We address the issue of low-level segmentation for vector- valued images, focusing on color images. The proposed approach relies on the formulation of the problem as a generalized Voronoi tessellation of the image domain. In this context, the issue is transferred to the definition of an appropriated pseudo-metric and the selection of a set of sources. Two types of pseudo-metrics are considered ; the first one is based on energy minimizing paths and the se- cond is associated to the families of nested partitions of the image domain. We discuss specific applications of our approach to pre-segmentation, edge detection and hierar- chical segmentation on color images.