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arXiv · 1206.4116

Dependence Maximizing Temporal Alignment via Squared-Loss Mutual Information

Makoto Yamada·Leonid Sigal·Michalis Raptis·Masashi Sugiyama
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Abstract

The goal of temporal alignment is to establish time correspondence between two sequences, which has many applications in a variety of areas such as speech processing, bioinformatics, computer vision, and computer graphics. In this paper, we propose a novel temporal alignment method called least-squares dynamic time warping (LSDTW). LSDTW finds an alignment that maximizes statistical dependency between sequences, measured by a squared-loss variant of mutual information. The benefit of this novel information-theoretic formulation is that LSDTW can align sequences with different lengths, different dimensionality, high non-linearity, and non-Gaussianity in a computationally efficient manner. In addition, model parameters such as an initial alignment matrix can be systematically optimized by cross-validation. We demonstrate the usefulness of LSDTW through experiments on synthetic and real-world Kinect action recognition datasets.

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Makoto Yamada, Leonid Sigal, Michalis Raptis, Masashi Sugiyama. 2012-06-19. Dependence Maximizing Temporal Alignment via Squared-Loss Mutual Information. https://arxiv.org/abs/1206.4116

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