Accelerating Random Forests on CPUs and GPUs for Object-Class Image Segmentation

Benedikt Waldvogel
Rheinische Friedrich-Wilhelms Universitat Bonn
Bonn University, 2013

   title={Accelerating Random Forests on CPUs and GPUs for Object-Class Image Segmentation},

   author={Waldvogel, Benedikt},



Random forests are a machine learning method that has recently become popular in the computer vision community to solve image segmentation and object detection tasks. Existing random forest implementations are either general purpose and not efficiently applicable for image segmentation or focus only on the speed of prediction. The implementation for the Microsoft Kinect gaming platform, for instance, achieves real-time speed on a single Microsoft Xbox GPU to recognize the pose of the user. Random forest training, however, has been conducted on a large cluster with 1000 CPU cores. Generally, training on large datasets is computationally demanding and impedes scientific research since the process takes long if a computing cluster is not available or too expensive for the task at hand. It is the goal of this master’s thesis to accelerate training and prediction of random forests for object-class image segmentation on RGB-D datasets by efficiently using CPUs and the massively parallel computing power offered by GPUs. We present an implementation that runs up to 28 times faster on GPU and is capable to train a random forest in less than four minutes on a GPU; thus drastically abbreviating a process that previously took about one whole day on a CPU. Dense classification of RGB-D images in VGA resolution runs in real-time speed on a single mobile GPU.
VN:F [1.9.22_1171]
Rating: 5.0/5 (1 vote cast)
Accelerating Random Forests on CPUs and GPUs for Object-Class Image Segmentation, 5.0 out of 5 based on 1 rating

* * *

* * *

Follow us on Twitter

HGPU group

1660 peoples are following HGPU @twitter

Like us on Facebook

HGPU group

334 people like HGPU on Facebook

* * *

Free GPU computing nodes at hgpu.org

Registered users can now run their OpenCL application at hgpu.org. We provide 1 minute of computer time per each run on two nodes with two AMD and one nVidia graphics processing units, correspondingly. There are no restrictions on the number of starts.

The platforms are

Node 1
  • GPU device 0: nVidia GeForce GTX 560 Ti 2GB, 822MHz
  • GPU device 1: AMD/ATI Radeon HD 6970 2GB, 880MHz
  • CPU: AMD Phenom II X6 @ 2.8GHz 1055T
  • RAM: 12GB
  • OS: OpenSUSE 13.1
  • SDK: nVidia CUDA Toolkit 6.5.14, AMD APP SDK 3.0
Node 2
  • GPU device 0: AMD/ATI Radeon HD 7970 3GB, 1000MHz
  • GPU device 1: AMD/ATI Radeon HD 5870 2GB, 850MHz
  • CPU: Intel Core i7-2600 @ 3.4GHz
  • RAM: 16GB
  • OS: OpenSUSE 12.3
  • SDK: AMD APP SDK 3.0

Completed OpenCL project should be uploaded via User dashboard (see instructions and example there), compilation and execution terminal output logs will be provided to the user.

The information send to hgpu.org will be treated according to our Privacy Policy

HGPU group © 2010-2015 hgpu.org

All rights belong to the respective authors

Contact us: