{"id":"https://openalex.org/W2809840990","doi":"https://doi.org/10.1145/3208854.3208886","title":"Learning Geometric Invariance Features and Discrimination Representation for Image Classification via Spatial Transform Network and XGBoost Modeling","display_name":"Learning Geometric Invariance Features and Discrimination Representation for Image Classification via Spatial Transform Network and XGBoost Modeling","publication_year":2018,"publication_date":"2018-03-28","ids":{"openalex":"https://openalex.org/W2809840990","doi":"https://doi.org/10.1145/3208854.3208886","mag":"2809840990"},"language":"en","primary_location":{"id":"doi:10.1145/3208854.3208886","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3208854.3208886","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 7th International Conference on Informatics, Environment, Energy and Applications","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/A5022549905","display_name":"Liye Mei","orcid":"https://orcid.org/0000-0002-2555-9199"},"institutions":[{"id":"https://openalex.org/I189210763","display_name":"Yunnan University","ror":"https://ror.org/0040axw97","country_code":"CN","type":"education","lineage":["https://openalex.org/I189210763"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Liye Mei","raw_affiliation_strings":["Yunnan University, School of Information Science and Engineering, Kunming, Yunnan, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Yunnan University, School of Information Science and Engineering, Kunming, Yunnan, China","institution_ids":["https://openalex.org/I189210763"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5014286651","display_name":"Xiaopeng Guo","orcid":"https://orcid.org/0000-0003-1111-2035"},"institutions":[{"id":"https://openalex.org/I189210763","display_name":"Yunnan University","ror":"https://ror.org/0040axw97","country_code":"CN","type":"education","lineage":["https://openalex.org/I189210763"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xiaopeng Guo","raw_affiliation_strings":["Yunnan University, School of Information Science and Engineering, Kunming, Yunnan, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Yunnan University, School of Information Science and Engineering, Kunming, Yunnan, China","institution_ids":["https://openalex.org/I189210763"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5035345099","display_name":"Wang Yin","orcid":"https://orcid.org/0000-0002-9298-4566"},"institutions":[{"id":"https://openalex.org/I4210119942","display_name":"Wuhan Textile University","ror":"https://ror.org/02jgsf398","country_code":"CN","type":"education","lineage":["https://openalex.org/I4210119942"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Wang Yin","raw_affiliation_strings":["Wuhan Textile University, School of math and computer Science, Wuhan, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Wuhan Textile University, School of math and computer Science, Wuhan, China","institution_ids":["https://openalex.org/I4210119942"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.05196292,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"222","last_page":"226"},"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":0.9994999766349792,"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":0.9994999766349792,"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":0.9988999962806702,"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"}},{"id":"https://openalex.org/T10824","display_name":"Image Retrieval and Classification Techniques","score":0.9987999796867371,"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/mnist-database","display_name":"MNIST database","score":0.7638133764266968},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.7585604190826416},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.7077259421348572},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.7050014138221741},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6642972230911255},{"id":"https://openalex.org/keywords/softmax-function","display_name":"Softmax function","score":0.5949721336364746},{"id":"https://openalex.org/keywords/affine-transformation","display_name":"Affine transformation","score":0.5194242000579834},{"id":"https://openalex.org/keywords/invariant","display_name":"Invariant (physics)","score":0.4920019805431366},{"id":"https://openalex.org/keywords/feature-extraction","display_name":"Feature extraction","score":0.45677489042282104},{"id":"https://openalex.org/keywords/contextual-image-classification","display_name":"Contextual image classification","score":0.4560298025608063},{"id":"https://openalex.org/keywords/classifier","display_name":"Classifier (UML)","score":0.4117491841316223},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.33711978793144226},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.3207379877567291},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.2502601742744446},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.21666067838668823}],"concepts":[{"id":"https://openalex.org/C190502265","wikidata":"https://www.wikidata.org/wiki/Q17069496","display_name":"MNIST database","level":3,"score":0.7638133764266968},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7585604190826416},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.7077259421348572},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.7050014138221741},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6642972230911255},{"id":"https://openalex.org/C188441871","wikidata":"https://www.wikidata.org/wiki/Q7554146","display_name":"Softmax function","level":3,"score":0.5949721336364746},{"id":"https://openalex.org/C92757383","wikidata":"https://www.wikidata.org/wiki/Q382497","display_name":"Affine transformation","level":2,"score":0.5194242000579834},{"id":"https://openalex.org/C190470478","wikidata":"https://www.wikidata.org/wiki/Q2370229","display_name":"Invariant (physics)","level":2,"score":0.4920019805431366},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.45677489042282104},{"id":"https://openalex.org/C75294576","wikidata":"https://www.wikidata.org/wiki/Q5165192","display_name":"Contextual image classification","level":3,"score":0.4560298025608063},{"id":"https://openalex.org/C95623464","wikidata":"https://www.wikidata.org/wiki/Q1096149","display_name":"Classifier (UML)","level":2,"score":0.4117491841316223},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.33711978793144226},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.3207379877567291},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.2502601742744446},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.21666067838668823},{"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/C37914503","wikidata":"https://www.wikidata.org/wiki/Q156495","display_name":"Mathematical physics","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3208854.3208886","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3208854.3208886","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 7th International Conference on Informatics, Environment, Energy and Applications","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/10","score":0.6399999856948853,"display_name":"Reduced inequalities"}],"awards":[],"funders":[{"id":"https://openalex.org/F4320324731","display_name":"Yunnan University","ror":"https://ror.org/0040axw97"}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":11,"referenced_works":["https://openalex.org/W603908379","https://openalex.org/W1522301498","https://openalex.org/W1686810756","https://openalex.org/W1836465849","https://openalex.org/W2194775991","https://openalex.org/W2295598076","https://openalex.org/W2747685395","https://openalex.org/W2750384547","https://openalex.org/W2919115771","https://openalex.org/W2949650786","https://openalex.org/W3102476541"],"related_works":["https://openalex.org/W4386603768","https://openalex.org/W4283819496","https://openalex.org/W3120400911","https://openalex.org/W4206451978","https://openalex.org/W3195622388","https://openalex.org/W4229443568","https://openalex.org/W4200096682","https://openalex.org/W3041443116","https://openalex.org/W2005234362","https://openalex.org/W1997235926"],"abstract_inverted_index":{"Convolutional":[0],"neural":[1],"network":[2,85],"(CNN)":[3],"has":[4,32],"proven":[5],"itself":[6],"as":[7,43,55,141,151],"a":[8,33,92,118,121,161,198],"promising":[9],"methodology":[10],"for":[11,102,201],"various":[12],"computer":[13],"vision":[14],"tasks":[15],"due":[16],"to":[17,36,40,50,123,171],"its":[18],"efficient":[19],"hierarchical":[20],"feature":[21],"learning":[22,103],"of":[23,67,96,111,130,135,155,163,175,192,205],"input":[24,131,193],"data.":[25,114],"However,":[26],"the":[27,41,44,64,68,104,112,126,137,142,146,152,156,173,188,202,206],"pre-trained":[28],"CNN":[29,119],"model":[30],"always":[31],"limited":[34],"ability":[35,66],"be":[37],"spatially":[38],"invariant":[39,49],"image":[42,113],"convolutional":[45],"layers":[46],"are":[47],"not":[48,185],"general":[51],"affine":[52],"transformations,":[53],"such":[54],"rotation":[56],"and":[57,87,100,108,148],"scale.":[58],"This":[59],"scenario":[60],"will":[61],"extremely":[62],"affect":[63],"generalization":[65],"trained":[69],"CNNs.":[70],"In":[71],"this":[72,76],"work,":[73],"we":[74,90,144],"address":[75],"problem":[77],"by":[78],"leveraging":[79],"recent":[80,211],"advances":[81],"in":[82],"spatial":[83],"transform":[84],"(STN)":[86],"XGBoost.":[88],"Specifically,":[89],"propose":[91],"framework":[93],"which":[94],"consists":[95],"an":[97],"embedded":[98],"STN":[99,122],"XGBoost":[101,150],"geometric":[105,127,189],"invariance":[106,128,190],"features":[107,129,191],"discrimination":[109,153],"representation":[110,154,204],"We":[115,159],"firstly":[116],"establish":[117],"embedding":[120],"effectively":[124],"extract":[125],"image;":[132],"then":[133],"instead":[134],"employing":[136],"conventional":[138],"softmax":[139],"unit":[140],"classifier,":[143],"adopt":[145],"high-efficient":[147],"faster":[149],"learned":[157,207],"features.":[158],"conduct":[160],"series":[162],"experiments":[164],"based":[165],"on":[166],"benchmark":[167],"dataset":[168],"Fashion":[169],"MNIST":[170],"verify":[172],"effectiveness":[174],"our":[176,182],"framework.":[177],"The":[178],"results":[179],"demonstrate":[180],"that":[181],"method":[183],"can":[184],"only":[186],"learn":[187],"images,":[194],"but":[195],"also":[196],"have":[197],"superior":[199],"performance":[200],"discriminate":[203],"features,":[208],"compared":[209],"with":[210],"several":[212],"representative":[213],"methods.":[214]},"counts_by_year":[],"updated_date":"2026-07-29T14:22:42.915294","created_date":"2025-10-10T00:00:00"}
