{"id":"https://openalex.org/W4385899901","doi":"https://doi.org/10.1109/iscas46773.2023.10182225","title":"Improving Deep Features for Image Retrieval Using Multi-Source Spatial Information","display_name":"Improving Deep Features for Image Retrieval Using Multi-Source Spatial Information","publication_year":2023,"publication_date":"2023-05-21","ids":{"openalex":"https://openalex.org/W4385899901","doi":"https://doi.org/10.1109/iscas46773.2023.10182225"},"language":"en","primary_location":{"id":"doi:10.1109/iscas46773.2023.10182225","is_oa":false,"landing_page_url":"http://dx.doi.org/10.1109/iscas46773.2023.10182225","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2023 IEEE International Symposium on Circuits and Systems (ISCAS)","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/A5007510059","display_name":"Farzad Sabahi","orcid":null},"institutions":[{"id":"https://openalex.org/I60158472","display_name":"Concordia University","ror":"https://ror.org/0420zvk78","country_code":"CA","type":"education","lineage":["https://openalex.org/I60158472"]}],"countries":["CA"],"is_corresponding":false,"raw_author_name":"Farzad Sabahi","raw_affiliation_strings":["Concordia University,Department of Electrical and Computer Engineering,Montreal,Canada","Department of Electrical and Computer Engineering, Concordia University, Montreal, Canada"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Concordia University,Department of Electrical and Computer Engineering,Montreal,Canada","institution_ids":["https://openalex.org/I60158472"]},{"raw_affiliation_string":"Department of Electrical and Computer Engineering, Concordia University, Montreal, Canada","institution_ids":["https://openalex.org/I60158472"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5068820891","display_name":"M. Omair Ahmad","orcid":"https://orcid.org/0000-0002-2924-6659"},"institutions":[{"id":"https://openalex.org/I60158472","display_name":"Concordia University","ror":"https://ror.org/0420zvk78","country_code":"CA","type":"education","lineage":["https://openalex.org/I60158472"]}],"countries":["CA"],"is_corresponding":false,"raw_author_name":"M. Omair Ahmad","raw_affiliation_strings":["Concordia University,Department of Electrical and Computer Engineering,Montreal,Canada","Department of Electrical and Computer Engineering, Concordia University, Montreal, Canada"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Concordia University,Department of Electrical and Computer Engineering,Montreal,Canada","institution_ids":["https://openalex.org/I60158472"]},{"raw_affiliation_string":"Department of Electrical and Computer Engineering, Concordia University, Montreal, Canada","institution_ids":["https://openalex.org/I60158472"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5013967994","display_name":"M.N.S. Swamy","orcid":"https://orcid.org/0000-0002-3989-5476"},"institutions":[{"id":"https://openalex.org/I60158472","display_name":"Concordia University","ror":"https://ror.org/0420zvk78","country_code":"CA","type":"education","lineage":["https://openalex.org/I60158472"]}],"countries":["CA"],"is_corresponding":false,"raw_author_name":"M. N. S. Swamy","raw_affiliation_strings":["Concordia University,Department of Electrical and Computer Engineering,Montreal,Canada","Department of Electrical and Computer Engineering, Concordia University, Montreal, Canada"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Concordia University,Department of Electrical and Computer Engineering,Montreal,Canada","institution_ids":["https://openalex.org/I60158472"]},{"raw_affiliation_string":"Department of Electrical and Computer Engineering, Concordia University, Montreal, Canada","institution_ids":["https://openalex.org/I60158472"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I60158472"],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":2,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":"8","issue":null,"first_page":"1","last_page":"5"},"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":0.9998999834060669,"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":0.9998999834060669,"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":0.9995999932289124,"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/T11307","display_name":"Domain Adaptation and Few-Shot Learning","score":0.9968000054359436,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"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/computer-science","display_name":"Computer science","score":0.7900898456573486},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6979765295982361},{"id":"https://openalex.org/keywords/residual","display_name":"Residual","score":0.6937121152877808},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.6545096635818481},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.6477646827697754},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.6273632645606995},{"id":"https://openalex.org/keywords/feature-extraction","display_name":"Feature extraction","score":0.5994302034378052},{"id":"https://openalex.org/keywords/block","display_name":"Block (permutation group theory)","score":0.5934939980506897},{"id":"https://openalex.org/keywords/convolution","display_name":"Convolution (computer science)","score":0.5171808004379272},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.5077003240585327},{"id":"https://openalex.org/keywords/spatial-analysis","display_name":"Spatial analysis","score":0.4620935320854187},{"id":"https://openalex.org/keywords/abstraction","display_name":"Abstraction","score":0.45466580986976624},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.30681440234184265},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.11256921291351318},{"id":"https://openalex.org/keywords/remote-sensing","display_name":"Remote sensing","score":0.0805819034576416},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.07483994960784912}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7900898456573486},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6979765295982361},{"id":"https://openalex.org/C155512373","wikidata":"https://www.wikidata.org/wiki/Q287450","display_name":"Residual","level":2,"score":0.6937121152877808},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.6545096635818481},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.6477646827697754},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.6273632645606995},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.5994302034378052},{"id":"https://openalex.org/C2777210771","wikidata":"https://www.wikidata.org/wiki/Q4927124","display_name":"Block (permutation group theory)","level":2,"score":0.5934939980506897},{"id":"https://openalex.org/C45347329","wikidata":"https://www.wikidata.org/wiki/Q5166604","display_name":"Convolution (computer science)","level":3,"score":0.5171808004379272},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.5077003240585327},{"id":"https://openalex.org/C159620131","wikidata":"https://www.wikidata.org/wiki/Q1938983","display_name":"Spatial analysis","level":2,"score":0.4620935320854187},{"id":"https://openalex.org/C124304363","wikidata":"https://www.wikidata.org/wiki/Q673661","display_name":"Abstraction","level":2,"score":0.45466580986976624},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.30681440234184265},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.11256921291351318},{"id":"https://openalex.org/C62649853","wikidata":"https://www.wikidata.org/wiki/Q199687","display_name":"Remote sensing","level":1,"score":0.0805819034576416},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.07483994960784912},{"id":"https://openalex.org/C2524010","wikidata":"https://www.wikidata.org/wiki/Q8087","display_name":"Geometry","level":1,"score":0.0},{"id":"https://openalex.org/C111472728","wikidata":"https://www.wikidata.org/wiki/Q9471","display_name":"Epistemology","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},{"id":"https://openalex.org/C127313418","wikidata":"https://www.wikidata.org/wiki/Q1069","display_name":"Geology","level":0,"score":0.0},{"id":"https://openalex.org/C41895202","wikidata":"https://www.wikidata.org/wiki/Q8162","display_name":"Linguistics","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/iscas46773.2023.10182225","is_oa":false,"landing_page_url":"http://dx.doi.org/10.1109/iscas46773.2023.10182225","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2023 IEEE International Symposium on Circuits and Systems (ISCAS)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[{"id":"https://openalex.org/F4320334593","display_name":"Natural Sciences and Engineering Research Council of Canada","ror":"https://ror.org/01h531d29"}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":24,"referenced_works":["https://openalex.org/W2062118960","https://openalex.org/W2123229215","https://openalex.org/W2544587078","https://openalex.org/W2618530766","https://openalex.org/W2761488830","https://openalex.org/W2767866943","https://openalex.org/W2804523126","https://openalex.org/W2883105896","https://openalex.org/W2887384995","https://openalex.org/W2890430426","https://openalex.org/W2918316070","https://openalex.org/W2945007112","https://openalex.org/W2954996726","https://openalex.org/W2966988368","https://openalex.org/W3013726901","https://openalex.org/W3042481550","https://openalex.org/W3093981303","https://openalex.org/W3105475654","https://openalex.org/W3106032784","https://openalex.org/W3110536152","https://openalex.org/W3134758788","https://openalex.org/W3161266886","https://openalex.org/W4214726668","https://openalex.org/W6762892961"],"related_works":["https://openalex.org/W2560215812","https://openalex.org/W2045155990","https://openalex.org/W2949601986","https://openalex.org/W4313163053","https://openalex.org/W2788972299","https://openalex.org/W3045811229","https://openalex.org/W4300973204","https://openalex.org/W2521347458","https://openalex.org/W1483408780","https://openalex.org/W2498789492"],"abstract_inverted_index":{"The":[0,108,130,156,176,188],"representational":[1,218],"quality":[2],"of":[3,85,143,162,173,210],"the":[4,57,70,137,146,160,163,184,192,208,211,217],"generated":[5],"feature":[6,28,58,71,101,117,122,127,138,174],"vectors":[7,29],"for":[8,12,30,74],"images":[9],"is":[10,22,198],"essential":[11],"image":[13,31,76],"retrieval":[14],"models":[15],"to":[16,42,99,167,200],"achieve":[17],"high":[18],"performance.":[19],"Spatial":[20],"information":[21,55,88,135,151],"crucial":[23],"in":[24,56,69,136,215],"obtaining":[25],"highly":[26],"representative":[27],"retrieval,":[32],"and":[33,125],"deep":[34,49,82,181],"convolutional":[35,47,50],"neural":[36,51],"networks":[37,52,80,83],"provide":[38,168],"an":[39],"excellent":[40],"framework":[41],"generate":[43,100],"such":[44],"features.":[45],"Through":[46],"operations,":[48],"include":[53,65],"spatial":[54,67,106,116,134,150],"maps.":[59,175],"However,":[60],"most":[61],"available":[62],"architectures":[63],"cannot":[64],"adequate":[66],"details":[68],"maps":[72,102,139],"required":[73],"high-performance":[75],"retrieval.":[77],"Deep":[78],"residual":[79,90,97,110,186,213],"are":[81],"capable":[84],"including":[86],"useful":[87],"through":[89],"learning.":[91],"This":[92],"paper":[93],"proposes":[94],"a":[95,115,120,126,169,180],"novel":[96],"block":[98,111,214],"by":[103],"focusing":[104],"on":[105,203],"information.":[107],"proposed":[109,185,193,212],"comprises":[112],"three":[113],"modules:":[114],"extraction":[118,123],"module,":[119,124],"hierarchical":[121],"fusion":[128],"module.":[129],"first":[131,164],"module":[132,148],"includes":[133,149],"at":[140],"different":[141],"levels":[142],"abstraction,":[144],"while":[145],"second":[147],"using":[152],"conventional":[153],"convolution":[154],"hierarchy.":[155],"third":[157],"model":[158],"fuses":[159],"outputs":[161],"two":[165],"modules":[166],"very":[170],"rich":[171],"set":[172],"present":[177],"study":[178],"tests":[179],"network":[182,194],"employing":[183],"block.":[187],"results":[189],"indicate":[190],"that":[191],"performs":[195],"comparably":[196],"or":[197],"superior":[199],"state-of-the-art":[201],"methods":[202],"standard":[204],"benchmarks,":[205],"thus":[206],"showing":[207],"effectiveness":[209],"improving":[216],"capacity.":[219]},"counts_by_year":[{"year":2024,"cited_by_count":2}],"updated_date":"2026-07-14T23:27:15.235271","created_date":"2025-10-10T00:00:00"}