2D Shape Recognition Using Information Theoretic Kernels

In this paper, a novel approach for contour-based 2D shape recognition is proposed, using a recently intro-duced class of information theoretic kernels. This kind of kernels, based on a non-extensive generalization of the classical Shannon information theory, are defined on probability measures. In the proposed approach, chain code representations are first extracted from the contours; then n-gram statistics are computed and used as input to the information theoretic kernels. We tested different versions of such kernels, using support vector machine and nearest neighbor classifiers. An experi-mental evaluation on the chicken pieces dataset shows that the proposed approach outperforms the current state-of-the-art methods. 1