Optimizing Optimum-Path Forest Classification for Huge Datasets

Authors:
Joao P. Papa;Fabio A. M. Cappabianco;Alexandre Xavier Falcao
Affiliations:
-;-;-
Venue:
ICPR '10 Proceedings of the 2010 20th International Conference on Pattern Recognition
Year:
2010

Citing 0
Cited 6

Improving the accuracy of the optimum-path forest supervised classifier for large datasets

CIARP'10 Proceedings of the 15th Iberoamerican congress conference on Progress in pattern recognition, image analysis, computer vision, and applications
Active learning paradigms for CBIR systems based on optimum-path forest classification

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Efficient supervised optimum-path forest classification for large datasets

Pattern Recognition
Incorporating multiple distance spaces in optimum-path forest classification to improve feedback-based learning

Computer Vision and Image Understanding
Brain tissue MR-image segmentation via optimum-path forest clustering

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A comparison between k-Optimum Path Forest and k-Nearest Neighbors supervised classifiers

Pattern Recognition Letters

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Abstract

Traditional pattern recognition techniques can not handle the classification of large datasets with both efficiency and effectiveness. In this context, the Optimum-Path Forest (OPF) classifier was recently introduced, trying to achieve high recognition rates and low computational cost. Although OPF was much faster than Support Vector Machines for training, it was slightly slower for classification. In this paper, we present the Efficient OPF (EOPF), which is an enhanced and faster version of the traditional OPF, and validate it for the automatic recognition of white matter and gray matter in magnetic resonance images of the human brain.