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1. Identity statement
Reference TypeConference Paper (Conference Proceedings)
Sitesibgrapi.sid.inpe.br
Holder Codeibi 8JMKD3MGPEW34M/46T9EHH
Identifier8JMKD3MGPAW/3PJ6MCH
Repositorysid.inpe.br/sibgrapi/2017/09.05.02.31
Last Update2017:09.11.23.33.15 (UTC) administrator
Metadata Repositorysid.inpe.br/sibgrapi/2017/09.05.02.31.10
Metadata Last Update2022:05.18.22.18.24 (UTC) administrator
Citation KeyPereiraSant:2017:ImReLe
TitleImage representation learning by color quantization optimization
FormatOn-line
Year2017
Access Date2024, May 01
Number of Files1
Size3513 KiB
2. Context
Author1 Pereira, Érico Marco Dias Alves
2 dos Santos, Jefersson Alex
Affiliation1 Universidade Federal de Minas Gerais
2 Universidade Federal de Minas Gerais
EditorTorchelsen, Rafael Piccin
Nascimento, Erickson Rangel do
Panozzo, Daniele
Liu, Zicheng
Farias, Mylène
Viera, Thales
Sacht, Leonardo
Ferreira, Nivan
Comba, João Luiz Dihl
Hirata, Nina
Schiavon Porto, Marcelo
Vital, Creto
Pagot, Christian Azambuja
Petronetto, Fabiano
Clua, Esteban
Cardeal, Flávio
e-Mail Addressemarco.pereira@dcc.ufmg.br
Conference NameConference on Graphics, Patterns and Images, 30 (SIBGRAPI)
Conference LocationNiterói, RJ, Brazil
Date17-20 Oct. 2017
PublisherSociedade Brasileira de Computação
Publisher CityPorto Alegre
Book TitleProceedings
Tertiary TypeUndergraduate Work
History (UTC)2017-09-05 02:31:10 :: emarco.pereira@dcc.ufmg.br -> administrator ::
2017-09-09 18:59:05 :: administrator -> emarco.pereira@dcc.ufmg.br :: 2017
2017-09-11 23:33:16 :: emarco.pereira@dcc.ufmg.br -> administrator :: 2017
2022-05-18 22:18:24 :: administrator -> :: 2017
3. Content and structure
Is the master or a copy?is the master
Content Stagecompleted
Transferable1
Keywordsrepresentation learning
color quantization
CBIR
genetic algorithm
feature extraction
AbstractThe state-of-art methods of representation learning, based on Deep Neural Networks, present serious drawbacks regarding usage complexity and resources consumption, leaving space for simpler alternatives. We proposed two approaches of a Representation Learning method which aims to provide more effective and compact image representations by optimizing the colour quantization for the image domain. Our hypothesis is that changes in the quantization affect the description quality of the features enabling representation improvements. We evaluated the method performing experiments for the task of Content-Based Image Retrieval on eight known datasets. The results showed that the first approach, focused on representation effectiveness, produced representations that outperforms the baseline in all the tested scenarios. And the second, focused on compactness, was able to produce superior results maintaining or even reducing the dimensionality and representations until 25% smaller that presented statistically equivalent performance.
Arrangementurlib.net > SDLA > Fonds > SIBGRAPI 2017 > Image representation learning...
doc Directory Contentaccess
source Directory Content
Pereira_DosSantos_2017.pdf 04/09/2017 23:31 3.4 MiB
agreement Directory Content
agreement.html 04/09/2017 23:31 1.2 KiB 
4. Conditions of access and use
data URLhttp://urlib.net/ibi/8JMKD3MGPAW/3PJ6MCH
zipped data URLhttp://urlib.net/zip/8JMKD3MGPAW/3PJ6MCH
Languageen
Target FilePereira_DosSantos_2017.pdf
User Groupemarco.pereira@dcc.ufmg.br
Visibilityshown
Update Permissionnot transferred
5. Allied materials
Mirror Repositorysid.inpe.br/banon/2001/03.30.15.38.24
Next Higher Units8JMKD3MGPAW/3PKCC58
Citing Item Listsid.inpe.br/sibgrapi/2017/09.12.13.04 7
Host Collectionsid.inpe.br/banon/2001/03.30.15.38
6. Notes
Empty Fieldsarchivingpolicy archivist area callnumber contenttype copyholder copyright creatorhistory descriptionlevel dissemination doi edition electronicmailaddress group isbn issn label lineage mark nextedition notes numberofvolumes orcid organization pages parameterlist parentrepositories previousedition previouslowerunit progress project readergroup readpermission resumeid rightsholder schedulinginformation secondarydate secondarykey secondarymark secondarytype serieseditor session shorttitle sponsor subject tertiarymark type url versiontype volume


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