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1. Identity statement
Reference TypeConference Paper (Conference Proceedings)
Sitesibgrapi.sid.inpe.br
Holder Codeibi 8JMKD3MGPEW34M/46T9EHH
Identifier8JMKD3MGPAW/3M9SC2B
Repositorysid.inpe.br/sibgrapi/2016/08.18.01.45
Last Update2016:08.18.01.45.10 (UTC) administrator
Metadata Repositorysid.inpe.br/sibgrapi/2016/08.18.01.45.10
Metadata Last Update2022:05.18.22.21.08 (UTC) administrator
Citation KeyNogueiraVeloSant:2016:StDeLe
TitleStatistical and Deep Learning Algorithms for Annotating and Parsing Clothing Items in Fashion Photographs
FormatOn-line
Year2016
Access Date2024, Apr. 27
Number of Files1
Size2511 KiB
2. Context
Author1 Nogueira, Keiller
2 Veloso, Adriano Alonso
3 Santos, Jefersson Alex dos
Affiliation1 Universidade Federal de Minas Gerais (UFMG)
2 Universidade Federal de Minas Gerais (UFMG)
3 Universidade Federal de Minas Gerais (UFMG)
EditorAliaga, Daniel G.
Davis, Larry S.
Farias, Ricardo C.
Fernandes, Leandro A. F.
Gibson, Stuart J.
Giraldi, Gilson A.
Gois, João Paulo
Maciel, Anderson
Menotti, David
Miranda, Paulo A. V.
Musse, Soraia
Namikawa, Laercio
Pamplona, Mauricio
Papa, João Paulo
Santos, Jefersson dos
Schwartz, William Robson
Thomaz, Carlos E.
e-Mail Addresskeillernogueira@gmail.com
Conference NameConference on Graphics, Patterns and Images, 29 (SIBGRAPI)
Conference LocationSão José dos Campos, SP, Brazil
Date4-7 Oct. 2016
PublisherSociedade Brasileira de Computação
Publisher CityPorto Alegre
Book TitleProceedings
Tertiary TypeMaster's or Doctoral Work
History (UTC)2016-08-18 01:45:10 :: keillernogueira@gmail.com -> administrator ::
2022-05-18 22:21:08 :: administrator -> :: 2016
3. Content and structure
Is the master or a copy?is the master
Content Stagecompleted
Transferable1
KeywordsMachine Learning
Image Annotation
Image Parsing
Descriptor
Visual Dictionary
Neural Networks
Deep Learning
AbstractClothing identification has important roles in several areas. In this work, we present effective algorithms to automatically annotate and parse clothes from social media data. Clothing annotation tries to recognize each garment item that appears in a photo. Clothing parsing, in turn, locates and annotates each garment item in a photo. Both task pose interesting challenges for existing vision and recognition algorithms, such as distinguishing similar clothes or creating a pattern of a specific item. For the first task, two approaches, based on traditional algorithms, were proposed: (i) the pointwise one, and (ii) a multi-instance or pairwise approach. An evaluation show improvements of the proposed methods when compared to popular first choice algorithms that range from 20% to 30%. For the second task, a multi-scale convolutional network was proposed. At the end, a class is associated with each patch of the image. Experiments shows that the proposed method achieves promising results.
Arrangementurlib.net > SDLA > Fonds > SIBGRAPI 2016 > Statistical and Deep...
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4. Conditions of access and use
data URLhttp://urlib.net/ibi/8JMKD3MGPAW/3M9SC2B
zipped data URLhttp://urlib.net/zip/8JMKD3MGPAW/3M9SC2B
Languageen
Target Filesibgrapi2016-wtd-camera_ready.pdf
User Groupkeillernogueira@gmail.com
Visibilityshown
Update Permissionnot transferred
5. Allied materials
Mirror Repositorysid.inpe.br/banon/2001/03.30.15.38.24
Next Higher Units8JMKD3MGPAW/3M2D4LP
Citing Item Listsid.inpe.br/sibgrapi/2016/07.02.23.50 6
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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