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Guided informative image partitioning

Conference Paper


Abstract


  • Image partitioning separates an image into multiple visually

    and semantically homogeneous regions, providing a summary of visual

    content. Knowing that human observers focus on interesting objects

    or regions when interpreting a scene, and envisioning the usefulness of

    this focus in many computer vision tasks, this paper develops a userattention

    adaptive image partitioning approach. Given a set of pairs of

    oversegments labeled by a user as “should be merged” or “should not

    be merged”, the proposed approach produces a fine partitioning in user

    defined interesting areas, to retain interesting information, and a coarser

    partitioning in other regions to provide a parsimonious representation.

    To achieve this, a novel Markov Random Field (MRF) model is used to

    optimally infer the relationship (“merge” or “not merge”) among oversegment

    pairs, by using the graph nodes to describe the relationship

    between pairs. By training an SVM classifier to provide the data term,

    a graph-cut algorithm is employed to infer the best MRF configuration.

    We discuss the difficulty in translating this configuration back to an

    image labelling, and develop a non-trivial post-processing to refine the

    configuration further. Experimental verification on benchmark data sets

    demonstrates the effectiveness of the proposed approach.

Authors


  •   Brewer, Nathan (external author)
  •   Liu, Nianjun (external author)
  •   Wang, Lei

Publication Date


  • 2010

Citation


  • Brewer, N., Liu, N. & Wang, L. (2010). Guided informative image partitioning. Proceedings of Thirteenth International Workshop on Structural and Syntactic Pattern Recognition (S+SSPR) (pp. 202-212). Heldermann Verlag: Springer-Verlag.

Scopus Eid


  • 2-s2.0-77958459096

Ro Metadata Url


  • http://ro.uow.edu.au/eispapers/533

Start Page


  • 202

End Page


  • 212

Abstract


  • Image partitioning separates an image into multiple visually

    and semantically homogeneous regions, providing a summary of visual

    content. Knowing that human observers focus on interesting objects

    or regions when interpreting a scene, and envisioning the usefulness of

    this focus in many computer vision tasks, this paper develops a userattention

    adaptive image partitioning approach. Given a set of pairs of

    oversegments labeled by a user as “should be merged” or “should not

    be merged”, the proposed approach produces a fine partitioning in user

    defined interesting areas, to retain interesting information, and a coarser

    partitioning in other regions to provide a parsimonious representation.

    To achieve this, a novel Markov Random Field (MRF) model is used to

    optimally infer the relationship (“merge” or “not merge”) among oversegment

    pairs, by using the graph nodes to describe the relationship

    between pairs. By training an SVM classifier to provide the data term,

    a graph-cut algorithm is employed to infer the best MRF configuration.

    We discuss the difficulty in translating this configuration back to an

    image labelling, and develop a non-trivial post-processing to refine the

    configuration further. Experimental verification on benchmark data sets

    demonstrates the effectiveness of the proposed approach.

Authors


  •   Brewer, Nathan (external author)
  •   Liu, Nianjun (external author)
  •   Wang, Lei

Publication Date


  • 2010

Citation


  • Brewer, N., Liu, N. & Wang, L. (2010). Guided informative image partitioning. Proceedings of Thirteenth International Workshop on Structural and Syntactic Pattern Recognition (S+SSPR) (pp. 202-212). Heldermann Verlag: Springer-Verlag.

Scopus Eid


  • 2-s2.0-77958459096

Ro Metadata Url


  • http://ro.uow.edu.au/eispapers/533

Start Page


  • 202

End Page


  • 212