{"id":"https://openalex.org/W3024645516","doi":"https://doi.org/10.1109/wacv45572.2020.9093522","title":"Color Composition Similarity and Its Application in Fine-grained Similarity","display_name":"Color Composition Similarity and Its Application in Fine-grained Similarity","publication_year":2020,"publication_date":"2020-03-01","ids":{"openalex":"https://openalex.org/W3024645516","doi":"https://doi.org/10.1109/wacv45572.2020.9093522","mag":"3024645516"},"language":"en","primary_location":{"id":"doi:10.1109/wacv45572.2020.9093522","is_oa":false,"landing_page_url":"https://doi.org/10.1109/wacv45572.2020.9093522","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2020 IEEE Winter Conference on Applications of Computer Vision (WACV)","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/A5113918183","display_name":"Mai Lan Ha","orcid":null},"institutions":[{"id":"https://openalex.org/I206895457","display_name":"University of Siegen","ror":"https://ror.org/02azyry73","country_code":"DE","type":"education","lineage":["https://openalex.org/I206895457"]}],"countries":["DE"],"is_corresponding":true,"raw_author_name":"Mai Lan Ha","raw_affiliation_strings":["University of Siegen"],"affiliations":[{"raw_affiliation_string":"University of Siegen","institution_ids":["https://openalex.org/I206895457"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5020653666","display_name":"Vlad Hosu","orcid":"https://orcid.org/0000-0001-7070-5688"},"institutions":[{"id":"https://openalex.org/I189712700","display_name":"University of Konstanz","ror":"https://ror.org/0546hnb39","country_code":"DE","type":"education","lineage":["https://openalex.org/I189712700"]}],"countries":["DE"],"is_corresponding":false,"raw_author_name":"Vlad Hosu","raw_affiliation_strings":["University of Konstanz"],"affiliations":[{"raw_affiliation_string":"University of Konstanz","institution_ids":["https://openalex.org/I189712700"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5010209213","display_name":"Volker Blanz","orcid":null},"institutions":[{"id":"https://openalex.org/I206895457","display_name":"University of Siegen","ror":"https://ror.org/02azyry73","country_code":"DE","type":"education","lineage":["https://openalex.org/I206895457"]}],"countries":["DE"],"is_corresponding":false,"raw_author_name":"Volker Blanz","raw_affiliation_strings":["University of Siegen"],"affiliations":[{"raw_affiliation_string":"University of Siegen","institution_ids":["https://openalex.org/I206895457"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5113918183"],"corresponding_institution_ids":["https://openalex.org/I206895457"],"apc_list":null,"apc_paid":null,"fwci":0.0977,"has_fulltext":false,"cited_by_count":1,"citation_normalized_percentile":{"value":0.372723,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":93},"biblio":{"volume":"11","issue":null,"first_page":"2548","last_page":"2557"},"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":0.996999979019165,"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":0.996999979019165,"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":0.9914000034332275,"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/T11019","display_name":"Image Enhancement Techniques","score":0.9897000193595886,"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/similarity","display_name":"Similarity (geometry)","score":0.7995051741600037},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.7667131423950195},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6718665361404419},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.5997917056083679},{"id":"https://openalex.org/keywords/benchmark","display_name":"Benchmark (surveying)","score":0.5386611819267273},{"id":"https://openalex.org/keywords/metric","display_name":"Metric (unit)","score":0.4894489347934723},{"id":"https://openalex.org/keywords/histogram","display_name":"Histogram","score":0.4574403762817383},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.32811954617500305},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.157223641872406}],"concepts":[{"id":"https://openalex.org/C103278499","wikidata":"https://www.wikidata.org/wiki/Q254465","display_name":"Similarity (geometry)","level":3,"score":0.7995051741600037},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7667131423950195},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6718665361404419},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.5997917056083679},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.5386611819267273},{"id":"https://openalex.org/C176217482","wikidata":"https://www.wikidata.org/wiki/Q860554","display_name":"Metric (unit)","level":2,"score":0.4894489347934723},{"id":"https://openalex.org/C53533937","wikidata":"https://www.wikidata.org/wiki/Q185020","display_name":"Histogram","level":3,"score":0.4574403762817383},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.32811954617500305},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.157223641872406},{"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},{"id":"https://openalex.org/C162324750","wikidata":"https://www.wikidata.org/wiki/Q8134","display_name":"Economics","level":0,"score":0.0},{"id":"https://openalex.org/C21547014","wikidata":"https://www.wikidata.org/wiki/Q1423657","display_name":"Operations management","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/wacv45572.2020.9093522","is_oa":false,"landing_page_url":"https://doi.org/10.1109/wacv45572.2020.9093522","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2020 IEEE Winter Conference on Applications of Computer Vision (WACV)","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":39,"referenced_works":["https://openalex.org/W1532499126","https://openalex.org/W1556531089","https://openalex.org/W1686810756","https://openalex.org/W1964169577","https://openalex.org/W1971812743","https://openalex.org/W1975517671","https://openalex.org/W2000512760","https://openalex.org/W2021354639","https://openalex.org/W2031489346","https://openalex.org/W2033379850","https://openalex.org/W2085660690","https://openalex.org/W2093848332","https://openalex.org/W2097053051","https://openalex.org/W2100799972","https://openalex.org/W2108598243","https://openalex.org/W2109506130","https://openalex.org/W2118153703","https://openalex.org/W2120504013","https://openalex.org/W2125560515","https://openalex.org/W2125574651","https://openalex.org/W2136705357","https://openalex.org/W2150969685","https://openalex.org/W2151103935","https://openalex.org/W2151509460","https://openalex.org/W2156843740","https://openalex.org/W2162762921","https://openalex.org/W2163605009","https://openalex.org/W2294130536","https://openalex.org/W2296249689","https://openalex.org/W2345254477","https://openalex.org/W2467570466","https://openalex.org/W2585034924","https://openalex.org/W2892235646","https://openalex.org/W2962785568","https://openalex.org/W6631637103","https://openalex.org/W6637373629","https://openalex.org/W6678800043","https://openalex.org/W6684191040","https://openalex.org/W6732513927"],"related_works":["https://openalex.org/W2378211422","https://openalex.org/W4321353415","https://openalex.org/W2745001401","https://openalex.org/W2130974462","https://openalex.org/W2028665553","https://openalex.org/W2086519370","https://openalex.org/W972276598","https://openalex.org/W1482441085","https://openalex.org/W2966858528","https://openalex.org/W2151687600"],"abstract_inverted_index":{"Assessing":[0],"visual":[1,10,36,162,188],"similarity":[2,37,50,64,83,105,129,155,189],"in-the-wild,":[3],"a":[4,13,31,101,119,127,141,199],"core":[5],"ability":[6],"of":[7,21,68,114,175],"the":[8,66,81,112,123,168],"human":[9],"system,":[11],"is":[12,85,198],"challenging":[14],"problem":[15],"for":[16,80,143,147,160,202],"computer":[17],"vision":[18],"methods":[19],"because":[20],"its":[22,44,57,115,192],"subjective":[23,108],"nature":[24],"and":[25,55,70,88,131,156,195],"limited":[26],"annotated":[27],"datasets.":[28],"We":[29,46,75,117,138],"make":[30],"stride":[32],"forward,":[33],"showing":[34],"that":[35,84,183],"can":[38],"be":[39],"better":[40],"studied":[41],"by":[42,190],"isolating":[43,191],"components.":[45],"identify":[47],"color":[48,103,128,136,145,149,154],"composition":[49,63,104],"as":[51],"an":[52,95],"important":[53],"aspect":[54],"study":[56,187],"interaction":[58],"with":[59,107],"category-level":[60],"similarity.":[61,150,163],"Color":[62],"considers":[65],"distribution":[67],"colors":[69],"their":[71],"layout":[72],"in":[73],"images.":[74],"create":[76,126],"predictive":[77],"models":[78],"accounting":[79],"global":[82,144],"beyond":[86],"pixel-based":[87],"patch-based,":[89],"or":[90],"histogram":[91],"level":[92,158],"information.":[93],"Using":[94],"active":[96],"learning":[97],"approach,":[98],"we":[99,152],"build":[100],"large-scale":[102],"dataset":[106,124],"ratings":[109],"via":[110],"crowd-sourcing,":[111],"first":[113],"kind.":[116],"train":[118],"Siamese":[120],"network":[121],"using":[122,172],"to":[125,186],"metric":[130],"descriptors":[132,146],"which":[133],"outperform":[134],"existing":[135],"descriptors.":[137],"also":[139],"provide":[140],"benchmark":[142],"perceptual":[148],"Finally,":[151],"combine":[153],"category":[157],"features":[159],"fine-grained":[161],"Our":[164],"proposed":[165],"model":[166],"surpasses":[167],"state-of-the-art":[169],"performance":[170],"while":[171],"three":[173],"orders":[174],"magnitude":[176],"less":[177],"training":[178],"data.":[179],"The":[180],"results":[181],"suggest":[182],"our":[184],"proposal":[185],"components,":[193],"modeling":[194],"combining":[196],"them":[197],"promising":[200],"paradigm":[201],"further":[203],"development.":[204]},"counts_by_year":[{"year":2021,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
