A Metric Approach to Vector-Valued Image Segmentation |
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Authors: | Pablo A Arbeláez Laurent D Cohen |
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Affiliation: | (1) CEREMADE, UMR CNRS 7534 Université Paris Dauphine, Place du maréchal de Lattre de Tassigny, 75775 Paris cedex 16, France |
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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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Keywords: | image segmentation distance transforms path variation ultrametrics vector-valued image color boundary detection |
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