Abstract

The region’s internal properties (color, texture, ...) help to identify them and their external relations (adjacency, inclusion, ...) are used to build groups of regions having a particular consistent meaning in a more abstract context. Low-level cue image segmentation in a bottom-up way, cannot and should not produce a complete final “good” segmentation. We present a hierarchical partitioning of images using a pairwise similarity function on a graph-based representation of an image. The aim of this paper is to build a minimum weight spanning tree (MST) of an image in order to find region borders quickly in a bottom-up ’stimulus-driven’ way based on local differences in a specific feature.

Keywords

Image Segmentation Maximal Clique Noisy Pixel Image Pyramid Shift Invariance 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2004

Authors and Affiliations

  • Yll Haxhimusa
    • 1
  • Walter Kropatsch
    • 1
  1. 1.Pattern Recognition and Image Processing Group 183/2, Institute for Computer Aided AutomationVienna University of TechnologyAustria

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