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		<doi>10.1109/SIBGRAPI.2005.25</doi>
		<citationkey>NeryCaPáQuMaCa:2005:DeApFe</citationkey>
		<title>Determining the appropriate feature set for fish classification tasks</title>
		<format>On-line</format>
		<year>2005</year>
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		<author>Nery, Marcelo Souza,</author>
		<author>Campos, Mario Fernando Montenegro,</author>
		<author>Pádua, Flávio Luis Cardeal,</author>
		<author>Queiroz Neto, José Pinheiro de,</author>
		<author>Machado, Alexei Manso Correa,</author>
		<author>Carceroni, Rodrigo Lima,</author>
		<affiliation>Departamento de Ciência da Computação - Universidade Federal de Minas Gerais</affiliation>
		<affiliation>Pontifícia Universidade Católica de Minas Gerais</affiliation>
		<affiliation>Centro Federal de Educação Tecnológica do Amazonas</affiliation>
		<editor>Rodrigues, Maria Andréia Formico,</editor>
		<editor>Frery, Alejandro César,</editor>
		<e-mailaddress>cardeal@dcc.ufmg.br</e-mailaddress>
		<conferencename>Brazilian Symposium on Computer Graphics and Image Processing, 18 (SIBGRAPI)</conferencename>
		<conferencelocation>Natal, RN, Brazil</conferencelocation>
		<date>9-12 Oct. 2005</date>
		<publisher>IEEE Computer Society</publisher>
		<publisheraddress>Los Alamitos</publisheraddress>
		<booktitle>Proceedings</booktitle>
		<tertiarytype>Full Paper</tertiarytype>
		<transferableflag>1</transferableflag>
		<versiontype>finaldraft</versiontype>
		<keywords>object classification, feature extraction, feature selection, fish classification.</keywords>
		<abstract>We present a novel fish classification methodology based on a robust feature selection technique. Unlike existing works for fish classification, which propose descriptors and do not analyze their individual impacts in the whole classification task, we propose a general set of features and their correspondent weights that should be used as a priori information by the classifier. In this sense, instead of studying techniques for improving the classifiers structure itself, we consider it as a "black box" and focus our research in the determination of which input information must bring a robust fish discrimination. All the experiments were performed with fish species of Rio Grande river in Minas Gerais, Brazil. This work has been developed as part of a wider research, which has as main goal the development of effective fish ladders for the Brazilian dams.</abstract>
		<language>en</language>
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