A Graph Representation for Silhouette Based on Multiscale Analysis

Graph descriptor is usually focused on in computer vision for its flexibility and richness. However, in object recognition, it is difficult to catch the feature of an object completely with a straightforward way by a graph. In this paper, from a multiscale viewpoint, we propose a method to construct a vertex-labeled graph for image recognition where the label represents the importance of a vertex to the graph. By using the Fourier Descriptor, when we adjust the cut-off frequency we can select how much detailed feature can be used to represent a contour. In this process, we found the medial axis of shape also evolves from a simple graph to a detailed graph. Focusing on this evolution, for each vertex in the graph we assign a label representing its importance in this graph.