Shape Similarity based on Combinatorial Maps and a Tree Pattern Kernel

Abstract : While the skeleton of a 2D shape corresponds to a planar graph, its encoding by usual graph data struc- tures does not allow to capture its planar properties. Graph kernels may be defined on graph's encoding of the skeleton in order to define a similarity measure be- tween shapes. Such graph kernels are usually based on a decomposition of graphs into bags of walks or trails. These linear patterns do not allow to fully encode the structure of a skeleton on branching points, hence los- ing important informations about the shape. This paper aims to solve these two drawbacks by using an encoding of the skeleton taking explicitly into account the orien- tation of the plane and by decomposing the resulting graph model into both linear and nonlinear patterns.
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Communication dans un congrès
21st International Conference on Pattern Recognition, Nov 2012, Tsukuba, Japan. IEEE Computer Society, pp.000-0000, 2012
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Dernière modification le : jeudi 7 février 2019 - 16:32:59
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bdbgm12treelet-kernel.pdf
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  • HAL Id : hal-00768662, version 1

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Luc Brun, Sébastien Bougleux, François-Xavier Dupé, Benoit Gaüzère, Myriam Mokhtari. Shape Similarity based on Combinatorial Maps and a Tree Pattern Kernel. 21st International Conference on Pattern Recognition, Nov 2012, Tsukuba, Japan. IEEE Computer Society, pp.000-0000, 2012. 〈hal-00768662〉

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