Extremely Randomized Trees and Random Subwindows for Image Classification, Annotation, and Retrieval

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