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		<citationkey>SantosBati:2007:FeSeEq</citationkey>
		<title>Feature selection with equalized salience measures and its application to segmentation</title>
		<format>Printed, On-line.</format>
		<year>2007</year>
		<numberoffiles>1</numberoffiles>
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		<author>Santos,  Davi Pereira dos,</author>
		<author>Batista, Joao,</author>
		<affiliation>ICMC - USP</affiliation>
		<affiliation>ICMC - USP</affiliation>
		<editor>Falcão, Alexandre Xavier,</editor>
		<editor>Lopes, Hélio Côrtes Vieira,</editor>
		<conferencename>Brazilian Symposium on Computer Graphics and Image Processing, 20 (SIBGRAPI)</conferencename>
		<conferencelocation>Belo Horizonte</conferencelocation>
		<date>Oct. 7-10, 2007</date>
		<booktitle>Proceedings</booktitle>
		<publisher>IEEE Computer Society</publisher>
		<publisheraddress>Los Alamitos</publisheraddress>
		<tertiarytype>Full Paper</tertiarytype>
		<transferableflag>1</transferableflag>
		<contenttype>External Contribution</contenttype>
		<keywords>Feature Selection, texture, salience  measures.</keywords>
		<abstract>Segmentation is a crucial step in  Computer Vision in which texture plays an important role. The existence of a large amount of methods from which texture can be  computed is, sometimes, a hurdle to overcome when it comes to modeling  solutions for texture-based segmentation. Following the excellence of the natural vision system and its generality, this work has adopted a feature selection method based on salience of synaptic connections of a Multilayer Perceptron neural network. Unlike traditional approaches, this paper introduces an equalization scheme to salience measures which contributed to significantly improve the selection of the most suitable features and, hence, yield better segmentation. The proposed method is compared with exhaustive search according to the Jeffrey-Matusita distance criterion. Segmentation for images of natural scenes has also been provided as a probable application  of the method.</abstract>
		<language>en</language>
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