Abstract
In this paper, we present a novel formulation of Random Forests, which introduces order statistics into the splitting functions of nodes. Order statistics, in general, neglect the absolute values of single feature dimensions and just consider the ordering of different feature dimensions. Recent works showed that such statistics have more discriminative power than just observing single feature dimensions. However, they were just used as a preprocessing step, transforming data into a higher dimensional feature space, or were limited to just consider two feature dimensions. In contrast, we integrate order statistics into the Random Forest framework, and thus avoid explicit mapping onto higher dimensional spaces. In this way, we can also exploit more than two feature dimensions, resulting in increased discriminative power. Moreover, we show that this idea can easily be extended for the popular Hough Forest framework. The experimental results demonstrate that using splitting functions building on order statistics can improve both, the performance for classification tasks (using Random Forests) and for object detection (using Hough Forests).
The work was supported by the FFG projects Human Factors Technologies and Services (2371236) and Mobile Traffic Checker (8258408).
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Schulter, S., Roth, P.M., Bischof, H. (2013). Ordinal Random Forests for Object Detection. In: Weickert, J., Hein, M., Schiele, B. (eds) Pattern Recognition. GCPR 2013. Lecture Notes in Computer Science, vol 8142. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-40602-7_29
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DOI: https://doi.org/10.1007/978-3-642-40602-7_29
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