@InProceedings{LucenaFerrOlivMach:2015:AtLoAu,
author = "Lucena, David Jones Ferreira de and Ferreira Junior, Jos{\'e}
Raniery and Oliveira, Marcelo Costa and Machado, Aydano Pamponet",
affiliation = "{Federal University of Alagoas} and {Federal University of
Alagoas} and {Federal University of Alagoas} and {Federal
University of Alagoas}",
title = "Atualiza{\c{c}}{\~a}o local autom{\'a}tica de pesos de
atributos para recupera{\c{c}}{\~a}o de n{\'o}dulos pulmonares
similares",
booktitle = "Proceedings...",
year = "2015",
editor = "Rios, Ricardo Araujo and Paiva, Afonso",
organization = "Conference on Graphics, Patterns and Images, 28. (SIBGRAPI)",
publisher = "Sociedade Brasileira de Computa{\c{c}}{\~a}o",
address = "Porto Alegre",
keywords = "Content-based image retrieval, information retrieval, decision
support, update weighing attributes, lung cancer.",
abstract = "Lung 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.",
conference-location = "Salvador, BA, Brazil",
conference-year = "26-29 Aug. 2015",
language = "pt",
ibi = "8JMKD3MGPBW34M/3JRLK92",
url = "http://urlib.net/ibi/8JMKD3MGPBW34M/3JRLK92",
targetfile = "SIBGRAPI-VERSAO-APROVADA2.pdf",
urlaccessdate = "2024, May 06"
}