{"id":"https://openalex.org/W630138847","doi":null,"title":"Searching with quantization: approximate nearest neighbor search using short codes and distance estimators","display_name":"Searching with quantization: approximate nearest neighbor search using short codes and distance estimators","publication_year":2009,"publication_date":"2009-08-01","ids":{"openalex":"https://openalex.org/W630138847","mag":"630138847"},"language":"en","primary_location":{"id":"pmh:oai:CiteSeerX.psu:10.1.1.212.6456","doi":null,"is_oa":false,"landing_page_url":"http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.212.6456","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"http://lear.inrialpes.fr/pubs/2009/JDS09d/jegou_searching_with_quantization.pdf","raw_type":"text"},"volume":null,"type":"article","is_traditional":true,"indexed_in":[],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://inria.hal.science/inria-00410767","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5111802678","display_name":"Herv\u00e9 Je\u01f5ou","orcid":null,"observed_orcids":[]},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Herv\u00e9 Je\u01f5ou","raw_affiliation_strings":["Learning and recognition in vision"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Learning and recognition in vision","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5053822883","display_name":"Matthijs Douze","orcid":"https://orcid.org/0009-0009-6754-7821","observed_orcids":["https://orcid.org/0009-0009-6754-7821"]},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Matthijs Douze","raw_affiliation_strings":["Learning and recognition in vision"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Learning and recognition in vision","institution_ids":[]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5109890544","display_name":"Cordelia Schmid","orcid":"https://orcid.org/0009-0005-7239-3741","observed_orcids":["https://orcid.org/0009-0005-7239-3741"]},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Cordelia Schmid","raw_affiliation_strings":["Learning and recognition in vision"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Learning and recognition in vision","institution_ids":[]}]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":0,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":1.6196,"has_fulltext":false,"cited_by_count":19,"citation_normalized_percentile":{"value":0.85396626,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":88,"max":98},"biblio":{"volume":null,"issue":null,"first_page":"25","last_page":null},"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":0.9990000128746033,"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":0.9990000128746033,"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":0.00039999998989515007,"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/T10901","display_name":"Advanced Data Compression Techniques","score":9.999999747378752e-05,"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/approximate-nearest-neighbor-search","display_name":"approximate nearest neighbor search","score":1.0},{"id":"https://openalex.org/keywords/product-quantization","display_name":"product quantization","score":0.7839999794960022},{"id":"https://openalex.org/keywords/distance-estimation","display_name":"distance estimation","score":0.7680000066757202},{"id":"https://openalex.org/keywords/short-codes","display_name":"short codes","score":0.753000020980835},{"id":"https://openalex.org/keywords/image-descriptors","display_name":"image descriptors","score":0.5649999976158142},{"id":"https://openalex.org/keywords/sift","display_name":"SIFT","score":0.8379999995231628},{"id":"https://openalex.org/keywords/gastrointestinal-stromal-tumor","display_name":"gastrointestinal stromal tumor","score":0.9430000185966492}],"concepts":[{"id":"https://openalex.org/C116738811","wikidata":"https://www.wikidata.org/wiki/Q608751","display_name":"Nearest neighbor search","level":2,"score":0.7828596830368042},{"id":"https://openalex.org/C120174047","wikidata":"https://www.wikidata.org/wiki/Q847073","display_name":"Euclidean distance","level":2,"score":0.6451827883720398},{"id":"https://openalex.org/C199833920","wikidata":"https://www.wikidata.org/wiki/Q612536","display_name":"Vector quantization","level":2,"score":0.6275540590286255},{"id":"https://openalex.org/C113238511","wikidata":"https://www.wikidata.org/wiki/Q1071612","display_name":"k-nearest neighbors algorithm","level":2,"score":0.5659950971603394},{"id":"https://openalex.org/C32900221","wikidata":"https://www.wikidata.org/wiki/Q181365","display_name":"Dot product","level":2,"score":0.5529301166534424},{"id":"https://openalex.org/C74270461","wikidata":"https://www.wikidata.org/wiki/Q1625299","display_name":"Locality-sensitive hashing","level":4,"score":0.5292066335678101},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.5265085101127625},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.5089030265808105},{"id":"https://openalex.org/C129782007","wikidata":"https://www.wikidata.org/wiki/Q162886","display_name":"Euclidean geometry","level":2,"score":0.4980125427246094},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.47624707221984863},{"id":"https://openalex.org/C193319292","wikidata":"https://www.wikidata.org/wiki/Q272172","display_name":"Hamming distance","level":2,"score":0.43858465552330017},{"id":"https://openalex.org/C185429906","wikidata":"https://www.wikidata.org/wiki/Q1130160","display_name":"Estimator","level":2,"score":0.4162626266479492},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.41478103399276733},{"id":"https://openalex.org/C177264268","wikidata":"https://www.wikidata.org/wiki/Q1514741","display_name":"Set (abstract data type)","level":2,"score":0.4138583540916443},{"id":"https://openalex.org/C28855332","wikidata":"https://www.wikidata.org/wiki/Q198099","display_name":"Quantization (signal processing)","level":2,"score":0.4103415608406067},{"id":"https://openalex.org/C99138194","wikidata":"https://www.wikidata.org/wiki/Q183427","display_name":"Hash function","level":2,"score":0.40926000475883484},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.38304412364959717},{"id":"https://openalex.org/C67388219","wikidata":"https://www.wikidata.org/wiki/Q207440","display_name":"Hash table","level":3,"score":0.20097577571868896},{"id":"https://openalex.org/C199360897","wikidata":"https://www.wikidata.org/wiki/Q9143","display_name":"Programming language","level":1,"score":0.0},{"id":"https://openalex.org/C38652104","wikidata":"https://www.wikidata.org/wiki/Q3510521","display_name":"Computer security","level":1,"score":0.0},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.0},{"id":"https://openalex.org/C2524010","wikidata":"https://www.wikidata.org/wiki/Q8087","display_name":"Geometry","level":1,"score":0.0}],"mesh":[],"locations_count":3,"locations":[{"id":"pmh:oai:CiteSeerX.psu:10.1.1.212.6456","doi":null,"is_oa":false,"landing_page_url":"http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.212.6456","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"http://lear.inrialpes.fr/pubs/2009/JDS09d/jegou_searching_with_quantization.pdf","raw_type":"text"},{"id":"pmh:oai:HAL:inria-00410767v1","doi":null,"is_oa":true,"landing_page_url":"https://inria.hal.science/inria-00410767","pdf_url":null,"source":{"id":"https://openalex.org/S4306402512","display_name":"HAL","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":"https://openalex.org/I4405252976","host_organization_name":"Centre pour la Communication Scientifique Directe","host_organization_lineage":["https://openalex.org/I4405252976"],"host_organization_lineage_names":[],"type":"repository"},"license":"other-oa","license_id":"https://openalex.org/licenses/other-oa","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"[Research Report] RR-7020, INRIA. 2009, pp.25","raw_type":"info:eu-repo/semantics/report"},{"id":"mag:630138847","doi":null,"is_oa":false,"landing_page_url":"http://lear.inrialpes.fr/pubs/2009/JDS09d/jegou_searching_with_quantization.pdf","pdf_url":null,"source":null,"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":null}],"best_oa_location":{"id":"pmh:oai:HAL:inria-00410767v1","doi":null,"is_oa":true,"landing_page_url":"https://inria.hal.science/inria-00410767","pdf_url":null,"source":{"id":"https://openalex.org/S4306402512","display_name":"HAL","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":"https://openalex.org/I4405252976","host_organization_name":"Centre pour la Communication Scientifique Directe","host_organization_lineage":["https://openalex.org/I4405252976"],"host_organization_lineage_names":[],"type":"repository"},"license":"other-oa","license_id":"https://openalex.org/licenses/other-oa","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"[Research Report] RR-7020, INRIA. 2009, pp.25","raw_type":"info:eu-repo/semantics/report"},"sustainable_development_goals":[],"study_designs":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":26,"referenced_works":["https://openalex.org/W1491105865","https://openalex.org/W1502916507","https://openalex.org/W1541459201","https://openalex.org/W1556531089","https://openalex.org/W1566135517","https://openalex.org/W1627400044","https://openalex.org/W1672197616","https://openalex.org/W1972378554","https://openalex.org/W2020308406","https://openalex.org/W2024668293","https://openalex.org/W2055289292","https://openalex.org/W2088866137","https://openalex.org/W2099907898","https://openalex.org/W2124222502","https://openalex.org/W2128017662","https://openalex.org/W2131846894","https://openalex.org/W2141362318","https://openalex.org/W2145607950","https://openalex.org/W2148809531","https://openalex.org/W2151103935","https://openalex.org/W2154956324","https://openalex.org/W2162006472","https://openalex.org/W2171790913","https://openalex.org/W2293597654","https://openalex.org/W2543932557","https://openalex.org/W3160851792"],"related_works":["https://openalex.org/W2124509324","https://openalex.org/W7096969480","https://openalex.org/W2133995768","https://openalex.org/W4285731587","https://openalex.org/W2083771455","https://openalex.org/W2563530322","https://openalex.org/W2100378165","https://openalex.org/W138284169","https://openalex.org/W2039357207","https://openalex.org/W2080806811"],"abstract_inverted_index":{"We":[0],"propose":[1],"an":[2,39],"approximate":[3],"nearest":[4,77],"neighbor":[5,78],"search":[6,90],"method":[7,34,93],"based":[8,68],"on":[9,69,82],"quantization.":[10],"It":[11],"uses,":[12],"in":[13,38],"particular,":[14],"product":[15],"quantizer":[16],"to":[17,96,117],"produce":[18],"short":[19],"codes":[20,72],"and":[21,49,84],"corresponding":[22],"distance":[23,28,45,67],"estimators":[24],"approximating":[25],"the":[26,30,44,65,102,121,125],"Euclidean":[27,66],"between":[29,46],"orginal":[31],"vectors.":[32],"The":[33,92],"is":[35,94],"advantageously":[36],"used":[37],"asymmetric":[40],"manner,":[41],"by":[42],"computing":[43],"a":[47,108],"vector":[48,109],"code,":[50],"unlike":[51],"competing":[52],"techniques":[53],"such":[54],"as":[55],"spectral":[56],"hashing":[57],"that":[58],"only":[59],"compare":[60],"codes.":[61],"Our":[62],"approach":[63],"approximates":[64],"memory":[70],"efficient":[71,76],"and,":[73],"thus,":[74],"permits":[75],"search.":[79],"Experiments":[80],"performed":[81],"SIFT":[83],"GIST":[85],"image":[86],"descriptors":[87],"show":[88],"excellent":[89,119],"accuracy.":[91],"shown":[95,116],"outperform":[97],"two":[98],"state-of-the-art":[99],"approaches":[100],"of":[101,111,124],"literature.":[103],"Timings":[104],"measured":[105],"when":[106],"searching":[107],"set":[110],"2":[112],"billion":[113],"vectors":[114],"are":[115],"be":[118],"given":[120],"high":[122],"accuracy":[123],"method.":[126]},"counts_by_year":[{"year":2025,"cited_by_count":1},{"year":2017,"cited_by_count":3},{"year":2016,"cited_by_count":4},{"year":2015,"cited_by_count":3},{"year":2014,"cited_by_count":1},{"year":2013,"cited_by_count":2}],"updated_date":"2026-11-03T10:50:19.383937","created_date":"2016-06-24T00:00:00"}