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1. Identity statement
Reference TypeConference Paper (Conference Proceedings)
Sitesibgrapi.sid.inpe.br
Holder Codeibi 8JMKD3MGPEW34M/46T9EHH
Identifier8JMKD3MGPEW34M/438SL2H
Repositorysid.inpe.br/sibgrapi/2020/09.14.16.01
Last Update2020:09.14.16.01.01 (UTC) administrator
Metadata Repositorysid.inpe.br/sibgrapi/2020/09.14.16.01.01
Metadata Last Update2022:06.14.00.00.02 (UTC) administrator
DOI10.1109/SIBGRAPI51738.2020.00036
Citation KeyRuizKrinTodt:2020:ImDaAu
TitleIDA: Improved Data Augmentation Applied to Salient Object Detection
FormatOn-line
Year2020
Access Date2024, May 04
Number of Files1
Size2655 KiB
2. Context
Author1 Ruiz, Daniel Vitor
2 Krinski, Bruno Alexandre
3 Todt, Eduardo
Affiliation1 Federal Univesity of Paraná
2 Federal Univesity of Paraná
3 Federal Univesity of Paraná
EditorMusse, Soraia Raupp
Cesar Junior, Roberto Marcondes
Pelechano, Nuria
Wang, Zhangyang (Atlas)
e-Mail Addressdvruiz@inf.ufpr.br
Conference NameConference on Graphics, Patterns and Images, 33 (SIBGRAPI)
Conference LocationPorto de Galinhas (virtual)
Date7-10 Nov. 2020
PublisherIEEE Computer Society
Publisher CityLos Alamitos
Book TitleProceedings
Tertiary TypeFull Paper
History (UTC)2020-09-14 16:01:02 :: dvruiz@inf.ufpr.br -> administrator ::
2022-06-14 00:00:02 :: administrator -> dvruiz@inf.ufpr.br :: 2020
3. Content and structure
Is the master or a copy?is the master
Content Stagecompleted
Transferable1
Version Typefinaldraft
Keywordsdata-augmentation
salient-object-detection
image-segmentation
deep-learning
image-inpainting
AbstractIn this paper, we present an Improved Data Augmentation (IDA) technique focused on Salient Object Detection (SOD). Standard data augmentation techniques proposed in the literature, such as image cropping, rotation, flipping, and resizing, only generate variations of the existing examples, providing a limited generalization. Our method combines image inpainting, affine transformations, and the linear combination of different generated background images with salient objects extracted from labeled data. Our proposed technique enables more precise control of the object's position and size while preserving background information. The background choice is based on an inter-image optimization, while object size follows a uniform random distribution within a specified interval, and the object position is intra-image optimal. We show that our method improves the segmentation quality when used for training state-of-the-art neural networks on several famous datasets of the SOD field. Combining our method with others surpasses traditional techniques such as horizontal-flip in 0.52% for F-measure and 1.19% for Precision. We also provide an evaluation in 7 different SOD datasets, with 9 distinct evaluation metrics and an average ranking of the evaluated methods.
Arrangement 1urlib.net > SDLA > Fonds > SIBGRAPI 2020 > IDA: Improved Data...
Arrangement 2urlib.net > SDLA > Fonds > Full Index > IDA: Improved Data...
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4. Conditions of access and use
data URLhttp://urlib.net/ibi/8JMKD3MGPEW34M/438SL2H
zipped data URLhttp://urlib.net/zip/8JMKD3MGPEW34M/438SL2H
Languageen
Target FilePID6611905.pdf
User Groupdvruiz@inf.ufpr.br
Visibilityshown
Update Permissionnot transferred
5. Allied materials
Mirror Repositorysid.inpe.br/banon/2001/03.30.15.38.24
Next Higher Units8JMKD3MGPEW34M/43G4L9S
8JMKD3MGPEW34M/4742MCS
Citing Item Listsid.inpe.br/sibgrapi/2020/10.28.20.46 4
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)dvruiz@inf.ufpr.br
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