A similarity-based approach for shape classification using Aslan skeletons

Shape skeletons are commonly used in generic shape recognition as they capture part hierarchy, providing a structural representation of shapes. However, their potential for shape classification has not been investigated much. In this study, we present a similarity based approach for classifying 2D shapes based on their Aslan skeletons [1, 2]. The coarse structure of this skeleton representation allows us to represent each shape category in the form of a reduced set of prototypical trees, offering an alternative solution to the problem of selecting the best representative examples. The ensemble of these category prototypes is then used to form a similarity based representation space in which the similarities between a given shape and the prototypes are computed using a tree edit distance algorithm, and Support Vector Machine (SVM) classifiers are used to predict the category membership of the shape based on computed similarities.