{"id":"https://openalex.org/W2294433560","doi":null,"title":"Tensor Decomposition of Dense SIFT Descriptors in Object Recognition","display_name":"Tensor Decomposition of Dense SIFT Descriptors in Object Recognition","publication_year":2014,"publication_date":"2014-04-23","ids":{"openalex":"https://openalex.org/W2294433560","mag":"2294433560"},"language":"en","primary_location":{"id":"pmh:oai:CiteSeerX.psu:10.1.1.637.1699","doi":null,"is_oa":false,"landing_page_url":"http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.637.1699","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"https://www.elen.ucl.ac.be/Proceedings/esann/esannpdf/es2014-34.pdf","raw_type":"text"},"volume":null,"type":"article","is_traditional":true,"indexed_in":[],"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/A5103000563","display_name":"Tan Vo","orcid":"https://orcid.org/0000-0003-3019-8639","observed_orcids":["https://orcid.org/0000-0003-3019-8639"]},"institutions":[{"id":"https://openalex.org/I188329596","display_name":"University of Canberra","ror":"https://ror.org/04s1nv328","country_code":"AU","type":"education","lineage":["https://openalex.org/I188329596"]}],"countries":["AU"],"is_corresponding":false,"raw_author_name":"Tan Vo","raw_affiliation_strings":["University of Canberra, Canberra, Australia"],"raw_orcid":"https://orcid.org/0000-0003-3019-8639","affiliations":[{"raw_affiliation_string":"University of Canberra, Canberra, Australia","institution_ids":["https://openalex.org/I188329596"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5006197101","display_name":"Dat Tran","orcid":"https://orcid.org/0000-0002-5788-0446","observed_orcids":["https://orcid.org/0000-0002-5788-0446"]},"institutions":[{"id":"https://openalex.org/I188329596","display_name":"University of Canberra","ror":"https://ror.org/04s1nv328","country_code":"AU","type":"education","lineage":["https://openalex.org/I188329596"]}],"countries":["AU"],"is_corresponding":false,"raw_author_name":"Dat Tran","raw_affiliation_strings":["University of Canberra, Canberra, Australia"],"raw_orcid":"https://orcid.org/0000-0002-5788-0446","affiliations":[{"raw_affiliation_string":"University of Canberra, Canberra, Australia","institution_ids":["https://openalex.org/I188329596"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5089771247","display_name":"Wanli Ma","orcid":"https://orcid.org/0000-0003-4088-9193","observed_orcids":["https://orcid.org/0000-0003-4088-9193","https://orcid.org/0009-0005-5447-373X"]},"institutions":[{"id":"https://openalex.org/I188329596","display_name":"University of Canberra","ror":"https://ror.org/04s1nv328","country_code":"AU","type":"education","lineage":["https://openalex.org/I188329596"]}],"countries":["AU"],"is_corresponding":false,"raw_author_name":"Wanli Ma","raw_affiliation_strings":["University of Canberra, Canberra, Australia"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of Canberra, Canberra, Australia","institution_ids":["https://openalex.org/I188329596"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I188329596"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":1,"citation_normalized_percentile":{"value":0.0,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":88,"max":93},"biblio":{"volume":null,"issue":null,"first_page":"319","last_page":"324"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12303","display_name":"Tensor decomposition and applications","score":0.5259000062942505,"subfield":{"id":"https://openalex.org/subfields/2605","display_name":"Computational Mathematics"},"field":{"id":"https://openalex.org/fields/26","display_name":"Mathematics"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T12303","display_name":"Tensor decomposition and applications","score":0.5259000062942505,"subfield":{"id":"https://openalex.org/subfields/2605","display_name":"Computational Mathematics"},"field":{"id":"https://openalex.org/fields/26","display_name":"Mathematics"},"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":0.4065000116825104,"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":0.04019999876618385,"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/sift-descriptors","display_name":"SIFT descriptors","score":0.8610000014305115},{"id":"https://openalex.org/keywords/tensor-decomposition","display_name":"tensor decomposition","score":0.9909999966621399},{"id":"https://openalex.org/keywords/dimensionality-reduction","display_name":"dimensionality reduction","score":0.8180000185966492},{"id":"https://openalex.org/keywords/object-recognition","display_name":"object recognition","score":0.8149999976158142},{"id":"https://openalex.org/keywords/image-classification","display_name":"image classification","score":0.8790000081062317},{"id":"https://openalex.org/keywords/high-dimensional-feature-vectors","display_name":"high-dimensional feature vectors","score":0.6019999980926514},{"id":"https://openalex.org/keywords/caltech-101","display_name":"Caltech-101","score":0.9010000228881836}],"concepts":[{"id":"https://openalex.org/C61265191","wikidata":"https://www.wikidata.org/wiki/Q767770","display_name":"Scale-invariant feature transform","level":3,"score":0.9791232943534851},{"id":"https://openalex.org/C111030470","wikidata":"https://www.wikidata.org/wiki/Q1430460","display_name":"Curse of dimensionality","level":2,"score":0.7604135274887085},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7237499356269836},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.660293698310852},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6498184204101562},{"id":"https://openalex.org/C94124525","wikidata":"https://www.wikidata.org/wiki/Q912550","display_name":"Categorization","level":2,"score":0.5880287885665894},{"id":"https://openalex.org/C64876066","wikidata":"https://www.wikidata.org/wiki/Q5141226","display_name":"Cognitive neuroscience of visual object recognition","level":3,"score":0.5765503644943237},{"id":"https://openalex.org/C70518039","wikidata":"https://www.wikidata.org/wiki/Q16000077","display_name":"Dimensionality reduction","level":2,"score":0.5530991554260254},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.5135612487792969},{"id":"https://openalex.org/C124681953","wikidata":"https://www.wikidata.org/wiki/Q339062","display_name":"Decomposition","level":2,"score":0.5123282670974731},{"id":"https://openalex.org/C155281189","wikidata":"https://www.wikidata.org/wiki/Q3518150","display_name":"Tensor (intrinsic definition)","level":2,"score":0.46390530467033386},{"id":"https://openalex.org/C190470478","wikidata":"https://www.wikidata.org/wiki/Q2370229","display_name":"Invariant (physics)","level":2,"score":0.4286222457885742},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.3997144103050232},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.26904553174972534},{"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/C18903297","wikidata":"https://www.wikidata.org/wiki/Q7150","display_name":"Ecology","level":1,"score":0.0},{"id":"https://openalex.org/C86803240","wikidata":"https://www.wikidata.org/wiki/Q420","display_name":"Biology","level":0,"score":0.0},{"id":"https://openalex.org/C202444582","wikidata":"https://www.wikidata.org/wiki/Q837863","display_name":"Pure mathematics","level":1,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"pmh:oai:CiteSeerX.psu:10.1.1.637.1699","doi":null,"is_oa":false,"landing_page_url":"http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.637.1699","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"https://www.elen.ucl.ac.be/Proceedings/esann/esannpdf/es2014-34.pdf","raw_type":"text"},{"id":"mag:2294433560","doi":null,"is_oa":false,"landing_page_url":"http://www.elen.ucl.ac.be/Proceedings/esann/esannpdf/es2014-34.pdf","pdf_url":null,"source":{"id":"https://openalex.org/S4306421047","display_name":"The European Symposium on Artificial Neural Networks","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"conference"},"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":false,"raw_source_name":"The European Symposium on Artificial Neural Networks","raw_type":null}],"best_oa_location":null,"sustainable_development_goals":[],"study_designs":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":8,"referenced_works":["https://openalex.org/W1980911747","https://openalex.org/W1997595258","https://openalex.org/W2000215628","https://openalex.org/W2024165284","https://openalex.org/W2145072179","https://openalex.org/W2151103935","https://openalex.org/W2166049352","https://openalex.org/W2171852577"],"related_works":["https://openalex.org/W2745464931","https://openalex.org/W2228351111","https://openalex.org/W2156402652","https://openalex.org/W2168314614","https://openalex.org/W1510423351","https://openalex.org/W2063404359","https://openalex.org/W2980684393","https://openalex.org/W2079393602","https://openalex.org/W2091283871","https://openalex.org/W2547360755"],"abstract_inverted_index":{"Abstract.":[0],"In":[1],"machine":[2],"vision,":[3],"Scale-invariant":[4],"feature":[5],"transform":[6],"(SIFT)":[7],"and":[8,45,63,81,84],"its":[9],"variants":[10],"have":[11],"been":[12],"widely":[13],"used":[14],"in":[15,28,40],"image":[16],"classification":[17],"task.":[18],"How-ever,":[19],"the":[20,29,55,85,89,94],"high":[21],"dimensionality":[22,73],"nature":[23],"of":[24,31,48,57],"SIFT":[25,59,70],"features,":[26],"usually":[27],"or-der":[30],"multiple":[32],"thousands":[33],"per":[34],"image,":[35],"would":[36],"require":[37],"careful":[38],"consideration":[39],"place":[41],"to":[42],"achieve":[43],"accurate":[44],"timely":[46],"categorization":[47],"objects":[49],"within":[50],"images.":[51],"This":[52],"paper":[53],"explores":[54],"possibility":[56],"processing":[58],"features":[60],"as":[61],"tensors":[62,71],"uses":[64],"tensor":[65],"decomposition":[66],"techniques":[67],"on":[68,78],"high-order":[69],"for":[72],"reduction.":[74],"The":[75],"method":[76],"focuses":[77],"both":[79],"accu-racy":[80],"efficiency":[82],"aspects":[83],"validation":[86],"result":[87],"with":[88,96],"Caltech":[90],"101":[91],"dataset":[92],"confirms":[93],"improvement":[95],"notable":[97],"margins.":[98],"1":[99]},"counts_by_year":[{"year":2019,"cited_by_count":1}],"updated_date":"2026-11-08T12:12:28.789561","created_date":"2016-06-24T00:00:00"}