Shape recognition based on Kernel-edit distance

In this paper a kernel method for shape recognition is proposed. The approach is based on the edit distance between pairs of shapes after transforming them into symbol strings. The transformation of shapes into symbol strings is invariant to similarity transforms and can handle partial occlusions. Representation of shape contours uses the shape contexts and applies dynamic programming for finding the correspondence between points over shape contours. Corresponding points are then transformed into symbolic representation and the normalized edit distance computes the dissimilarity between pairs of strings in the database. Obtained distances are then transformed into suitable kernels which are classified using support vector machines. Experimental results over a variety of shape databases show that the proposed approach is suitable for shape recognition.