“Silhouette Image Recognition with Weighted Minimum Common Supergraph”

It is desired to recognize objects in images correctly. Recognition using structural feature has been studied. A single structure extracted by the medial axis transform from a digital silhouette image is not always an essential feature as a prototype that represents silhouettes in a category, because of noise and distortion. In this paper, we propose a method for recognizing silhouette images by obtaining an essential structure from the images of a category. The essential structure is defined as a weighted minimum common supergraph of graphs which are extracted from silhouette images. To show the validly of the proposed method, experiments are carried out for categorizing silhouette images.