Reference : A generic approach for image classification based on decision tree ensembles and local s...
Scientific congresses and symposiums : Paper published in a book
Engineering, computing & technology : Computer science
http://hdl.handle.net/2268/12601
A generic approach for image classification based on decision tree ensembles and local sub-windows
English
Marée, Raphaël mailto [Université de Liège - ULg > Department of Electrical Engineering and Computer Science > Systèmes et Modélisation > >]
Geurts, Pierre mailto [Université de Liège - ULg > Dép. d'électric., électron. et informat. (Inst.Montefiore) > Systèmes et modélisation >]
Piater, Justus mailto [Université de Liège - ULg > Dép. d'électric., électron. et informat. (Inst.Montefiore) > INTELSIG Group >]
Wehenkel, Louis mailto [Université de Liège - ULg > Dép. d'électric., électron. et informat. (Inst.Montefiore) > Systèmes et modélisation >]
Hong, K.-S. [> > > >]
Zhang, Z. [> > > >]
2004
Proceedings of the 6th Asian Conference on Computer Vision
Asian Federation of Computer Vision Societies (AFCV)
2
860-865
Yes
No
International
6th Asian Conference on Computer Vision
Jeju
South Korea
[en] machine learning
[en] A novel and generic approach for image classification is presented.
The method operates directly on pixel values and does not require
feature extraction. It combines a simple local sub-window extraction
technique with induction of ensembles of extremely randomized
decision trees. We report results on four well known and publicly
available datasets corresponding to representative applications
of image classification problems: handwritten digits (MNIST),
faces (ORL), 3D objects (COIL-100), and textures (OUTEX). A
comparison with studies from the computer vision literature shows
that our method is competitive with the state of the art, an interesting
result considering its generality and conceptual simplicity.
Further experiments are carried out on the COIL-100 dataset to
evaluate the robustness of the learned models to rotation, scaling,
or occlusion of test images. These preliminary results are very
encouraging
Fonds de la Recherche Scientifique (Communauté française de Belgique) - F.R.S.-FNRS ; Région wallonne : Direction générale des Technologies, de la Recherche et de l'Energie - DGTRE
http://hdl.handle.net/2268/12601
http://www.montefiore.ulg.ac.be/services/stochastic/pubs/2004/MGPW04

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