The following is the implementation of the Online Multi-Class LPBoost [1], Online Multi-Class Gradient Boost [1], and Online Random Forest algorithms [2]. Online boosting is one of the most successful online learning algorithms in computer vision. While many challenging online learning problems are inherently multi-class, online boosting and its variants are only able to solve binary tasks. In this work, we present Online Multi-Class LPBoost (OMCLP) which is directly applicable to multi-class problems. From a theoretical point of view, our algorithm tries to maximize the multi-class soft-margin of the samples. In order to solve the LP problem in online settings, we perform an efficient variant of online convex programming, which is based on primal-dual gradient descent-ascent update strategies.

For machine learning and Caltech101 categorization experiments, you can directly use the following code. The tracking code is not included in this package. However, you can see in the following videos the performance of our tracker, and how tracking with virtual classes works.

Algorithms implemented:

  • Online Multi-Class LPBoost
  • Online Gradient Boost (with exponential, logit, and Savage loss functions)
  • Online Random Forests
  • Weighted Linear LaRank SVM (modifications to the original code by Antoine Bordes)

Tracking Results: our tracker is shown in red color.


Tracking with virtual classes.


Download: OMCLPBoost-0.11.tar.gz (Release date: 06/5/2010)


Author: Amir Saffari

Please read the INSTALL file for build instructions. The license is GPL V3.

Change Log:

2010-05-06: Release 0.11:

  • Bug fix in RandomTest class which was previously choosing only integer thresholds (thanks to Andreas Geiger for pointing out the problem).

2010-04-18: Release 0.1

  • First release.

[1] Amir Saffari, Martin Godec, Thomas Pock Christian Leistner, and Horst Bischof, “

Online Multi-Class LPBoost“, in IEEE Conference on Computer Vision and Patter Recognition (CVPR), 2010.

[2] Amir Saffari, Christian Leistner, Jakob Santner, Martin Godec, and Horst Bischof, “Online Random Forests“, in 3rd IEEE ICCV Workshop on On-line Computer Vision, 2009.