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A Metric Approach to Vector-Valued Image Segmentation

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Abstract

We address the issue of low-level segmentation of vector-valued images, focusing on the case of color natural images. The proposed approach relies on the formulation of the problem in the metric framework, as a Voronoi tessellation of the image domain. In this context, a segmentation is determined by a distance transform and a set of sites. Our method consists in dividing the segmentation task in two successive sub-tasks: pre-segmentation and hierarchical representation. We design specific distances for both sub-problems by considering low-level image attributes and, particularly, color and lightness information. Then, the interpretation of the metric formalism in terms of boundaries allows the definition of a soft contour map that has the property of producing a set of closed curves for any threshold. Finally, we evaluate the quality of our results with respect to ground-truth segmentation data.

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Authors and Affiliations

  1. CEREMADE, UMR CNRS 7534 Université Paris Dauphine, Place du maréchal de Lattre de Tassigny, 75775, Paris cedex 16, France

    Pablo A. Arbeláez & Laurent D. Cohen

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  1. Pablo A. Arbeláez
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  2. Laurent D. Cohen
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Correspondence to Pablo A. Arbeláez.

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Arbeláez, P.A., Cohen, L.D. A Metric Approach to Vector-Valued Image Segmentation. Int J Comput Vision 69, 119–126 (2006). https://doi.org/10.1007/s11263-006-6857-5

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  • DOI: https://doi.org/10.1007/s11263-006-6857-5

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