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		<identifier>8JMKD3MGPBW34M/3JRLK92</identifier>
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		<citationkey>LucenaFerrOlivMach:2015:AtLoAu</citationkey>
		<title>Atualização local automática de pesos de atributos para recuperação de nódulos pulmonares similares</title>
		<format>On-line</format>
		<year>2015</year>
		<numberoffiles>1</numberoffiles>
		<size>572 KiB</size>
		<author>Lucena, David Jones Ferreira de,</author>
		<author>Ferreira Junior, José Raniery,</author>
		<author>Oliveira, Marcelo Costa,</author>
		<author>Machado, Aydano Pamponet,</author>
		<affiliation>Federal University of Alagoas</affiliation>
		<affiliation>Federal University of Alagoas</affiliation>
		<affiliation>Federal University of Alagoas</affiliation>
		<affiliation>Federal University of Alagoas</affiliation>
		<editor>Rios, Ricardo Araujo,</editor>
		<editor>Paiva, Afonso,</editor>
		<e-mailaddress>davidjones162@gmail.com</e-mailaddress>
		<conferencename>Conference on Graphics, Patterns and Images, 28 (SIBGRAPI)</conferencename>
		<conferencelocation>Salvador</conferencelocation>
		<date>Aug. 26-29, 2015</date>
		<publisher>Sociedade Brasileira de Computação</publisher>
		<publisheraddress>Porto Alegre</publisheraddress>
		<booktitle>Proceedings</booktitle>
		<tertiarytype>Work in Progress</tertiarytype>
		<transferableflag>1</transferableflag>
		<keywords>Content-based image retrieval, information retrieval, decision support, update weighing attributes, lung cancer.</keywords>
		<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.</abstract>
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		<targetfile>SIBGRAPI-VERSAO-APROVADA2.pdf</targetfile>
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		<url>http://sibgrapi.sid.inpe.br/rep-/sid.inpe.br/sibgrapi/2015/07.13.22.57</url>
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