Wehenkel, Louis[Université de Liège - ULg > Dép. d'électric., électron. et informat. (Inst.Montefiore) > Systèmes et modélisation >]
Geurts, Pierre[Université de Liège - ULg > Dép. d'électric., électron. et informat. (Inst.Montefiore) > Systèmes et modélisation >]
Feb-2009
Proc. International Conference on Computer Vision Theory and Applications (VISAPP)
2
196-203
International
International Conference on Computer Vision Theory and Applications (VISAPP)
Février 2009
INSTICC
Lisbon
Portugal
[en] This paper addresses image annotation, i.e. labelling pixels of an image with a class among a finite set of predefined classes. We propose a new method which extracts a sample of subwindows from a set of annotated images in order to train a subwindow annotation model by using the extremely randomized trees ensemble method appropriately extended to handle high-dimensional output spaces. The annotation of a pixel of an unseen image is done by aggregating the annotations of its subwindows containing this pixel. The proposed method is compared to a more basic approach predicting the class of a pixel from a single window centered on that pixel and to other state-of-the-art image annotation methods. In terms of accuracy, the proposed method significantly outperforms the basic method and shows good performances with respect to the state-of-the-art, while being more generic, conceptually simpler, and of higher computational efficiency than these latter.
Fonds de la Recherche Scientifique (Communauté française de Belgique) - F.R.S.-FNRS ; Fonds Européen de Développement Régional - FEDER ; Politique Scientifique Fédérale (Belgique) = Belgian Federal Science Policy
[Université de Liège - ULg > > GIGA-Management : Plateforme bioinformatique >]
