This is the author post-print (ie. final draft post-refereeing) accepted version of the paper. Publisher (Elsevier) version will be available in Pattern Recognition Letters. http://www.journals.elsevier.com/pattern-recognition-letters/
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[en] This paper considers the general problem of image classification
without using any prior knowledge about image classes. We study
variants of a method based on supervised learning whose common steps
are the extraction of random subwindows described by raw pixel intensity values
and the use of ensemble of extremely randomized trees to directly
classify images or to learn image features. The influence of method
parameters and variants is thoroughly evaluated so as to provide baselines and
guidelines for future studies. Detailed results are provided on 80
publicly available datasets that depict very diverse types of images
(more than 3800 image classes and over 1.5 million images).
Geurts, Pierre ; Université de Liège > Dép. d'électric., électron. et informat. (Inst.Montefiore) > Algorith. des syst. en interaction avec le monde physique
Wehenkel, Louis ; Université de Liège > Dép. d'électric., électron. et informat. (Inst.Montefiore) > Systèmes et modélisation
Language :
English
Title :
Towards Generic Image Classification using Tree-based Learning: an Extensive Empirical Study
Publication date :
2016
Journal title :
Pattern Recognition Letters
ISSN :
0167-8655
eISSN :
1872-7344
Publisher :
Elsevier, Netherlands
Volume :
74
Issue :
15
Pages :
17-23
Peer reviewed :
Peer Reviewed verified by ORBi
Tags :
CÉCI : Consortium des Équipements de Calcul Intensif
Funders :
Service public de Wallonie : Direction générale opérationnelle de l'économie, de l'emploi et de la recherche - DG06
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