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		<site>sibgrapi.sid.inpe.br 802</site>
		<identifier>8JMKD3MGPBW34M/3C9U2C8</identifier>
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		<citationkey>MansanoMaAfPaFaTo:2012:ImImCl</citationkey>
		<title>Improving Image Classification Through Descriptor Combination</title>
		<format>DVD, On-line.</format>
		<year>2012</year>
		<numberoffiles>1</numberoffiles>
		<size>216 KiB</size>
		<author>Mansano, Alex Fernandes,</author>
		<author>Matsuoka, Jessica Akemi,</author>
		<author>Afonso, Luis Claudio Sugi,</author>
		<author>Papa, Joao Paulo,</author>
		<author>Faria, Fabio,</author>
		<author>Torres, Ricardo da Silva,</author>
		<affiliation>UNESP - Univ Estadual Paulista</affiliation>
		<affiliation>UNESP - Univ Estadual Paulista</affiliation>
		<affiliation>UNESP - Univ Estadual Paulista</affiliation>
		<affiliation>UNESP - Univ Estadual Paulista</affiliation>
		<affiliation>University of Campinas</affiliation>
		<affiliation>University of Campinas</affiliation>
		<editor>Freitas, Carla Maria Dal Sasso,</editor>
		<editor>Sarkar, Sudeep,</editor>
		<editor>Scopigno, Roberto,</editor>
		<editor>Silva, Luciano,</editor>
		<e-mailaddress>papa.joaopaulo@gmail.com</e-mailaddress>
		<conferencename>Conference on Graphics, Patterns and Images, 25 (SIBGRAPI)</conferencename>
		<conferencelocation>Ouro Preto</conferencelocation>
		<date>Aug. 22-25, 2012</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>Image classification, Evolutionary algorithms, Descriptor Combination.</keywords>
		<abstract>The efficiency in image classification tasks can be improved using combined information provided by several sources, such as shape, color, and texture visual properties. Although many works proposed to combine different feature vectors, we model the descriptor combination as an optimization problem to be addressed by evolutionary-based techniques, which compute distances between samples that maximize their separability in the feature space. The robustness of the proposed technique is assessed by the Optimum-Path Forest classifier. Experiments showed that the proposed methodology can outperform individual information provided by single descriptors in well-known public datasets.</abstract>
		<language>en</language>
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