Title of article :
A Metric Approach to Vector-Valued Image Segmentation
Author/Authors :
PABLO A. ARBELA´ EZ AND LAURENT D. COHEN، نويسنده ,
Issue Information :
روزنامه با شماره پیاپی سال 2006
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
Keywords :
Boundary detection , vector-valued image , path variation , Distance transforms , Color , image segmentation , ultrametrics
Journal title :
INTERNATIONAL JOURNAL OF COMPUTER VISION
Journal title :
INTERNATIONAL JOURNAL OF COMPUTER VISION