Riemannian Optimization for Elastic Shape Analysis
نویسندگان
چکیده
In elastic shape analysis, a representation of a shape is invariant to translation, scaling, rotation and reparameterization and important problems (such as computing the distance and geodesic between two curves, the mean of a set of curves, and other statistical analyses) require finding a best rotation and re-parameterization between two curves. In this paper, we focus on this key subproblem and study different tools for optimizations on the joint group of rotations and re-parameterizations. In this conference paper, we give a first account of a novel Riemannian optimization approach and evaluate its use in computing the distance between two curves and classification using two public data sets. Experiments show significant advantages in computational time and reliability in performance compared to the current state-of-the-art method. Further information will become available in a forthcoming full version of this conference paper.
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