Description:

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 currently developed at the Multimedia Computing and Computer Vision Lab of the University of Augsburg and 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 and interface independent save/load mechanics: train forests and trees on a Linux cluster using C++ and use them on a Windows PC with MATLAB.

• Documented and consistent interfaces in C++, Python and Matlab.

For more information, see the library homepage.

Changes to previous version:

Initial Announcement on mloss.org.

BibTeX Entry: Download

Corresponding Paper BibTeX Entry: Download

Supported Operating Systems: Linux, Windows, Ubuntu, Mac

Data Formats: Ascii, Agnostic

Tags: Decision Trees, Decision Tree Learning, Decision Forests

Archive: download here

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