A novel approach to train random forests on GPU for computer vision applications using local features. (August 2016)

• Record Type: Journal Article
• Authors: Pianu, Daniele; Nerino, Roberto; Ferraris, Claudia; Chimienti, Antonio
• Is Part Of: International journal of high performance computing applications. Volume 30:Number 3(2016:Autumn)
• Page Start: 290
• Page End: 304
• Publication Date: 2016-08
• Subjects: Random Forests -- GPGPU -- computer vision -- local features -- image segmentation -- OpenCL; High performance computing -- Periodicals; Supercomputers -- Periodicals; 004.1105
• DOI: 10.1177/1094342015622672
• Languages: English
• ISSNs: 1094-3420

Abstract

The random forests (RF) classifier has recently gained momentum in the computer vision field, thanks to its successful application in human body tracking, hand pose estimation and object detection. In this article, we present a novel approach to train RF on a graphics processing unit (GPU) for computer vision applications where simple per-pixel features are computed. Besides leveraging the processing power of the GPU to accelerate the training, we reformulate the training problem to limit costly image transfers when it is not possible to store the entire data set in GPU memory. Furthermore, our implementation supports arbitrary image types and allows the user to specify custom features. We extensively compare our approach with the state of the art on publicly available data sets, and we obtain a reduction in training time of up to 18 times. Finally, we train our implementation on a large data set (around 100 K images), demonstrating that our approach is suitable for training RF on the vast data sets typically used in computer vision.