ISSN : 1796-203X
Volume : 3    Issue : 5    Date : May 2008

Shape Recognition by Clustering and Matching of Skeletons
Hamidreza Zaboli, Mohammad Rahmati, and Abdolreza Mirzaei
Page(s): 24-33
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We perform the task of shape recognition using a skeleton based method. Skeleton of the shape is
considered as a free tree and is represented by a connectivity graph. Geometric features of the
shape are captured using Radius function along the skeletal curve segments. Matching of the
connectivity graphs based on their topologies and geometric features gives a distance measure for
determining similarity or dissimilarity of the shapes. Then the distance measure is used for
clustering and classification of the shapes by employing hierarchical clustering methods. Moreover,
for each class, a median skeleton is computed and is located as the indicator of its related class.
The resulted hierarchy of the shapes classes and their indicators are used for the task of shape
recognition. This is performed for any given shape by a top-down traversing of the resulted hierarchy
and matching with the indicators. We evaluate the proposed method by different shapes of
silhouette datasets and we show how the method efficiently recognizes and classifies shapes.

Index Terms
object recognition, shape recognition, shape classification, skeleton, radius function, clustering.