Michael Ying Yang, Wolfgang Förstner, Martin Drauschke


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

author={Michael Ying Yang and Wolfgang Förstner and Martin Drauschke},
booktitle={Proceedings of the International Conference on Computer Vision Theory and Applications - Volume 2: VISAPP, (VISIGRAPP 2010)},

in EndNote Style

JO - Proceedings of the International Conference on Computer Vision Theory and Applications - Volume 2: VISAPP, (VISIGRAPP 2010)
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