Symmetric axis based representations have been widely employed to enhance visualization and to enable quantitative analysis, classification, and registration of medical images. Although the basic idea of shape representation via local symmetries is very old, recently, various new techniques for extracting local symmetries are proposed. Despite seemingly different tools, the main - if not only - difference among these new methods is how the computation is carried out. Recently, by Tari and Shah, a new method for computing symmetries are proposed, and the comparison of the method to the related works is provided. The method constructs a nested symmetry set of an increasing degree of symmetry and decreasing dimension. This is achieved by examining the local geometry of a new distance function. Because the method doesn't suppress any of the symmetry based representations. In this paper, a computational implementation for assigning perceptual meaning and significance to the points in the symmetry set is provided. The coloring scheme allows recovery of the features of interest such as the shape skeletons from the complicated symmetry representation. The method is applicable to arbitrary data including color and multi-modality imags. On the computational side, for a 256 X 256 binary image, two minutes on a low-end Pentium machine is sufficient to compute both the distance function and the colored nested symmetries at four scales.