Abstract
Data used to train models for semantic segmentation have the same spatial structure as the image data, are mostly densely labeled, and thus contain contextual information such as class geometry and cooccurrence. We aim to exploit this information for structured prediction. Multiple structured label spaces, representing different aspects of context information, are introduced and integrated into the Random Forest framework. The main advantage are structural subclasses which carry information about the context of a data point. The output of the applied classification forest is a decomposable posterior probability distribution, which allows substituting the prior by information carried by these subclasses. The experimental evaluation shows results superior to standard Random Forests as well as a related method of structured prediction.
Material
Paper (as published in the proceedings of VISAPP2018)
Appendix
Master Thesis on which the publication is based on.
Citation
M. Wöllhaf, R. Hänsch, O. Hellwich, Leveraging the Spatial Label Structure for Semantic Image Labeling using Random Forests
@PROCEEDINGS{ Woellhaf:2018,
author = {Manuel Wöllhaf and Ronny Hänsch and Olaf Hellwich},
title = {Leveraging the Spatial Label Structure for Semantic Image Labeling using Random Forests},
booktitle= {VISAPP 2018},
volume = {tbd},
pages = {tbd},
year = {2017}
}