{"id":"https://openalex.org/W2391444567","doi":null,"title":"Efficient Patch-Level Descriptor for Image Categorization via Patch Pivots Selection","display_name":"Efficient Patch-Level Descriptor for Image Categorization via Patch Pivots Selection","publication_year":2015,"publication_date":"2015-01-01","ids":{"openalex":"https://openalex.org/W2391444567","mag":"2391444567"},"language":"en","primary_location":{"id":"mag:2391444567","doi":null,"is_oa":false,"landing_page_url":"https://en.cnki.com.cn/Article_en/CJFDTotal-RJXB201511015.htm","pdf_url":null,"source":null,"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":false,"raw_source_name":"Journal of Software","raw_type":null},"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/A5071912894","display_name":"Bo Xie","orcid":"https://orcid.org/0000-0001-7641-5139","observed_orcids":["https://orcid.org/0000-0001-7641-5139"]},"institutions":[],"countries":[],"is_corresponding":true,"raw_author_name":"Bo Xie","raw_affiliation_strings":["Beijing Key Laboratory of Traffic Data Analysis and Mining"],"raw_orcid":"https://orcid.org/0000-0001-7641-5139","affiliations":[{"raw_affiliation_string":"Beijing Key Laboratory of Traffic Data Analysis and Mining","institution_ids":[]}]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":0,"corresponding_author_ids":["https://openalex.org/A5071912894"],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.0,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":null,"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":0.9014000296592712,"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":0.9014000296592712,"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":0.09759999811649323,"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/T10036","display_name":"Advanced Neural Network Applications","score":0.00019999999494757503,"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/image-classification","display_name":"image classification","score":0.578000009059906},{"id":"https://openalex.org/keywords/incomplete-cholesky-decomposition","display_name":"incomplete Cholesky decomposition","score":0.9449999928474426},{"id":"https://openalex.org/keywords/kernel-pca","display_name":"kernel principal component analysis","score":0.8870000243186951},{"id":"https://openalex.org/keywords/maximum-mean-discrepancy","display_name":"maximum mean discrepancy","score":0.9350000023841858},{"id":"https://openalex.org/keywords/image-similarity","display_name":"image similarity","score":0.6240000128746033},{"id":"https://openalex.org/keywords/computational-efficiency","display_name":"computational efficiency","score":0.7139999866485596}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7766402959823608},{"id":"https://openalex.org/C61265191","wikidata":"https://www.wikidata.org/wiki/Q767770","display_name":"Scale-invariant feature transform","level":3,"score":0.772616446018219},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7067332863807678},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.6981654763221741},{"id":"https://openalex.org/C74193536","wikidata":"https://www.wikidata.org/wiki/Q574844","display_name":"Kernel (algebra)","level":2,"score":0.6498880982398987},{"id":"https://openalex.org/C45374587","wikidata":"https://www.wikidata.org/wiki/Q12525525","display_name":"Computation","level":2,"score":0.4792782962322235},{"id":"https://openalex.org/C27438332","wikidata":"https://www.wikidata.org/wiki/Q2873","display_name":"Principal component analysis","level":2,"score":0.4581346809864044},{"id":"https://openalex.org/C182335926","wikidata":"https://www.wikidata.org/wiki/Q17093020","display_name":"Kernel principal component analysis","level":4,"score":0.4560045003890991},{"id":"https://openalex.org/C12267149","wikidata":"https://www.wikidata.org/wiki/Q282453","display_name":"Support vector machine","level":2,"score":0.4173338711261749},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.40191450715065},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.3735700249671936},{"id":"https://openalex.org/C122280245","wikidata":"https://www.wikidata.org/wiki/Q620622","display_name":"Kernel method","level":3,"score":0.2769049406051636},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.1871122419834137},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.13818100094795227},{"id":"https://openalex.org/C114614502","wikidata":"https://www.wikidata.org/wiki/Q76592","display_name":"Combinatorics","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"mag:2391444567","doi":null,"is_oa":false,"landing_page_url":"https://en.cnki.com.cn/Article_en/CJFDTotal-RJXB201511015.htm","pdf_url":null,"source":null,"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":false,"raw_source_name":"Journal of Software","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":34,"referenced_works":["https://openalex.org/W1566135517","https://openalex.org/W1964491636","https://openalex.org/W2017257315","https://openalex.org/W2027922120","https://openalex.org/W2028930140","https://openalex.org/W2036054070","https://openalex.org/W2073682124","https://openalex.org/W2100495367","https://openalex.org/W2103444992","https://openalex.org/W2107034620","https://openalex.org/W2108598243","https://openalex.org/W2110798204","https://openalex.org/W2112796928","https://openalex.org/W2118585731","https://openalex.org/W2118858186","https://openalex.org/W2123745357","https://openalex.org/W2125865219","https://openalex.org/W2133257461","https://openalex.org/W2136922672","https://openalex.org/W2137557016","https://openalex.org/W2140095548","https://openalex.org/W2142422094","https://openalex.org/W2145889472","https://openalex.org/W2147625498","https://openalex.org/W2147800946","https://openalex.org/W2150694071","https://openalex.org/W2151103935","https://openalex.org/W2155904486","https://openalex.org/W2161969291","https://openalex.org/W2162915993","https://openalex.org/W2163605009","https://openalex.org/W2166742463","https://openalex.org/W2295125894","https://openalex.org/W4285719527"],"related_works":["https://openalex.org/W2073682124","https://openalex.org/W3149433129","https://openalex.org/W2203099733","https://openalex.org/W2103444992","https://openalex.org/W2779608143","https://openalex.org/W7096999656","https://openalex.org/W2578212641","https://openalex.org/W2119587978","https://openalex.org/W2212363941","https://openalex.org/W2402187206"],"abstract_inverted_index":{"Designing":[0],"patch-level":[1,35,42,109],"features":[2,43,63],"is":[3,44,94,112,140],"essential":[4],"for":[5,48,142],"achieving":[6],"good":[7,46],"performance":[8],"in":[9,88],"computer":[10],"vision":[11],"tasks,":[12],"such":[13],"as":[14],"image":[15,69,125],"classification":[16,158],"and":[17,23,77,97],"object":[18],"recognition.":[19],"SIFT(scale-invariant":[20],"feature":[21],"transform)":[22],"HOG(histogram":[24],"of":[25,52,124],"oriented":[26],"gradient)":[27],"are":[28,86],"the":[29,38,50,89,149],"typical":[30],"schemes":[31],"among":[32],"many":[33],"pre-defined":[34],"descriptors,":[36],"but":[37],"difference":[39],"between":[40,145],"artificial":[41],"not":[45,98],"enough":[47],"reflecting":[49],"similarities":[51,144],"images.":[53,146],"Kernel":[54],"descriptor(KD)":[55],"method":[56],"offers":[57],"a":[58,121],"new":[59],"way":[60],"to":[61,128],"generate":[62],"from":[64],"match":[65],"kernel":[66,90],"defined":[67],"over":[68],"patch":[70],"pairs":[71],"using":[72],"KPCA(kernel":[73],"principal":[74],"component":[75],"analysis)":[76],"yields":[78],"impressive":[79],"results.":[80],"However,":[81],"all":[82],"joint":[83],"basis":[84],"vectors":[85],"involved":[87],"descriptor":[91],"computation,":[92],"which":[93,111],"both":[95],"expensive":[96],"necessary.":[99],"To":[100],"address":[101],"this":[102,104],"problem,":[103],"paper":[105],"presents":[106],"an":[107],"efficient":[108],"descriptor(EPLd)":[110],"built":[113],"upon":[114],"incomplete":[115],"Cholesky":[116],"decomposition.":[117],"EPLd":[118,150],"automatically":[119],"selects":[120],"small":[122],"number":[123],"patches":[126],"pivots":[127],"achieve":[129],"better":[130],"computational":[131],"efficiency.":[132],"Based":[133],"on":[134,155],"EPLd,":[135],"MMD(maximum":[136],"mean":[137],"discrepancy)":[138],"distance":[139],"used":[141],"computing":[143],"In":[147],"experiments,":[148],"approach":[151],"achieves":[152],"competitive":[153],"results":[154],"several":[156],"image/scene":[157],"datasets":[159],"compared":[160],"with":[161],"state-of-the-art":[162],"methods.":[163]},"counts_by_year":[],"updated_date":"2025-11-06T04:12:42.849631","created_date":"2016-06-24T00:00:00"}