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2010
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2D Shape Recognition Using Information Theoretic Kernels

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2D Shape Recognition Using Information Theoretic Kernels
In this paper, a novel approach for contour-based 2D shape recognition is proposed, using a recently introduced 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 experimental evaluation on the chicken pieces dataset shows that the proposed approach outperforms the current state-of-the-art methods.
Manuele Bicego, André Filipe Torres Martins, Vitt
Added 03 Jul 2010
Updated 03 Jul 2010
Type Conference
Year 2010
Where ICPR
Authors Manuele Bicego, André Filipe Torres Martins, Vittorio Murino, Pedro M. Q. Aguiar, Mario A. T. Figueiredo
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