HIERARCHICAL CONDITIONAL RANDOM FIELD FOR MULTI-CLASS IMAGE CLASSIFICATION

Michael Ying Yang, Wolfgang Förstner, Martin Drauschke

Abstract

Multi-class image classification has made significant advances in recent years through the combination of local and global features. This paper proposes a novel approach called hierarchical conditional random field (HCRF) that explicitly models region adjacency graph and region hierarchy graph structure of an image. This allows to set up a joint and hierarchical model of local and global discriminative methods that augments conditional random field to a multi-layer model. Region hierarchy graph is based on a multi-scale watershed segmentation.

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Paper Citation


in Harvard Style

Ying Yang M., Förstner W. and Drauschke M. (2010). HIERARCHICAL CONDITIONAL RANDOM FIELD FOR MULTI-CLASS IMAGE CLASSIFICATION . In Proceedings of the International Conference on Computer Vision Theory and Applications - Volume 2: VISAPP, (VISIGRAPP 2010) ISBN 978-989-674-029-0, pages 464-469. DOI: 10.5220/0002877404640469


in Bibtex Style

@conference{visapp10,
author={Michael Ying Yang and Wolfgang Förstner and Martin Drauschke},
title={HIERARCHICAL CONDITIONAL RANDOM FIELD FOR MULTI-CLASS IMAGE CLASSIFICATION},
booktitle={Proceedings of the International Conference on Computer Vision Theory and Applications - Volume 2: VISAPP, (VISIGRAPP 2010)},
year={2010},
pages={464-469},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0002877404640469},
isbn={978-989-674-029-0},
}


in EndNote Style

TY - CONF
JO - Proceedings of the International Conference on Computer Vision Theory and Applications - Volume 2: VISAPP, (VISIGRAPP 2010)
TI - HIERARCHICAL CONDITIONAL RANDOM FIELD FOR MULTI-CLASS IMAGE CLASSIFICATION
SN - 978-989-674-029-0
AU - Ying Yang M.
AU - Förstner W.
AU - Drauschke M.
PY - 2010
SP - 464
EP - 469
DO - 10.5220/0002877404640469