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Reference TypeConference Paper (Conference Proceedings)
Sitesibgrapi.sid.inpe.br
Identifier8JMKD3MGPBW34M/3JMNT72
Repositorysid.inpe.br/sibgrapi/2015/06.19.21.00
Last Update2015:06.19.21.00.11 (UTC) pedrosennapsc@gmail.com
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Metadata Last Update2020:02.19.02.14.03 (UTC) administrator
Citation KeyCamposDrumBast:2015:BaFeBa
TitleBMAX: a bag of features based method for image classification
FormatOn-line
Year2015
Access Date2021, Dec. 01
Number of Files1
Size839 KiB
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Author1 Campos, Pedro Senna de
2 Drummond, Isabela Neves
3 Bastos, Guilherme Sousa
Affiliation1 UNIFEI
2 UNIFEI
3 UNIFEI
EditorPapa, Joćo Paulo
Sander, Pedro Vieira
Marroquim, Ricardo Guerra
Farrell, Ryan
e-Mail Addresspedrosennapsc@gmail.com
Conference NameConference on Graphics, Patterns and Images, 28 (SIBGRAPI)
Conference LocationSalvador
DateAug. 26-29, 2015
PublisherIEEE Computer Society
Publisher CityLos Alamitos
Book TitleProceedings
Tertiary TypeFull Paper
History (UTC)2015-06-19 21:00:11 :: pedrosennapsc@gmail.com -> administrator ::
2020-02-19 02:14:03 :: administrator -> :: 2015
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Transferable1
Content TypeExternal Contribution
KeywordsImage classification
bag-of-features
HMAX
low feature usage
AbstractThis work presents an image classification method based on bag of features, that needs less local features extracted for create a representative description of the image. The feature vector creation process of our approach is inspired in the cortex-like mechanisms used in "Hierarchical Model and X" proposed by Riesenhuber & Poggio. Bag of Max Features - BMAX works with the distance from each visual word to its nearest feature found in the image, instead of occurrence frequency of each word. The motivation to reduce the amount of features used is to obtain a better relation between recognition rate and computational cost. We perform tests in three public images databases generally used as benchmark, and varying the quantity of features extracted. The proposed method can spend up to 60 times less local features than the standard bag of features, with estimate loss around 5% considering recognition rate, that represents up to 17 times reduction in the running time.
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Languageen
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Next Higher Units8JMKD3MGPBW34M/3K24PF8
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