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1. Identity statement
Reference TypeConference Paper (Conference Proceedings)
Sitesibgrapi.sid.inpe.br
Holder Codeibi 8JMKD3MGPEW34M/46T9EHH
Identifier8JMKD3MGPAW/3PJ55BB
Repositorysid.inpe.br/sibgrapi/2017/09.04.17.48
Last Update2017:09.04.17.48.38 (UTC) administrator
Metadata Repositorysid.inpe.br/sibgrapi/2017/09.04.17.48.38
Metadata Last Update2022:05.18.22.18.23 (UTC) administrator
Citation KeySilvaNasc:2017:ReInSc
TitleRepresenting Indoor Scenes as a Sparse Composition of Feature Segments
FormatOn-line
Year2017
Access Date2024, Oct. 08
Number of Files1
Size2279 KiB
2. Context
Author1 Silva, Camila Laranjeira da
2 Nascimento, Erickson Rangel
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 Addressmila.laranjeira@gmail.com
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 TypeWork in Progress
History (UTC)2017-09-04 17:48:38 :: mila.laranjeira@gmail.com -> administrator ::
2022-05-18 22:18:23 :: administrator -> :: 2017
3. Content and structure
Is the master or a copy?is the master
Content Stagecompleted
Transferable1
KeywordsIndoor Scene Recognition
Semantic Segmentation
Regularization
AbstractResearchers in the fields of Computer Vision and Pattern Recognition have been trying to tackle the problem of scene recognition for many years. Several approaches rely on the assumption that object-level information can be highly discriminatory, which has been extensively validated in the literature. We propose an approach that merges sparse semantic segmentation features with object features, composing a sparse representation of feature segments, as an attempt to represent the composition of objects of a given scene. Our premise is that by adding sparsity constraints to a semantic segmentation feature, we represent a small amount of well chosen objects or parts of objects. We expect this will add robustness to the final feature, since it will recognize a given scene by its most distinctive segments, thus increasing the generalization power of the representation. According to our results, the methodology seems promising, but it is strongly affected by the poor performance of segmentation features on classes containing small objects.
Arrangementurlib.net > SDLA > Fonds > SIBGRAPI 2017 > Representing Indoor Scenes...
doc Directory Contentaccess
source Directory Contentthere are no files
agreement Directory Content
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4. Conditions of access and use
data URLhttp://urlib.net/ibi/8JMKD3MGPAW/3PJ55BB
zipped data URLhttp://urlib.net/zip/8JMKD3MGPAW/3PJ55BB
Languageen
Target FileSibgrapi_2017_WiP_camera-ready.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 Units8JMKD3MGPAW/3PKCC58
Citing Item Listsid.inpe.br/sibgrapi/2017/09.12.13.04 36
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 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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