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Reference TypeConference Paper (Conference Proceedings)
Sitesibgrapi.sid.inpe.br
Identifier8JMKD3MGPBW34M/3JRLK92
Repositorysid.inpe.br/sibgrapi/2015/07.13.22.57
Last Update2015:07.13.22.57.56 (UTC) administrator
Metadatasid.inpe.br/sibgrapi/2015/07.13.22.57.57
Metadata Last Update2016:06.03.21.18.36 (UTC) administrator
Citation KeyLucenaFerrOlivMach:2015:AtLoAu
TitleAtualização local automática de pesos de atributos para recuperação de nódulos pulmonares similares
FormatOn-line
Year2015
Access Date2021, Dec. 03
Number of Files1
Size572 KiB
Context area
Author1 Lucena, David Jones Ferreira de
2 Ferreira Junior, José Raniery
3 Oliveira, Marcelo Costa
4 Machado, Aydano Pamponet
Affiliation1 Federal University of Alagoas
2 Federal University of Alagoas
3 Federal University of Alagoas
4 Federal University of Alagoas
EditorRios, Ricardo Araujo
Paiva, Afonso
e-Mail Addressdavidjones162@gmail.com
Conference NameConference on Graphics, Patterns and Images, 28 (SIBGRAPI)
Conference LocationSalvador
DateAug. 26-29, 2015
PublisherSociedade Brasileira de Computação
Publisher CityPorto Alegre
Book TitleProceedings
Tertiary TypeWork in Progress
History (UTC)2015-07-13 22:57:57 :: davidjones162@gmail.com -> administrator ::
2016-06-03 21:18:36 :: administrator -> :: 2015
Content and structure area
Is the master or a copy?is the master
Content Stagecompleted
Transferable1
KeywordsContent-based image retrieval
information retrieval
decision support
update weighing attributes
lung cancer
AbstractLung cancer is the third most common among the types of cancer existing in the world, staying back of prostate cancer in men and breast cancer in women. Computer-Aided (CAD) systems have been built in order to help experts identify and classify lung nodules. One type of CAD that has shown good results is the Content-Based Image Retrieval (CBIR). But one of the biggest challenges of CBIR is to define the appropriate measure for evaluating the similarity, other is to find a way to address the gap between the features used by experts to evaluate the images and attributes extracted from it segmentation. This work proposes a CBIR architecture to automatically calculate the weights of the attributes based on local learning to reflect the user interpretation in image retrieval process, reducing the semantic gap and improving performance in the recovery based on content.
Arrangementurlib.net > SDLA > SIBGRAPI 2015 > Atualização local automática...
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data URLhttp://urlib.net/ibi/8JMKD3MGPBW34M/3JRLK92
zipped data URLhttp://urlib.net/zip/8JMKD3MGPBW34M/3JRLK92
Languagept
Target FileSIBGRAPI-VERSAO-APROVADA2.pdf
User Groupadministrator
davidjones162@gmail.com
Visibilityshown
Update Permissionnot transferred
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Mirror Repositorysid.inpe.br/banon/2001/03.30.15.38.24
Next Higher Units8JMKD3MGPBW34M/3K24PF8
Host Collectionsid.inpe.br/banon/2001/03.30.15.38
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