Symmetry-Driven Shape Matching
نویسندگان
چکیده
We propose a novel Bayesian framework of symmetry-driven shape similarity to incorporate structural shape descriptor into conventional geometrical shape descriptor. We use rotation and reflection symmetries for structural shape description. Symmetry detection on each shape image provides a qualitative and a quantitative categorization of the types and the degrees of symmetry level. The posterior shape similarity enhances the shape matching performance based on the symmetry structural discrimination of shapes. Comprehensive experimental results show statistically significant improvement on retrieval accuracy over the best state of the art methods on MPEG-7 data set.
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