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

Abstract

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