Analysis of the Performance of AdaBoost.M2 for the Simulated Digit-Recognition-Example
EMCL '01 Proceedings of the 12th European Conference on Machine Learning
Efficient Graph-Based Image Segmentation
International Journal of Computer Vision
Building a Classification Cascade for Visual Identification from One Example
ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision (ICCV'05) Volume 1 - Volume 01
Geometric Context from a Single Image
ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision (ICCV'05) Volume 1 - Volume 01
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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.