%0 Conference Proceedings
%4 sid.inpe.br/sibgrapi/2016/07.08.22.58
%2 sid.inpe.br/sibgrapi/2016/07.08.22.58.58
%@doi 10.1109/SIBGRAPI.2016.062
%T Learning to Classify Seismic Images with Deep Optimum-Path Forest
%D 2016
%A Afonso, Luis Claudio Sugi,
%A Vidal, Alexandre Campane,
%A Kuroda, Michelle Chaves,
%A Falcao, Alexandre Xavier,
%A Papa, Joao Paulo,
%@affiliation Federal University of Sao Carlos
%@affiliation University of Campinas
%@affiliation University of Campinas
%@affiliation University of Campinas
%@affiliation Sao Paulo State University
%E Aliaga, Daniel G.,
%E Davis, Larry S.,
%E Farias, Ricardo C.,
%E Fernandes, Leandro A. F.,
%E Gibson, Stuart J.,
%E Giraldi, Gilson A.,
%E Gois, João Paulo,
%E Maciel, Anderson,
%E Menotti, David,
%E Miranda, Paulo A. V.,
%E Musse, Soraia,
%E Namikawa, Laercio,
%E Pamplona, Mauricio,
%E Papa, João Paulo,
%E Santos, Jefersson dos,
%E Schwartz, William Robson,
%E Thomaz, Carlos E.,
%B Conference on Graphics, Patterns and Images, 29 (SIBGRAPI)
%C São José dos Campos, SP, Brazil
%8 4-7 Oct. 2016
%I IEEE Computer Society´s Conference Publishing Services
%J Los Alamitos
%S Proceedings
%K Optimum-Path Forest, Image Clustering, Deep Representations, Seismic Images.
%X 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.
%@language en
%3 paper.pdf