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Reference TypeConference Paper (Conference Proceedings)
Sitesibgrapi.sid.inpe.br
Identifier8JMKD3MGPBW34M/3JLTB85
Repositorysid.inpe.br/sibgrapi/2015/06.14.17.29
Last Update2015:06.14.19.13.07 (UTC) rbk@dcc.ufmg.br
Metadatasid.inpe.br/sibgrapi/2015/06.14.17.29.27
Metadata Last Update2020:02.19.02.14.02 (UTC) administrator
Citation KeyKlossCirSilPedSch:2015:PaLeSq
TitlePartial Least Squares Image Clustering
FormatOn-line
Year2015
Access Date2021, Dec. 03
Number of Files1
Size5758 KiB
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Author1 Kloss, Ricardo Barbosa
2 Cirne, Marcos Vinicius Mussel
3 Silva, Samira
4 Pedrini, Hlio
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, Joo 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
DateAug. 26-29, 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
2020-02-19 02:14:02 :: administrator -> :: 2015
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Is the master or a copy?is the master
Content Stagecompleted
Transferable1
Content TypeExternal Contribution
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.
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Next Higher Units8JMKD3MGPBW34M/3K24PF8
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