2011-08-30 14:00:29 UTC
I'm Victor Oliveira, a master student in computer engineering at
University of Campinas. I've been doing my thesis in the Optimum-Path
Forest [OPF] classifier [1,2,3,4] for image processing. As a way to
compare its performance to others classifiers, I implemented it
according scikits.learn guidelines.
As you can see in the references, it is/has:
* naturally multi-class
* fast fitting and predicting
* good accuracy
* few parameters
* allows some superposition between clusters
The classifier comes with a supervised and an unsupervised version also.
I implemented it in C and used Cython to make a proper Python interface.
So, are you interested? There is some documentation and formatting
missing, but I'd be glad to do it if you say so!
LibOPF repository: https://github.com/victormatheus/LibOPF
Handwritten digits classification example:
More about me and our lab:
 João P. Papa, Alexandre X. Falcão and Celso T. N. Suzuki.
Supervised Pattern Classification based on Optimum-Path Forest. Intl.
Journal of Imaging Systems and Technology, Wiley, Vol. 19, Issue 2,
pp. 120–131, Jun 2009.
 L.M. Rocha, F.A.M. Cappabianco, and A.X. Falcão. Data clustering
as an optimum-path forest problem with applications in image analysis.
International Journal of Imaging Systems and Technology, 19(2):50-68,
 A. X. Falcao, J. Stolfi, and R. A. Lotufo. The image foresting
transform: Theory, algorithms, and applications. IEEE Trans. on Patt.
Anal. Mach. Intell., 26(1):19-29, 2004.
 A.T. da Silva, A.X. Falcão, L.P. Magalhães: A new CBIR approach
based on relevance feedback and optimum-path forest classification.
Journal of WSCG, Vol.18, No.1-3, pp. 73-80.