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1. Identity statement
Reference TypeConference Paper (Conference Proceedings)
Sitesibgrapi.sid.inpe.br
Holder Codeibi 8JMKD3MGPEW34M/46T9EHH
Identifier8JMKD3MGPBW34M/3JLTB85
Repositorysid.inpe.br/sibgrapi/2015/06.14.17.29
Last Update2015:06.14.19.13.07 (UTC) administrator
Metadata Repositorysid.inpe.br/sibgrapi/2015/06.14.17.29.27
Metadata Last Update2022:06.14.00.08.01 (UTC) administrator
DOI10.1109/SIBGRAPI.2015.25
Citation KeyKlossCirSilPedSch:2015:PaLeSq
TitlePartial Least Squares Image Clustering
FormatOn-line
Year2015
Access Date2024, May 02
Number of Files1
Size5758 KiB
2. Context
Author1 Kloss, Ricardo Barbosa
2 Cirne, Marcos Vinicius Mussel
3 Silva, Samira
4 Pedrini, Hélio
5 Schwartz, William Robson
Affiliation1 Universidade Federal de Minas Gerais
2 Universidade de Campinas
3 Universidade Federal de Minas Gerais
4 Universidade de Campinas
5 Universidade Federal de Minas Gerais
EditorPapa, João Paulo
Sander, Pedro Vieira
Marroquim, Ricardo Guerra
Farrell, Ryan
e-Mail Addressrbk@dcc.ufmg.br
Conference NameConference on Graphics, Patterns and Images, 28 (SIBGRAPI)
Conference LocationSalvador, BA, Brazil
Date26-29 Aug. 2015
PublisherIEEE Computer Society
Publisher CityLos Alamitos
Book TitleProceedings
Tertiary TypeFull Paper
History (UTC)2015-06-14 19:13:07 :: rbk@dcc.ufmg.br -> administrator :: 2015
2022-06-14 00:08:01 :: administrator -> :: 2015
3. Content and structure
Is the master or a copy?is the master
Content Stagecompleted
Transferable1
Version Typefinaldraft
KeywordsImage Clustering
Partial Least Squares
Video Summarization
Shot Sampling
AbstractClustering techniques have been widely used in areas that handle massive amounts of data, such as statistics, information retrieval, data mining and image analysis. This work presents a novel image clustering method called Partial Least Square Image Clustering (PLSIC), which employs a one-against-all Partial Least Squares classifier to find image clusters with low redundancy (each cluster represents different visual concept) and high purity (two visual concepts should not be in the same cluster). The main goal of the proposed approach is to find groups of images in an arbitrary set of unlabeled images to convey well defined visual concepts. As a case study, we evaluate the PLSIC to the video summarization problem by means of experiments with 50 videos from various genres of the Open Video Project, comparing summaries generated by the PLSIC with other video summarization approaches found in the literature. A experimental evaluation demonstrates that the proposed method can produce very satisfactory results.
Arrangement 1urlib.net > SDLA > Fonds > SIBGRAPI 2015 > Partial Least Squares...
Arrangement 2urlib.net > SDLA > Fonds > Full Index > Partial Least Squares...
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4. Conditions of access and use
data URLhttp://urlib.net/ibi/8JMKD3MGPBW34M/3JLTB85
zipped data URLhttp://urlib.net/zip/8JMKD3MGPBW34M/3JLTB85
Languageen
Target FilePID3763835.pdf
User Grouprbk@dcc.ufmg.br
Visibilityshown
Update Permissionnot transferred
5. Allied materials
Mirror Repositorysid.inpe.br/banon/2001/03.30.15.38.24
Next Higher Units8JMKD3MGPBW34M/3K24PF8
8JMKD3MGPEW34M/4742MCS
Citing Item Listsid.inpe.br/sibgrapi/2015/08.03.22.49 9
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


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