| Author(s): |
Iwashita, Adriana S.
;
Romero, Marcos V. T.
;
Baldassin, Alexandro
;
Costa, Kelton A. P.
;
Papa, Joao P.
;
Battiato, S
;
Braz, J
Total Authors: 7
|
| Document type: | Journal article |
| Source: | PROCEEDINGS OF THE 2014 9TH INTERNATIONAL CONFERENCE ON COMPUTER VISION, THEORY AND APPLICATIONS (VISAPP 2014), VOL 2; v. N/A, p. 8-pg., 2014-01-01. |
| Abstract | |
In this paper, we presented a Graphics Processing Unit (GPU)-based training algorithm for Optimum-Path Forest (OPF) classifier. The proposed approach employs the idea of a vector-matrix multiplication to speed up both traditional OPF training algorithm and a recently proposed Central Processing Unit (CPU)-based OPF training algorithm. Experiments in several public datasets have showed the efficiency of the proposed approach, which demonstrated to be up to 14 times faster for some datasets. To the best of our knowledge, this is the first GPU-based implementation for OPF training algorithm. (AU) | |
| FAPESP's process: | 09/16206-1 - New trends on optimum-path forest-based pattern recognition |
| Grantee: | João Paulo Papa |
| Support Opportunities: | Research Grants - Young Investigators Grants |
| FAPESP's process: | 10/12697-8 - On the Implementation of the Optimum-Path Forest Training Algorithm in GPU |
| Grantee: | Adriana Sayuri Iwashita |
| Support Opportunities: | Scholarships in Brazil - Master |