Fast computation of rotation-invariant distances for image time-series data

초록

Computing the rotation-invariant distance between image time-series is a time-consuming process in boundary image matching since it requires a lot of Euclidean distance computations for all possible rotations. In this paper we propose a novel solution that significantly reduces the number of distance computations using the triangular inequality. We first present the notion of self rotation distance and formally show that the self rotation distance with the triangular inequality produces a tight lower bound and prunes many unnecessary distance computations. Experimental results show that our self rotation distance-based algorithm significantly outperforms the existing algorithm by up to one or two orders of magnitude. © 2012 Springer-Verlag.

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