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CURFIL: Random Forests for Image Labeling on GPU

Hannes Schulz, Benedikt Waldvogel, Rasha Sheikh, Sven Behnke
University of Bonn, Computer Science Institute VI, Autonomous Intelligent Systems, Friedrich-Ebert-Allee 144, 53113 Bonn
10th International Conference on Computer Vision Theory and Applications (VISAPP), 2015

@article{schulz2015curfil,

   title={CURFIL: Random Forests for Image Labeling on GPU},

   author={Schulz, Hannes and Waldvogel, Benedikt and Sheikh, Rasha and Behnke, Sven},

   year={2015}

}

Random forests are popular classifiers for computer vision tasks such as image labeling or object detection. Learning random forests on large datasets, however, is computationally demanding. Slow learning impedes model selection and scientific research on image features. We present an open-source implementation that significantly accelerates both random forest learning and prediction for image labeling of RGB-D and RGB images on GPU when compared to an optimized multi-core CPU implementation. We use the fast training to conduct hyper-parameter searches, which significantly improves on previous results on the NYU Depth v2 dataset. Our prediction runs in real time at VGA resolution on a mobile GPU and has been used as data term in multiple applications.
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