loading
Papers Papers/2022 Papers Papers/2022

Research.Publish.Connect.

Paper

Paper Unlock

Authors: Manuel Wöllhaf ; Ronny Hänsch and Olaf Hellwich

Affiliation: Technische Universität Berlin, Germany

Keyword(s): Random Forests, Semantic Segmentation, Structured Prediction, Context Information.

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.

CC BY-NC-ND 4.0

Sign In Guest: Register as new SciTePress user now for free.

Sign In SciTePress user: please login.

PDF ImageMy Papers

You are not signed in, therefore limits apply to your IP address 3.21.100.34

In the current month:
Recent papers: 100 available of 100 total
2+ years older papers: 200 available of 200 total

Paper citation in several formats:
Wöllhaf, M.; Hänsch, R. and Hellwich, O. (2018). Leveraging the Spatial Label Structure for Semantic Image Labeling using Random Forests. In Proceedings of the 13th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2018) - Volume 5: VISAPP; ISBN 978-989-758-290-5; ISSN 2184-4321, SciTePress, pages 193-200. DOI: 10.5220/0006546801930200

@conference{visapp18,
author={Manuel Wöllhaf. and Ronny Hänsch. and Olaf Hellwich.},
title={Leveraging the Spatial Label Structure for Semantic Image Labeling using Random Forests},
booktitle={Proceedings of the 13th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2018) - Volume 5: VISAPP},
year={2018},
pages={193-200},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0006546801930200},
isbn={978-989-758-290-5},
issn={2184-4321},
}

TY - CONF

JO - Proceedings of the 13th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2018) - Volume 5: VISAPP
TI - Leveraging the Spatial Label Structure for Semantic Image Labeling using Random Forests
SN - 978-989-758-290-5
IS - 2184-4321
AU - Wöllhaf, M.
AU - Hänsch, R.
AU - Hellwich, O.
PY - 2018
SP - 193
EP - 200
DO - 10.5220/0006546801930200
PB - SciTePress

- Science and Technology Publications, Lda.
RESOURCES

Proceedings

Papers

Authors

Ontology

CONTACTS

Science and Technology Publications, Lda
Avenida de S. Francisco Xavier, Lote 7 Cv. C,
2900-616 Setúbal, Portugal.

Phone: +351 265 520 185 (National fixed network call)
Fax: +351 265 520 186
Email: info@scitepress.org

EXTERNAL LINKS

PRIMORIS

INSTICC

SCITEVENTS

CROSSREF

PROCEEDINGS SUBMITTED FOR INDEXATION BY:

dblp

Ei Compendex

SCOPUS

Semantic Scholar

Google Scholar

Microsoft Academic