| Full text | |
| Author(s): |
Afonso, Luis
;
Vidal, Alexandre
;
Kuroda, Michelle
;
Falcao, Alexandre
;
Papa, Joao
;
IEEE
Total Authors: 6
|
| Document type: | Journal article |
| Source: | 2016 29TH SIBGRAPI CONFERENCE ON GRAPHICS, PATTERNS AND IMAGES (SIBGRAPI); v. N/A, p. 7-pg., 2016-01-01. |
| 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. (AU) | |
| FAPESP's process: | 14/16250-9 - On the parameter optimization in machine learning techniques: advances and paradigms |
| Grantee: | João Paulo Papa |
| Support Opportunities: | Regular Research Grants |