{"id":"https://openalex.org/W2100291541","doi":"https://doi.org/10.1109/icassp.2014.6854313","title":"Fast and accurate Nearest Neighbor search in the manifolds of symmetric positive definite matrices","display_name":"Fast and accurate Nearest Neighbor search in the manifolds of symmetric positive definite matrices","publication_year":2014,"publication_date":"2014-05-01","ids":{"openalex":"https://openalex.org/W2100291541","doi":"https://doi.org/10.1109/icassp.2014.6854313","mag":"2100291541"},"language":"en","primary_location":{"id":"doi:10.1109/icassp.2014.6854313","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icassp.2014.6854313","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2014 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)","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/A5077124910","display_name":"Ligang Zheng","orcid":"https://orcid.org/0000-0002-2991-0564"},"institutions":[{"id":"https://openalex.org/I37987034","display_name":"Guangzhou University","ror":"https://ror.org/05ar8rn06","country_code":"CN","type":"education","lineage":["https://openalex.org/I37987034"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Ligang Zheng","raw_affiliation_strings":["School of Computer Science, Guangzhou University, Guangzhou, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Computer Science, Guangzhou University, Guangzhou, China","institution_ids":["https://openalex.org/I37987034"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5114259459","display_name":"Guoping Qiu","orcid":"https://orcid.org/0000-0002-5877-5648"},"institutions":[{"id":"https://openalex.org/I142263535","display_name":"University of Nottingham","ror":"https://ror.org/01ee9ar58","country_code":"GB","type":"education","lineage":["https://openalex.org/I142263535"]}],"countries":["GB"],"is_corresponding":false,"raw_author_name":"Guoping Qiu","raw_affiliation_strings":["School of Computer Science, The University of Nottingham, Nottingham, UK","School of Computer Science; University of Nottingham; Nottingham UK"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Computer Science, The University of Nottingham, Nottingham, UK","institution_ids":["https://openalex.org/I142263535"]},{"raw_affiliation_string":"School of Computer Science; University of Nottingham; Nottingham UK","institution_ids":["https://openalex.org/I142263535"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5047964483","display_name":"Jiwu Huang","orcid":"https://orcid.org/0000-0002-7625-5689"},"institutions":[{"id":"https://openalex.org/I180726961","display_name":"Shenzhen University","ror":"https://ror.org/01vy4gh70","country_code":"CN","type":"education","lineage":["https://openalex.org/I180726961"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Jiwu Huang","raw_affiliation_strings":["College of Information Engineering, Shenzhen University, Shenzhen, China","College of Information Engineering, Shenzhen University Shenzhen, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"College of Information Engineering, Shenzhen University, Shenzhen, China","institution_ids":["https://openalex.org/I180726961"]},{"raw_affiliation_string":"College of Information Engineering, Shenzhen University Shenzhen, China","institution_ids":["https://openalex.org/I180726961"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5101518907","display_name":"Jiang Duan","orcid":"https://orcid.org/0000-0002-8573-311X"},"institutions":[{"id":"https://openalex.org/I204831749","display_name":"Southwestern University of Finance and Economics","ror":"https://ror.org/04ewct822","country_code":"CN","type":"education","lineage":["https://openalex.org/I204831749"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Jiang Duan","raw_affiliation_strings":["Southwestern University of Finance and Economics, Chengdu, China","Southwestern University of Finance and Economics  Chengdu China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Southwestern University of Finance and Economics, Chengdu, China","institution_ids":["https://openalex.org/I204831749"]},{"raw_affiliation_string":"Southwestern University of Finance and Economics  Chengdu China","institution_ids":["https://openalex.org/I204831749"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":4,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":7,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":"66","issue":null,"first_page":"3804","last_page":"3808"},"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":0.9991999864578247,"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":0.9991999864578247,"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"}},{"id":"https://openalex.org/T10331","display_name":"Video Surveillance and Tracking Methods","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"}}],"keywords":[{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.5927837491035461},{"id":"https://openalex.org/keywords/k-nearest-neighbors-algorithm","display_name":"k-nearest neighbors algorithm","score":0.5642776489257812},{"id":"https://openalex.org/keywords/nearest-neighbor-search","display_name":"Nearest neighbor search","score":0.5390950441360474},{"id":"https://openalex.org/keywords/large-margin-nearest-neighbor","display_name":"Large margin nearest neighbor","score":0.5087421536445618},{"id":"https://openalex.org/keywords/metric","display_name":"Metric (unit)","score":0.5022919178009033},{"id":"https://openalex.org/keywords/cluster-analysis","display_name":"Cluster analysis","score":0.47859618067741394},{"id":"https://openalex.org/keywords/affine-transformation","display_name":"Affine transformation","score":0.4779638946056366},{"id":"https://openalex.org/keywords/invariant","display_name":"Invariant (physics)","score":0.4480918049812317},{"id":"https://openalex.org/keywords/positive-definite-matrix","display_name":"Positive-definite matrix","score":0.44062793254852295},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.42616766691207886},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.3522838354110718},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.3406672477722168},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.31643861532211304}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5927837491035461},{"id":"https://openalex.org/C113238511","wikidata":"https://www.wikidata.org/wiki/Q1071612","display_name":"k-nearest neighbors algorithm","level":2,"score":0.5642776489257812},{"id":"https://openalex.org/C116738811","wikidata":"https://www.wikidata.org/wiki/Q608751","display_name":"Nearest neighbor search","level":2,"score":0.5390950441360474},{"id":"https://openalex.org/C94475309","wikidata":"https://www.wikidata.org/wiki/Q6489154","display_name":"Large margin nearest neighbor","level":3,"score":0.5087421536445618},{"id":"https://openalex.org/C176217482","wikidata":"https://www.wikidata.org/wiki/Q860554","display_name":"Metric (unit)","level":2,"score":0.5022919178009033},{"id":"https://openalex.org/C73555534","wikidata":"https://www.wikidata.org/wiki/Q622825","display_name":"Cluster analysis","level":2,"score":0.47859618067741394},{"id":"https://openalex.org/C92757383","wikidata":"https://www.wikidata.org/wiki/Q382497","display_name":"Affine transformation","level":2,"score":0.4779638946056366},{"id":"https://openalex.org/C190470478","wikidata":"https://www.wikidata.org/wiki/Q2370229","display_name":"Invariant (physics)","level":2,"score":0.4480918049812317},{"id":"https://openalex.org/C49712288","wikidata":"https://www.wikidata.org/wiki/Q77601250","display_name":"Positive-definite matrix","level":3,"score":0.44062793254852295},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.42616766691207886},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.3522838354110718},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.3406672477722168},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.31643861532211304},{"id":"https://openalex.org/C162324750","wikidata":"https://www.wikidata.org/wiki/Q8134","display_name":"Economics","level":0,"score":0.0},{"id":"https://openalex.org/C37914503","wikidata":"https://www.wikidata.org/wiki/Q156495","display_name":"Mathematical physics","level":1,"score":0.0},{"id":"https://openalex.org/C158693339","wikidata":"https://www.wikidata.org/wiki/Q190524","display_name":"Eigenvalues and eigenvectors","level":2,"score":0.0},{"id":"https://openalex.org/C202444582","wikidata":"https://www.wikidata.org/wiki/Q837863","display_name":"Pure mathematics","level":1,"score":0.0},{"id":"https://openalex.org/C21547014","wikidata":"https://www.wikidata.org/wiki/Q1423657","display_name":"Operations management","level":1,"score":0.0},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0},{"id":"https://openalex.org/C62520636","wikidata":"https://www.wikidata.org/wiki/Q944","display_name":"Quantum mechanics","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/icassp.2014.6854313","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icassp.2014.6854313","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2014 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)","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":25,"referenced_works":["https://openalex.org/W92310073","https://openalex.org/W1501994587","https://openalex.org/W1519309101","https://openalex.org/W1554640171","https://openalex.org/W1592510033","https://openalex.org/W1598557217","https://openalex.org/W1983364709","https://openalex.org/W1983496390","https://openalex.org/W2020308406","https://openalex.org/W2059378705","https://openalex.org/W2059640215","https://openalex.org/W2070208691","https://openalex.org/W2071781501","https://openalex.org/W2090407557","https://openalex.org/W2097546612","https://openalex.org/W2118722335","https://openalex.org/W2128017662","https://openalex.org/W2131846894","https://openalex.org/W2147717514","https://openalex.org/W2166334548","https://openalex.org/W2397770138","https://openalex.org/W2911964244","https://openalex.org/W6630176648","https://openalex.org/W6635900159","https://openalex.org/W6674674671"],"related_works":["https://openalex.org/W4246757943","https://openalex.org/W2351157934","https://openalex.org/W2381195555","https://openalex.org/W2795392346","https://openalex.org/W2969538544","https://openalex.org/W2787484455","https://openalex.org/W2108882154","https://openalex.org/W2182477562","https://openalex.org/W2519241726","https://openalex.org/W1595303882"],"abstract_inverted_index":{"In":[0],"this":[1],"paper,":[2],"we":[3,71],"present":[4],"a":[5,20,97],"fast":[6],"and":[7,43,119],"accurate":[8],"Nearest":[9],"Neighbor":[10],"(NN)":[11],"search":[12,86],"method":[13,106],"in":[14,52,88,96,114],"the":[15,49,75,83,89,104,111],"Riemannian":[16,64],"manifolds":[17],"formed":[18],"by":[19],"kind":[21],"of":[22,35,92,110,116],"structured":[23],"data":[24],"-":[25],"symmetric":[26],"positive":[27],"definite":[28],"(SPD)":[29],"matrices.":[30],"We":[31,81],"use":[32,74],"an":[33],"ensemble":[34],"vocabulary":[36,58],"trees":[37,46,59],"based":[38],"on":[39],"hierarchical":[40],"k-means":[41],"clustering":[42],"query":[44],"these":[45,57],"to":[47,73,78],"find":[48],"NN":[50,85],"candidates":[51],"sub-linear":[53],"time.":[54],"As":[55],"generating":[56],"with":[60],"widely":[61],"used":[62],"affine-invariant":[63],"metric":[65],"(AIRM)":[66],"will":[67],"be":[68],"very":[69],"time-demanding,":[70],"propose":[72],"second-order":[76],"approximation":[77],"AIRM":[79],"(SOA-AIRM).":[80],"evaluate":[82],"proposed":[84,105],"algorithm":[87],"application":[90],"scenario":[91],"near-duplicate":[93],"image":[94],"detection":[95],"large":[98],"database.":[99],"Experimental":[100],"results":[101],"demonstrate":[102],"that":[103],"significantly":[107],"outperforms":[108],"state":[109],"art":[112],"techniques":[113],"terms":[115],"both":[117],"accuracy":[118],"speed.":[120]},"counts_by_year":[{"year":2021,"cited_by_count":2},{"year":2017,"cited_by_count":3},{"year":2016,"cited_by_count":2}],"updated_date":"2026-07-14T23:27:15.235271","created_date":"2025-10-10T00:00:00"}
