Hierarchical conditional random field for multi-class image classification

Michael Ying Yang, Wolfgang Förstner, Martin Drauschke

Research output: Chapter in Book/Report/Conference proceedingConference contributionAcademicpeer-review

10 Citations (Scopus)

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.

Original languageEnglish
Title of host publicationVISAPP 2010 - Proceedings of the International Conference on Computer Vision Theory and Applications
Pages464-469
Number of pages6
Publication statusPublished - 10 Sep 2010
Event5th International Conference on Computer Vision Theory and Applications, VISAPP 2010 - Angers, France
Duration: 17 May 201021 May 2010
Conference number: 5

Publication series

NameVISAPP 2010 - Proceedings of the International Conference on Computer Vision Theory and Applications
Volume2

Conference

Conference5th International Conference on Computer Vision Theory and Applications, VISAPP 2010
Abbreviated titleVISAPP 2010
CountryFrance
CityAngers
Period17/05/1021/05/10

Keywords

  • Hierarchical conditional random field
  • Image segmentation
  • Multi-class image classification
  • Region adjacency graph
  • Region hierarchy graph

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  • Cite this

    Yang, M. Y., Förstner, W., & Drauschke, M. (2010). Hierarchical conditional random field for multi-class image classification. In VISAPP 2010 - Proceedings of the International Conference on Computer Vision Theory and Applications (pp. 464-469). (VISAPP 2010 - Proceedings of the International Conference on Computer Vision Theory and Applications; Vol. 2).