer":[8],"photographs":[9],"into":[10],"different":[11],"events":[12],"when":[13],"date":[14],"and":[15,46,135],"time":[16],"information":[17,23],"is":[18],"not":[19],"available.":[20],"Without":[21],"any":[22],"about":[24],"the":[25,28,35,91,94,119,144],"context":[26],"of":[27,48,93],"pictures,":[29],"we":[30,56,108],"have":[31,57],"rely":[33],"on":[34,64,105,133],"image":[36],"content.":[37],"Our":[38],"approach":[39],"involves":[40],"using":[41],"an":[42],"efficient":[43],"segmentation":[44,61,69,114,134],"scheme":[45,115],"extraction":[47],"low-level":[49],"features":[50],"detect":[52],"event":[53,122],"boundaries.":[54],"Specifically,":[55],"developed":[58],"foreground/background":[60],"based":[63],"block-based":[65,113,121],"clustering.":[66],"block":[68],"provides":[70],"less":[71],"precision,":[72],"but":[73],"still":[74],"gives":[75],"good":[76],"results":[77,142],"with":[78,90],"low":[79],"computation":[80],"cost.":[81],"A":[82],"third-party":[83],"ground":[84],"truth":[85],"database":[86],"has":[87],"been":[88],"created":[89],"help":[92],"Human":[95],"Factors":[96],"Laboratory":[97],"at":[98],"Kodak,":[99],"benchmark":[101],"our":[102],"approaches.":[103],"Based":[104],"these":[106],"results,":[107],"concluded":[109],"that":[110,129],"simple":[112],"performed":[116],"better":[117,141],"than":[118],"original":[120],"clustering":[123],"without":[125],"segmentation.":[126],"We":[127],"believe":[128],"many":[130],"improvements,":[131],"especially":[132],"feature":[136],"extraction,":[137],"should":[138],"lead":[139],"in":[143],"future.":[145]},"counts_by_year":[],"updated_date":"2026-06-11T09:08:48.828518","created_date":"2025-10-10T00:00:00"}
