[en] In this paper, we compare five tree-based machine learning methods within a recent generic image classification framework based on random extraction and classification of subwindows. We evaluate them on three publicly available object recognition datasets (COIL-100, ETH-80, and ZuBuD). Our comparison shows that this general and conceptually simple framework yields good results when combined with ensemble of decision trees, especially when using Tree Boosting or Extra-Trees. The latter is also particularly attractive in terms of computational efficiency.
Disciplines :
Computer science
Author, co-author :
Marée, Raphaël ; Université de Liège - ULiège > Department of Electrical Engineering and Computer Science > Systèmes et Modélisation
Geurts, Pierre ; Université de Liège - ULiège > Dép. d'électric., électron. et informat. (Inst.Montefiore) > Systèmes et modélisation
Piater, Justus ; Université de Liège - ULiège > Dép. d'électric., électron. et informat. (Inst.Montefiore) > INTELSIG Group
Wehenkel, Louis ; Université de Liège - ULiège > Dép. d'électric., électron. et informat. (Inst.Montefiore) > Systèmes et modélisation
Language :
English
Title :
Decision Trees and Random Subwindows for Object Recognition
Publication date :
2005
Event name :
ICML workshop on Machine Learning Techniques for Processing Multimedia Content (MLMM2005)
Event place :
Bonn, Germany
Audience :
International
Main work title :
ICML workshop on Machine Learning Techniques for Processing Multimedia Content (MLMM2005)
