Learning to classify seismic images with deep optimum-path forest

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Afonso, Luis
Vidal, Alexandre
Kuroda, Michelle
Falcao, Alexandre Xavier
Papa, Joao P. [UNESP]

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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.



Deep Representations, Image Clustering, Optimum-Path Forest, Seismic Images

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Proceedings - 2016 29th SIBGRAPI Conference on Graphics, Patterns and Images, SIBGRAPI 2016, p. 401-407.