Grouping and segmentation in a hierarchy of graphs

Walter G. Kropatsch; Yll Haxhimusa

doi:10.1117/12.543421

21 May 2004 Grouping and segmentation in a hierarchy of graphs

Walter G. Kropatsch, Yll Haxhimusa

Proceedings Volume 5299, Computational Imaging II; (2004) https://doi.org/10.1117/12.543421
Event: Electronic Imaging 2004, 2004, San Jose, California, United States

Abstract

We review multilevel hierarchies under the special aspect of their potential for segmentation and grouping. The one-to-one correspondence between salient image features and salient model features are a limiting assumption that makes prototypical or generic object recognition impossible. The region's internal properties (color, texture, shape, ...) help to identify them and their external relations (adjacency, inclusion, similarity of properties) 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. This function measures the difference along the boundary of two components relative to a measure of differences of the components' internal differences. Two components are merged if there is a low-cost connection between them. We use this idea to find region borders quickly and effortlessly in a bottom-up way, based on local differences in a specific feature. The aim of this paper is to build a minimum weight spanning tree (MST) in order to find region borders quickly in a bottom-up 'stimulus-driven' way based on local differences in a specific feature.

Citation Download Citation

Walter G. Kropatsch and Yll Haxhimusa "Grouping and segmentation in a hierarchy of graphs", Proc. SPIE 5299, Computational Imaging II, (21 May 2004); https://doi.org/10.1117/12.543421

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