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		<doi>10.1109/SIBGRAPI.2016.062</doi>
		<citationkey>AfonsoVidKurFalPap:2016:LeClSe</citationkey>
		<title>Learning to Classify Seismic Images with Deep Optimum-Path Forest</title>
		<format>On-line</format>
		<year>2016</year>
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		<author>Afonso, Luis Claudio Sugi,</author>
		<author>Vidal, Alexandre Campane,</author>
		<author>Kuroda, Michelle Chaves,</author>
		<author>Falcao, Alexandre Xavier,</author>
		<author>Papa, Joao Paulo,</author>
		<affiliation>Federal University of Sao Carlos</affiliation>
		<affiliation>University of Campinas</affiliation>
		<affiliation>University of Campinas</affiliation>
		<affiliation>University of Campinas</affiliation>
		<affiliation>Sao Paulo State University</affiliation>
		<editor>Aliaga, Daniel G.,</editor>
		<editor>Davis, Larry S.,</editor>
		<editor>Farias, Ricardo C.,</editor>
		<editor>Fernandes, Leandro A. F.,</editor>
		<editor>Gibson, Stuart J.,</editor>
		<editor>Giraldi, Gilson A.,</editor>
		<editor>Gois, João Paulo,</editor>
		<editor>Maciel, Anderson,</editor>
		<editor>Menotti, David,</editor>
		<editor>Miranda, Paulo A. V.,</editor>
		<editor>Musse, Soraia,</editor>
		<editor>Namikawa, Laercio,</editor>
		<editor>Pamplona, Mauricio,</editor>
		<editor>Papa, João Paulo,</editor>
		<editor>Santos, Jefersson dos,</editor>
		<editor>Schwartz, William Robson,</editor>
		<editor>Thomaz, Carlos E.,</editor>
		<e-mailaddress>papa.joaopaulo@gmail.com</e-mailaddress>
		<conferencename>Conference on Graphics, Patterns and Images, 29 (SIBGRAPI)</conferencename>
		<conferencelocation>São José dos Campos, SP, Brazil</conferencelocation>
		<date>4-7 Oct. 2016</date>
		<publisher>IEEE Computer Society´s Conference Publishing Services</publisher>
		<publisheraddress>Los Alamitos</publisheraddress>
		<booktitle>Proceedings</booktitle>
		<tertiarytype>Full Paper</tertiarytype>
		<transferableflag>1</transferableflag>
		<versiontype>finaldraft</versiontype>
		<keywords>Optimum-Path Forest, Image Clustering, Deep Representations, Seismic Images.</keywords>
		<abstract>Due to the lack of labeled information, clustering techniques have been paramount in the last years once more. In this paper, inspired by the deep learning phenomenon, we presented a multi-scale approach to obtain more refined cluster representations of the Optimum-Path Forest (OPF) classifier, which has obtained promising results in a number of works in the literature. Here, we propose to fill a gap in OPF-based works by using a deep-driven representation of the feature space. Additionally, we validated the work in the context of high resolution seismic images aiming at petroleum exploration, as well as in general-purpose applications. Quantitative and qualitative analysis are conducted in order to assess the robustness of the proposed approach.</abstract>
		<language>en</language>
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