08.13
A new and relatively faster implementation of this algorithm exists in my “Online Multi-Class LPBoost” package.
This package implements the “Online Random Forests” (ORF) algorithm of Saffari et al., ICCV-OLCV 2009 [1]. This algorithm extends the offline Random Forests (RF) to learn from online training data samples. ORF is a multi-class classifier which is able to learn the classifier without 1-vs-all or 1-vs-1 binary decompositions.
ORF package is implemented in C++ and uses ATLAS/LAPACK subroutines for high performance computations. Currently, it is only tested under Linux (Debian and Ubuntu), but overall it should be possible to run the package on other operating systems with minimal modifications. For installation instructions refer to the “INSTALL” file in the package. Also the usage instructions are available in “README” file.
Download: OnlineForest-0.11.tar.gz (Release date: 03/10/2009)
Author: Amir Saffari
[1] Amir Saffari, Christian Leistner, Jakob Santner, Martin Godec, and Horst Bischof, “On-line Random Forests,” in 3rd IEEE ICCV Workshop on On-line Computer Vision, 2009.
Powered by FireStats
[...] Online Random Forests [...]
Dear Mr. Amir
I’m a student of Nguyen Dang Binh
I’m researching about Online Random Forest
Can you show me how to use OnlineForest-0.11.tar.gz packet in Visual Studio 2005
Hi,
I don’t program in Windows, so I don’t know how to compile the ORF in VS2005. There are dependencies which have to be compiled before hand (like ATLAS and libconfig). So I would suggest you start building those libraries before attempting in compiling the ORF package.
However, on many Linux distributions those libraries are either installed or can be easily installed from their repositories. So if you just want to make a few experiments and see how ORF works, I would suggest install a Linux distribution (Ubuntu or Debian for example) and try to use ORF there.
thank you very much
[...] ***Looking for an Online version of RF? You can get an implementation of online random forest at Amir Saffari’s website: http://www.ymer.org/amir/software/online-random-forests/ [...]
Hi Amir,
I have some problems when I‘m researching the code of the ORF algorithm.
Could you note the command line in the ORF algorithm more clearly?
Regards,
Hi Minh,
Could you please be more specific with your question? Do you have problems understanding how to call the ORF binary from command line? Please take a look at the README file, there is plenty of explanation how to use it. If that does not solve your problem, please write exactly what happens and what is not clear.
Cheers, Amir
Hi Amir,
Could you explain to me the meaning of the attributes in the RandomTest class ?
const int *m_numClasses;
double m_threshold;
double m_trueCount;
double m_falseCount;
vector m_trueStats;
vector m_falseStats;
m_numClasses -> number of classes
m_threshold -> threshold for a test in each node
m_trueCount -> number of samples in the true (right) branch of a node
m_falseCount -> number of samples in the false (left) branch of a node
m_trueStats -> density of classes in the true (right) branch of a node
m_falseStats -> density of classes in the false (left) branch of a node
thanks
Sir,I’m a student new to online random forests. Can i ask you a question, which data do you use for tracking? Lately i have studied camshift algorithm. And in opencv, they just use “hue”, for tracking. So which data do you use, hue? to histogram? or else
Hi Taixi, We usually use Haar features for tracking applications.