  An approach that extracts global attributes from outdoor images to facilitate
geometric layout labeling is investigated in this work. The proposed
Global-attributes Assisted Labeling (GAL) system exploits both local features
and global attributes. First, by following a classical method, we use local
features to provide initial labels for all super-pixels. Then, we develop a set
of techniques to extract global attributes from 2D outdoor images. They include
sky lines, ground lines, vanishing lines, etc. Finally, we propose the GAL
system that integrates global attributes in the conditional random field (CRF)
framework to improve initial labels so as to offer a more robust labeling
result. The performance of the proposed GAL system is demonstrated and
benchmarked with several state-of-the-art algorithms against a popular outdoor
scene layout dataset.
