{"id":"https://openalex.org/W2976922753","doi":"https://doi.org/10.2352/issn.2470-1173.2019.8.imawm-406","title":"Comparison of texture retrieval techniques using deep convolutional features","display_name":"Comparison of texture retrieval techniques using deep convolutional features","publication_year":2019,"publication_date":"2019-01-13","ids":{"openalex":"https://openalex.org/W2976922753","doi":"https://doi.org/10.2352/issn.2470-1173.2019.8.imawm-406","mag":"2976922753"},"language":"en","primary_location":{"id":"doi:10.2352/issn.2470-1173.2019.8.imawm-406","is_oa":false,"landing_page_url":"https://doi.org/10.2352/issn.2470-1173.2019.8.imawm-406","pdf_url":null,"source":{"id":"https://openalex.org/S4210227276","display_name":"Electronic Imaging","issn_l":"2470-1173","issn":["2470-1173"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Electronic Imaging","raw_type":"journal-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/A5053066123","display_name":"Augusto C. Valente","orcid":"https://orcid.org/0000-0002-5509-6420"},"institutions":[],"countries":[],"is_corresponding":true,"raw_author_name":"Augusto C Valente","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5077348775","display_name":"F\u00e1bio Vin\u00edcius Moreira Perez","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"F\u00e1bio V. M Perez","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5028029220","display_name":"Guilherme A. S. Megeto","orcid":"https://orcid.org/0000-0003-1452-5572"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Guilherme A. S Megeto","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5050376887","display_name":"Marcos H. Cascone","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Marcos H Cascone","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5066296162","display_name":"Ot\u00e1vio da Fonseca Martins Gomes","orcid":"https://orcid.org/0000-0002-6472-3625"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Otavio Gomes","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5073974102","display_name":"Thomas S. Paula","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Thomas S Paula","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5017412318","display_name":"Qian Lin","orcid":"https://orcid.org/0000-0002-6342-3460"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Qian Lin","raw_affiliation_strings":[],"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":7,"corresponding_author_ids":["https://openalex.org/A5053066123"],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.2024,"has_fulltext":false,"cited_by_count":2,"citation_normalized_percentile":{"value":0.5421858,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":94},"biblio":{"volume":"31","issue":"8","first_page":"406","last_page":"1"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10824","display_name":"Image Retrieval and Classification Techniques","score":0.9994000196456909,"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":0.9994000196456909,"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":0.9973999857902527,"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":0.9272000193595886,"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/artificial-intelligence","display_name":"Artificial intelligence","score":0.7494668364524841},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.7103715538978577},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6897259950637817},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.6355571746826172},{"id":"https://openalex.org/keywords/pooling","display_name":"Pooling","score":0.6094465255737305},{"id":"https://openalex.org/keywords/image-retrieval","display_name":"Image retrieval","score":0.5570223331451416},{"id":"https://openalex.org/keywords/similarity","display_name":"Similarity (geometry)","score":0.530222475528717},{"id":"https://openalex.org/keywords/benchmark","display_name":"Benchmark (surveying)","score":0.5288066864013672},{"id":"https://openalex.org/keywords/cosine-similarity","display_name":"Cosine similarity","score":0.5165785551071167},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.4894029200077057},{"id":"https://openalex.org/keywords/feature-extraction","display_name":"Feature extraction","score":0.4342968165874481},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.2893787622451782},{"id":"https://openalex.org/keywords/geography","display_name":"Geography","score":0.09656906127929688},{"id":"https://openalex.org/keywords/cartography","display_name":"Cartography","score":0.07528042793273926}],"concepts":[{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7494668364524841},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.7103715538978577},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6897259950637817},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.6355571746826172},{"id":"https://openalex.org/C70437156","wikidata":"https://www.wikidata.org/wiki/Q7228652","display_name":"Pooling","level":2,"score":0.6094465255737305},{"id":"https://openalex.org/C1667742","wikidata":"https://www.wikidata.org/wiki/Q10927554","display_name":"Image retrieval","level":3,"score":0.5570223331451416},{"id":"https://openalex.org/C103278499","wikidata":"https://www.wikidata.org/wiki/Q254465","display_name":"Similarity (geometry)","level":3,"score":0.530222475528717},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.5288066864013672},{"id":"https://openalex.org/C2780762811","wikidata":"https://www.wikidata.org/wiki/Q1784941","display_name":"Cosine similarity","level":3,"score":0.5165785551071167},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.4894029200077057},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.4342968165874481},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.2893787622451782},{"id":"https://openalex.org/C205649164","wikidata":"https://www.wikidata.org/wiki/Q1071","display_name":"Geography","level":0,"score":0.09656906127929688},{"id":"https://openalex.org/C58640448","wikidata":"https://www.wikidata.org/wiki/Q42515","display_name":"Cartography","level":1,"score":0.07528042793273926},{"id":"https://openalex.org/C41895202","wikidata":"https://www.wikidata.org/wiki/Q8162","display_name":"Linguistics","level":1,"score":0.0},{"id":"https://openalex.org/C138885662","wikidata":"https://www.wikidata.org/wiki/Q5891","display_name":"Philosophy","level":0,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.2352/issn.2470-1173.2019.8.imawm-406","is_oa":false,"landing_page_url":"https://doi.org/10.2352/issn.2470-1173.2019.8.imawm-406","pdf_url":null,"source":{"id":"https://openalex.org/S4210227276","display_name":"Electronic Imaging","issn_l":"2470-1173","issn":["2470-1173"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Electronic Imaging","raw_type":"journal-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":["https://openalex.org/W2389818373","https://openalex.org/W2220831889","https://openalex.org/W4312683641","https://openalex.org/W3027421045","https://openalex.org/W2576320324","https://openalex.org/W2980386803","https://openalex.org/W3215994059","https://openalex.org/W2319823519","https://openalex.org/W4206798987","https://openalex.org/W2801158176"],"abstract_inverted_index":{"Considering":[0],"the":[1,8,37,108,131,147,155,162],"complexity":[2],"of":[3,11,39,164],"a":[4,16,21,27,115,174,182],"multimedia":[5],"society":[6],"and":[7,48,65,76,93,127],"subjective":[9],"task":[10],"describing":[12],"images":[13,35,64],"with":[14,36,120,167,181],"words,":[15],"visual":[17],"search":[18],"application":[19,32],"is":[20,144],"valuable":[22],"tool.":[23],"This":[24],"work":[25],"implements":[26],"Content-Based":[28],"Image":[29],"Retrieval":[30],"(CBIR)":[31],"for":[33,138],"texture":[34,91],"goal":[38],"comparing":[40],"three":[41],"deep":[42],"convolutional":[43,87],"neural":[44],"networks":[45],"(VGG-16,":[46],"ResNet-50,":[47],"DenseNet-161),":[49],"used":[50],"as":[51,114],"image":[52,139],"descriptors":[53],"by":[54],"extracting":[55,99],"global":[56,82],"features":[57,100],"from":[58,101,112],"images.":[59],"For":[60],"measuring":[61],"similarity":[62],"among":[63],"ranking":[66],"them,":[67],"we":[68],"employed":[69],"cosine":[70],"similarity,":[71],"Manhattan":[72],"distance,":[73],"Bray-Curtis":[74,121],"dissimilarity,":[75],"Canberra":[77],"distance.":[78],"We":[79,123,152,169],"confirm":[80],"that":[81],"average":[83,109],"pooling":[84,110],"applied":[85],"to":[86,95,146,172,178],"layers":[88],"provides":[89],"good":[90],"descriptors,":[92],"propose":[94],"use":[96],"it":[97],"when":[98,160],"VGGbased":[102],"models.":[103],"Our":[104,141],"best":[105],"result":[106,143],"uses":[107],"layer":[111],"DenseNet-161":[113],"2208-dim":[116,175],"feature":[117,165],"vector":[118],"along":[119],"dissimilarity.":[122],"achieved":[124],"73:09%":[125],"mAP@1":[126,142],"76:98%":[128],"mAP@5":[129],"on":[130,157],"Describable":[132],"Textures":[133],"Dataset":[134],"(DTD)":[135],"benchmark,":[136],"adapted":[137],"retrieval.":[140],"comparable":[145],"state-of-the-art":[148],"classification":[149],"accuracy":[150],"(73:8%).":[151],"also":[153],"investigate":[154],"impact":[156],"retrieval":[158],"performance":[159],"reducing":[161],"number":[163],"components":[166,180],"PCA.":[168],"are":[170],"able":[171],"compress":[173],"descriptor":[176],"down":[177],"128":[179],"moderate":[183],"3.3":[184],"percentage":[185],"points":[186],"drop":[187],"in":[188],"mAP@1.":[189]},"counts_by_year":[{"year":2022,"cited_by_count":1},{"year":2020,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
