A learning-based algorithm for geometric labeling of indoor images
Xiaoqing Liu, Jagath K. Samarabandu · 2006
Abstract: This paper aims to use a large set of feature descriptions as geometric cues to build the structural knowledge of an indoor image. In this paper, a large quantity of training images are used to obtain the required information through learning. We apply a multi-class version of AdaBoost with weak learners based on the decision tree to label regions in an indoor image as “ground”, “wall ” and “ceiling”. Through labeling, we can estimate the coarse geometric properties of an indoor scene, which can be used in a large number of applications, such as mobile robot navigation, object detection, automatic single-view or 3D reconstruction, virtual reality, video games, etc.