{"id":"https://openalex.org/W2107131697","doi":"https://doi.org/10.1145/2425836.2425874","title":"Scene classification based on category-specific representations created through prototype feature selection","display_name":"Scene classification based on category-specific representations created through prototype feature selection","publication_year":2012,"publication_date":"2012-11-26","ids":{"openalex":"https://openalex.org/W2107131697","doi":"https://doi.org/10.1145/2425836.2425874","mag":"2107131697"},"language":"en","primary_location":{"id":"doi:10.1145/2425836.2425874","is_oa":false,"landing_page_url":"https://doi.org/10.1145/2425836.2425874","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 27th Conference on Image and Vision Computing New Zealand","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/A5100955332","display_name":"Shuang Bai","orcid":"https://orcid.org/0009-0003-8376-5964"},"institutions":[{"id":"https://openalex.org/I60134161","display_name":"Nagoya University","ror":"https://ror.org/04chrp450","country_code":"JP","type":"education","lineage":["https://openalex.org/I60134161"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"Shuang Bai","raw_affiliation_strings":["Nagoya University, Nagoya-shi, Japan"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Nagoya University, Nagoya-shi, Japan","institution_ids":["https://openalex.org/I60134161"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5102357809","display_name":"Tetsuya Matsumoto","orcid":null},"institutions":[{"id":"https://openalex.org/I60134161","display_name":"Nagoya University","ror":"https://ror.org/04chrp450","country_code":"JP","type":"education","lineage":["https://openalex.org/I60134161"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"Tetsuya Matsumoto","raw_affiliation_strings":["Nagoya University, Nagoya-shi, Japan"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Nagoya University, Nagoya-shi, Japan","institution_ids":["https://openalex.org/I60134161"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101943961","display_name":"Hiroaki Kudo","orcid":"https://orcid.org/0009-0006-4886-4969"},"institutions":[{"id":"https://openalex.org/I60134161","display_name":"Nagoya University","ror":"https://ror.org/04chrp450","country_code":"JP","type":"education","lineage":["https://openalex.org/I60134161"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"Hiroaki Kudo","raw_affiliation_strings":["Nagoya University, Nagoya-shi, Japan"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Nagoya University, Nagoya-shi, Japan","institution_ids":["https://openalex.org/I60134161"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5105934558","display_name":"Noboru Ohnishi","orcid":null},"institutions":[{"id":"https://openalex.org/I60134161","display_name":"Nagoya University","ror":"https://ror.org/04chrp450","country_code":"JP","type":"education","lineage":["https://openalex.org/I60134161"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"Noboru Ohnishi","raw_affiliation_strings":["Nagoya University, Nagoya-shi, Japan"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Nagoya University, Nagoya-shi, Japan","institution_ids":["https://openalex.org/I60134161"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5101432602","display_name":"Yoshinori Takeuchi","orcid":"https://orcid.org/0000-0001-6936-3596"},"institutions":[{"id":"https://openalex.org/I51803142","display_name":"Daido University","ror":"https://ror.org/02cntp233","country_code":"JP","type":"education","lineage":["https://openalex.org/I51803142"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"Yoshinori Takeuchi","raw_affiliation_strings":["Daido University, Nagoya-shi, Japan","Daido University, Nagoya-shi, Japan#TAB#"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Daido University, Nagoya-shi, Japan","institution_ids":["https://openalex.org/I51803142"]},{"raw_affiliation_string":"Daido University, Nagoya-shi, Japan#TAB#","institution_ids":["https://openalex.org/I51803142"]}]}],"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":1,"citation_normalized_percentile":{"value":0.21327194,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":93},"biblio":{"volume":null,"issue":null,"first_page":"174","last_page":"179"},"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":1.0,"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":1.0,"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":0.9998000264167786,"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/T10689","display_name":"Remote-Sensing Image Classification","score":0.995199978351593,"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":0.8092478513717651},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.7228028178215027},{"id":"https://openalex.org/keywords/classifier","display_name":"Classifier (UML)","score":0.7113980054855347},{"id":"https://openalex.org/keywords/support-vector-machine","display_name":"Support vector machine","score":0.6402704119682312},{"id":"https://openalex.org/keywords/contextual-image-classification","display_name":"Contextual image classification","score":0.6201324462890625},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6105407476425171},{"id":"https://openalex.org/keywords/salient","display_name":"Salient","score":0.5996689796447754},{"id":"https://openalex.org/keywords/pooling","display_name":"Pooling","score":0.5798976421356201},{"id":"https://openalex.org/keywords/feature-selection","display_name":"Feature selection","score":0.48532834649086},{"id":"https://openalex.org/keywords/probabilistic-classification","display_name":"Probabilistic classification","score":0.4357905983924866},{"id":"https://openalex.org/keywords/feature-vector","display_name":"Feature vector","score":0.41793298721313477},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.34529411792755127},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.3174405097961426},{"id":"https://openalex.org/keywords/naive-bayes-classifier","display_name":"Naive Bayes classifier","score":0.2608834505081177}],"concepts":[{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.8092478513717651},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.7228028178215027},{"id":"https://openalex.org/C95623464","wikidata":"https://www.wikidata.org/wiki/Q1096149","display_name":"Classifier (UML)","level":2,"score":0.7113980054855347},{"id":"https://openalex.org/C12267149","wikidata":"https://www.wikidata.org/wiki/Q282453","display_name":"Support vector machine","level":2,"score":0.6402704119682312},{"id":"https://openalex.org/C75294576","wikidata":"https://www.wikidata.org/wiki/Q5165192","display_name":"Contextual image classification","level":3,"score":0.6201324462890625},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6105407476425171},{"id":"https://openalex.org/C2780719617","wikidata":"https://www.wikidata.org/wiki/Q1030752","display_name":"Salient","level":2,"score":0.5996689796447754},{"id":"https://openalex.org/C70437156","wikidata":"https://www.wikidata.org/wiki/Q7228652","display_name":"Pooling","level":2,"score":0.5798976421356201},{"id":"https://openalex.org/C148483581","wikidata":"https://www.wikidata.org/wiki/Q446488","display_name":"Feature selection","level":2,"score":0.48532834649086},{"id":"https://openalex.org/C189119545","wikidata":"https://www.wikidata.org/wiki/Q5128022","display_name":"Probabilistic classification","level":4,"score":0.4357905983924866},{"id":"https://openalex.org/C83665646","wikidata":"https://www.wikidata.org/wiki/Q42139305","display_name":"Feature vector","level":2,"score":0.41793298721313477},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.34529411792755127},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.3174405097961426},{"id":"https://openalex.org/C52001869","wikidata":"https://www.wikidata.org/wiki/Q812530","display_name":"Naive Bayes classifier","level":3,"score":0.2608834505081177}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/2425836.2425874","is_oa":false,"landing_page_url":"https://doi.org/10.1145/2425836.2425874","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 27th Conference on Image and Vision Computing New Zealand","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":15,"referenced_works":["https://openalex.org/W1510526001","https://openalex.org/W1566135517","https://openalex.org/W1589362500","https://openalex.org/W2097208978","https://openalex.org/W2111868822","https://openalex.org/W2115973703","https://openalex.org/W2125029393","https://openalex.org/W2128554449","https://openalex.org/W2130660124","https://openalex.org/W2135611967","https://openalex.org/W2138826255","https://openalex.org/W2151103935","https://openalex.org/W2154301842","https://openalex.org/W2162915993","https://openalex.org/W2166742463"],"related_works":["https://openalex.org/W2953234277","https://openalex.org/W2626256601","https://openalex.org/W147410782","https://openalex.org/W2900413183","https://openalex.org/W2748667022","https://openalex.org/W3211770882","https://openalex.org/W2010629964","https://openalex.org/W4390143830","https://openalex.org/W2081219918","https://openalex.org/W4225854414"],"abstract_inverted_index":{"In":[0,13,34,132],"this":[1,38],"paper,":[2],"we":[3,44,68],"propose":[4,45],"a":[5,25,48,61,71,110,118,146,155,174,190,203],"novel":[6],"approach":[7],"to":[8,30,36,46,56,101,138,153,166,195,215,229],"coping":[9],"with":[10],"scene":[11,75,119,240,244],"classification.":[12,232],"natural":[14],"scenes,":[15],"images":[16,84,150,172],"from":[17],"different":[18],"categories":[19,241,245],"often":[20],"share":[21],"similar":[22],"components.":[23],"As":[24],"result,":[26],"it":[27,230],"is":[28,193,227,236],"difficult":[29],"distinguish":[31],"them":[32,55],"directly.":[33],"order":[35],"overcome":[37],"problem,":[39],"for":[40,73,108,117,179,231],"an":[41,93,221],"input":[42,94],"image,":[43,95,223],"create":[47,70],"set":[49,147,169,186],"of":[50,81,145,148,170,183,187,202,252],"category-specific":[51,111,143,181],"representations":[52,144],"and":[53,106,243],"use":[54],"model":[57],"the":[58,65,89,133,142,160,184,224,250,253],"image":[59,205],"as":[60,123,208],"probability":[62,191,200],"distribution":[63],"over":[64],"categories.":[66],"Specifically,":[67],"first":[69],"prototype":[72],"each":[74,102,180],"category":[76,90,103,120,140],"by":[77],"pooling":[78],"local":[79,98],"features":[80,99,116,124],"its":[82,96,129,209],"sample":[83],"together.":[85],"Then,":[86],"based":[87],"on":[88,238],"prototypes,":[91],"given":[92],"salient":[97,115],"corresponding":[100,137],"are":[104,121,151,164,206,213],"extracted":[105],"used":[107,152,165,214],"creating":[109],"representation,":[112,211],"respectively.":[113],"Here,":[114],"defined":[122],"that":[125,178],"appear":[126],"frequently":[127],"in":[128,173],"instance":[130],"images.":[131],"classifier":[134],"training":[135,149,171,188,204],"stage,":[136],"one":[139],"prototype,":[141],"train":[154,216],"multi-class":[156],"SVM":[157,162,218],"classifier.":[158,219],"Thereafter,":[159],"obtained":[161],"classifiers":[163],"classify":[167],"another":[168,217],"probabilistic":[175],"way,":[176],"so":[177],"representation":[182],"second":[185],"images,":[189],"vector":[192],"able":[194],"be":[196],"obtained.":[197],"Subsequently,":[198],"all":[199],"vectors":[201],"concatenated":[207],"final":[210],"which":[212],"For":[220],"unknown":[222],"same":[225],"process":[226],"applied":[228],"The":[233],"proposed":[234,254],"method":[235],"evaluated":[237],"datasets":[239],"8":[242],"15,":[246],"experiment":[247],"results":[248],"demonstrated":[249],"effectiveness":[251],"method.":[255]},"counts_by_year":[{"year":2021,"cited_by_count":1}],"updated_date":"2026-07-29T14:22:42.915294","created_date":"2025-10-10T00:00:00"}