Fertilized Forest Library
The fertilized forests project has the aim to provide an easy to use, easy to extend, yet fast library for decision forests. It summarizes the research in this field and provides a solid platform to extend it.
The library is thoroughly tested and highly flexible. It is available under the permissive 2-clause BSD license.
Feature highlights are:
- Object oriented model of the unified decision forest model of Antonio Criminisi and Jamie Shotton, as well as extensions (e.g., Hough forests).
- Templated C++ classes for maximum memory and calculation efficiency.
- Compatible to the Microsoft Visual C++, the GNU, and the Intel compiler.
- Platform independent serialization: train forests and trees on a Linux cluster and use them on a Windows PC.
- Documented and consistent interfaces in C++, Python and Matlab.
First research results include the development of the newly introduced Induced Entropy and a successful application for uncertainty sampling in the context of self organizing adaptive systems.
Christoph Lassner and Rainer Lienhart. Norm-induced entropies for decision forests.
IEEE Winter Conference on Applications of Computer Vision 2015 (WACV15), Waikoloa Beach, HI, January 6-9, 2015 [ PDF ] [ Code ]