{"id":"https://openalex.org/W2092892517","doi":"https://doi.org/10.1145/1096601.1096632","title":"A statistical method for binary classification of images","display_name":"A statistical method for binary classification of images","publication_year":2005,"publication_date":"2005-11-02","ids":{"openalex":"https://openalex.org/W2092892517","doi":"https://doi.org/10.1145/1096601.1096632","mag":"2092892517"},"language":"en","primary_location":{"id":"doi:10.1145/1096601.1096632","is_oa":false,"landing_page_url":"https://doi.org/10.1145/1096601.1096632","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 2005 ACM symposium on Document engineering","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/A5022519243","display_name":"Steven J. Simske","orcid":null},"institutions":[{"id":"https://openalex.org/I1324840837","display_name":"Hewlett-Packard (United States)","ror":"https://ror.org/059rn9488","country_code":"US","type":"company","lineage":["https://openalex.org/I1324840837"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Steven J. Simske","raw_affiliation_strings":["Hewlett-Packard Labs, Fort Collins, CO","Hewlett-Packard Labs, Fort Collins, CO#TAB#"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Hewlett-Packard Labs, Fort Collins, CO","institution_ids":["https://openalex.org/I1324840837"]},{"raw_affiliation_string":"Hewlett-Packard Labs, Fort Collins, CO#TAB#","institution_ids":["https://openalex.org/I1324840837"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5079632219","display_name":"Dalong Li","orcid":"https://orcid.org/0000-0002-6953-150X"},"institutions":[{"id":"https://openalex.org/I130701444","display_name":"Georgia Institute of Technology","ror":"https://ror.org/01zkghx44","country_code":"US","type":"education","lineage":["https://openalex.org/I130701444"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Dalong Li","raw_affiliation_strings":["Georgia Institute of Technology, Atlanta, GA","Georgia Institute of Technology Atlanta, GA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Georgia Institute of Technology, Atlanta, GA","institution_ids":["https://openalex.org/I130701444"]},{"raw_affiliation_string":"Georgia Institute of Technology Atlanta, GA","institution_ids":["https://openalex.org/I130701444"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5009618585","display_name":"Jason S. Aronoff","orcid":null},"institutions":[{"id":"https://openalex.org/I1324840837","display_name":"Hewlett-Packard (United States)","ror":"https://ror.org/059rn9488","country_code":"US","type":"company","lineage":["https://openalex.org/I1324840837"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Jason S. Aronoff","raw_affiliation_strings":["Hewlett-Packard Labs, Fort Collins, CO","Hewlett-Packard Labs, Fort Collins, CO#TAB#"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Hewlett-Packard Labs, Fort Collins, CO","institution_ids":["https://openalex.org/I1324840837"]},{"raw_affiliation_string":"Hewlett-Packard Labs, Fort Collins, CO#TAB#","institution_ids":["https://openalex.org/I1324840837"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.2565,"has_fulltext":false,"cited_by_count":8,"citation_normalized_percentile":{"value":0.48068995,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":91,"max":99},"biblio":{"volume":null,"issue":null,"first_page":"127","last_page":"129"},"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":0.9968000054359436,"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/T13114","display_name":"Image Processing Techniques and Applications","score":0.9889000058174133,"subfield":{"id":"https://openalex.org/subfields/2214","display_name":"Media Technology"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.733761191368103},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.7302243113517761},{"id":"https://openalex.org/keywords/weighting","display_name":"Weighting","score":0.7107486724853516},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6844119429588318},{"id":"https://openalex.org/keywords/classifier","display_name":"Classifier (UML)","score":0.6775294542312622},{"id":"https://openalex.org/keywords/contextual-image-classification","display_name":"Contextual image classification","score":0.6201863288879395},{"id":"https://openalex.org/keywords/binary-classification","display_name":"Binary classification","score":0.6130275130271912},{"id":"https://openalex.org/keywords/binary-number","display_name":"Binary number","score":0.5001399517059326},{"id":"https://openalex.org/keywords/random-subspace-method","display_name":"Random subspace method","score":0.48464637994766235},{"id":"https://openalex.org/keywords/training-set","display_name":"Training set","score":0.42557889223098755},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.40087786316871643},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.244947612285614},{"id":"https://openalex.org/keywords/support-vector-machine","display_name":"Support vector machine","score":0.23830324411392212}],"concepts":[{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.733761191368103},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.7302243113517761},{"id":"https://openalex.org/C183115368","wikidata":"https://www.wikidata.org/wiki/Q856577","display_name":"Weighting","level":2,"score":0.7107486724853516},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6844119429588318},{"id":"https://openalex.org/C95623464","wikidata":"https://www.wikidata.org/wiki/Q1096149","display_name":"Classifier (UML)","level":2,"score":0.6775294542312622},{"id":"https://openalex.org/C75294576","wikidata":"https://www.wikidata.org/wiki/Q5165192","display_name":"Contextual image classification","level":3,"score":0.6201863288879395},{"id":"https://openalex.org/C66905080","wikidata":"https://www.wikidata.org/wiki/Q17005494","display_name":"Binary classification","level":3,"score":0.6130275130271912},{"id":"https://openalex.org/C48372109","wikidata":"https://www.wikidata.org/wiki/Q3913","display_name":"Binary number","level":2,"score":0.5001399517059326},{"id":"https://openalex.org/C106135958","wikidata":"https://www.wikidata.org/wiki/Q7291993","display_name":"Random subspace method","level":3,"score":0.48464637994766235},{"id":"https://openalex.org/C51632099","wikidata":"https://www.wikidata.org/wiki/Q3985153","display_name":"Training set","level":2,"score":0.42557889223098755},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.40087786316871643},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.244947612285614},{"id":"https://openalex.org/C12267149","wikidata":"https://www.wikidata.org/wiki/Q282453","display_name":"Support vector machine","level":2,"score":0.23830324411392212},{"id":"https://openalex.org/C94375191","wikidata":"https://www.wikidata.org/wiki/Q11205","display_name":"Arithmetic","level":1,"score":0.0},{"id":"https://openalex.org/C71924100","wikidata":"https://www.wikidata.org/wiki/Q11190","display_name":"Medicine","level":0,"score":0.0},{"id":"https://openalex.org/C126838900","wikidata":"https://www.wikidata.org/wiki/Q77604","display_name":"Radiology","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/1096601.1096632","is_oa":false,"landing_page_url":"https://doi.org/10.1145/1096601.1096632","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 2005 ACM symposium on Document engineering","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"score":0.7400000095367432,"id":"https://metadata.un.org/sdg/11","display_name":"Sustainable cities and communities"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":4,"referenced_works":["https://openalex.org/W1988790447","https://openalex.org/W2112442216","https://openalex.org/W2159169014","https://openalex.org/W3119651796"],"related_works":["https://openalex.org/W2180954594","https://openalex.org/W2004617984","https://openalex.org/W2910954186","https://openalex.org/W202723009","https://openalex.org/W4205999209","https://openalex.org/W2964083560","https://openalex.org/W2137625224","https://openalex.org/W2444525338","https://openalex.org/W4288570796","https://openalex.org/W3117807895"],"abstract_inverted_index":{"The":[0,75],"classification":[1,22,159],"of":[2,27,65,78,80,111,115,149,155],"documents":[3],"with":[4],"sparse":[5],"text,":[6],"and":[7,43,67,104],"video":[8],"analysis,":[9],"relies":[10],"on":[11,87],"accurate":[12,140],"image":[13,58],"classification.":[14,74],"We":[15],"herein":[16],"present":[17],"a":[18,63,71,88,146],"method":[19,160],"for":[20,126],"binary":[21],"that":[23,138],"accommodates":[24],"any":[25],"number":[26],"individual":[28,31],"classifiers.":[29],"Each":[30],"classifier":[32,141],"is":[33,47,98],"defined":[34],"by":[35],"the":[36,108,112,127,153,156],"critical":[37],"point":[38],"between":[39],"its":[40,44,51],"two":[41],"means,":[42],"relative":[45],"weighting":[46],"inversely":[48],"proportional":[49],"to":[50],"expected":[52],"error":[53],"rate.":[54],"Using":[55],"10":[56,82],"simple":[57,150],"analysis":[59],"metrics,":[60],"we":[61],"distinguish":[62],"set":[64,114,129],"\"natural\"":[66],"\"city\"":[68],"scenes,":[69],"providing":[70],"\"semantically":[72],"meaningful\"":[73],"optimal":[76],"combination":[77],"5":[79],"these":[81],"classifiers":[83,116,151],"provides":[84],"85.8%":[85],"accuracy":[86,110],"small":[89],"(120":[90],"image)":[91],"feasibility":[92,96],"corpus.":[93],"When":[94],"this":[95],"corpus":[97],"then":[99],"split":[100],"into":[101],"half":[102,105],"training":[103,131],"testing":[106],"images,":[107],"mean":[109],"optimum":[113],"was":[117,124,133],"81.7%.":[118],"Accuracy":[119],"as":[120,122],"high":[121],"90%":[123],"obtained":[125],"test":[128],"when":[130],"percentage":[132],"increased.":[134],"These":[135],"results":[136],"demonstrate":[137],"an":[139],"can":[142],"be":[143],"constructed":[144],"from":[145],"large":[147],"pool":[148],"through":[152],"use":[154],"statistical":[157],"(\"Normal\")":[158],"described":[161],"herein.":[162]},"counts_by_year":[{"year":2025,"cited_by_count":1},{"year":2013,"cited_by_count":6}],"updated_date":"2026-07-29T14:22:42.915294","created_date":"2025-10-10T00:00:00"}
