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1. Identity statement
Reference TypeConference Paper (Conference Proceedings)
Sitesibgrapi.sid.inpe.br
Holder Codeibi 8JMKD3MGPEW34M/46T9EHH
Identifier8JMKD3MGPEW34M/3U2NP8L
Repositorysid.inpe.br/sibgrapi/2019/09.10.17.27
Last Update2019:09.10.17.27.20 (UTC) administrator
Metadata Repositorysid.inpe.br/sibgrapi/2019/09.10.17.27.20
Metadata Last Update2022:06.14.00.09.37 (UTC) administrator
DOI10.1109/SIBGRAPI.2019.00041
Citation KeyLaranjeiraLaceNasc:2019:MoCoOb
TitleOn Modeling Context from Objects with a Long Short-Term Memory for Indoor Scene Recognition
FormatOn-line
Year2019
Access Date2024, Oct. 15
Number of Files1
Size1627 KiB
2. Context
Author1 Laranjeira, Camila
2 Lacerda, Anisio
3 Nascimento, Erickson R.
Affiliation1 Universidade Federal de Minas Gerais
2 Universidade Federal de Minas Gerais
3 Universidade Federal de Minas Gerais
EditorOliveira, Luciano Rebouças de
Sarder, Pinaki
Lage, Marcos
Sadlo, Filip
e-Mail Addressmila.laranjeira@gmail.com
Conference NameConference on Graphics, Patterns and Images, 32 (SIBGRAPI)
Conference LocationRio de Janeiro, RJ, Brazil
Date28-31 Oct. 2019
PublisherIEEE Computer Society
Publisher CityLos Alamitos
Book TitleProceedings
Tertiary TypeFull Paper
History (UTC)2019-09-10 17:27:20 :: mila.laranjeira@gmail.com -> administrator ::
2022-06-14 00:09:37 :: administrator -> mila.laranjeira@gmail.com :: 2019
3. Content and structure
Is the master or a copy?is the master
Content Stagecompleted
Transferable1
Version Typefinaldraft
KeywordsIndoor Scene Recognition
Recurrent Neural Networks
AbstractRecognizing indoor scenes is still regarded an open challenge on the Computer Vision field. Indoor scenes can be well represented by their composing objects, which can vary in angle, appearance, besides often being partially occluded. Even though Convolutional Neural Networks are remarkable for image-related problems, the top performances on indoor scenes are from approaches modeling the intricate relationship of objects. Knowing that Recurrent Neural Networks were designed to model structure from a given sequence, we propose representing an image as a sequence of object-level information in order to feed a bidirectional Long Short-Term Memory network trained for scene classification. We perform a Many-to-Many training approach, such that each element outputs a scene prediction, allowing us to use each prediction to boost recognition. Our method outperforms RNN-based approaches on MIT67, an entirely indoor dataset, while also improved over the most successful methods through an ensemble of classifiers.
Arrangement 1urlib.net > SDLA > Fonds > SIBGRAPI 2019 > On Modeling Context...
Arrangement 2urlib.net > SDLA > Fonds > Full Index > On Modeling Context...
doc Directory Contentaccess
source Directory Contentthere are no files
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4. Conditions of access and use
data URLhttp://urlib.net/ibi/8JMKD3MGPEW34M/3U2NP8L
zipped data URLhttp://urlib.net/zip/8JMKD3MGPEW34M/3U2NP8L
Languageen
Target FilePID6127653.pdf
User Groupmila.laranjeira@gmail.com
Visibilityshown
Update Permissionnot transferred
5. Allied materials
Mirror Repositorysid.inpe.br/banon/2001/03.30.15.38.24
Next Higher Units8JMKD3MGPEW34M/3UA4FNL
8JMKD3MGPEW34M/3UA4FPS
8JMKD3MGPEW34M/4742MCS
Citing Item Listsid.inpe.br/sibgrapi/2019/10.25.18.30.33 29
sid.inpe.br/sibgrapi/2022/06.10.21.49 2
sid.inpe.br/banon/2001/03.30.15.38.24 1
Host Collectionsid.inpe.br/banon/2001/03.30.15.38
6. Notes
Empty Fieldsarchivingpolicy archivist area callnumber contenttype copyholder copyright creatorhistory descriptionlevel dissemination 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 volume
7. Description control
e-Mail (login)mila.laranjeira@gmail.com
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