Reference : Extremely Randomized Trees and Random Subwindows for Image Classification, Annotation...
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Engineering, computing & technology : Computer science
http://hdl.handle.net/2268/144561
Extremely Randomized Trees and Random Subwindows for Image Classification, Annotation, and Retrieval
English
Marée, Raphaël mailto [Université de Liège - ULg > > GIGA-Management : Plateforme bioinformatique >]
Wehenkel, Louis mailto [Université de Liège - ULg > Dép. d'électric., électron. et informat. (Inst.Montefiore) > Systèmes et modélisation >]
Geurts, Pierre mailto [Université de Liège - ULg > Dép. d'électric., électron. et informat. (Inst.Montefiore) > Systèmes et modélisation >]
Feb-2013
Decision Forests in Computer Vision and Medical Image Analysis, Advances in Computer Vision and Pattern Recognition
Criminisi, A
Shotton, J
Springer
Advances in Computer Vision and Pattern Recognition
125-142
Yes
978-1-4471-4929-3
[en] extremely randomized trees ; random subwindows ; machine learning
[en] We present a unified framework involving the extraction of
random subwindows within images and the induction of ensembles of
extremely randomized trees. We discuss the specialization of this
framework for solving several general problems in computer vision,
ranging from image classification and segmentation to content-based
image retrieval and interest point detection. The methods are
illustrated on various applications and datasets from the biomedical
domain
Giga-Systems Biology and Chemical Biology
Fonds Européen de Développement Régional - FEDER ; Service public de Wallonie : Direction générale opérationnelle de l'économie, de l'emploi et de la recherche - DG06 ; Fonds de la Recherche Scientifique (Communauté française de Belgique) - F.R.S.-FNRS
Researchers
http://hdl.handle.net/2268/144561
http://www.springer.com/computer/image+processing/book/978-1-4471-4928-6

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