Topic
Dynamic time warping
About: Dynamic time warping is a research topic. Over the lifetime, 6013 publications have been published within this topic receiving 133130 citations.
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TL;DR: This work presents an enhanced Dynamic Time Warping (DTW) based online signature verification system by utilizing the code-vectors generated from a Vector-Quantization (VQ) strategy, which is the first of its kind, that exploits the characteristics of the warping path for online signatures verification.
80 citations
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23 Sep 2007TL;DR: Novel methods for ranked subsequence matching under time warping are presented, which finds top-k subsequences most similar to a query sequence from data sequences, which is the first and most sophisticated subsequences matching solution mentioned in the literature.
Abstract: Existing work on similar sequence matching has focused on either whole matching or range subsequence matching. In this paper, we present novel methods for ranked subsequence matching under time warping, which finds top-k subsequences most similar to a query sequence from data sequences. To the best of our knowledge, this is the first and most sophisticated subsequence matching solution mentioned in the literature. Specifically, we first provide a new notion of the minimum-distance matching-window pair (MDMWP) and formally define the mdmwp-distance, a lower bound between a data subsequence and a query sequence. The mdmwp-distance can be computed prior to accessing the actual subsequence. Based on the mdmwp-distance, we then develop a ranked subsequence matching algorithm to prune unnecessary subsequence accesses. Next, to reduce random disk I/Os and bad buffer utilization, we develop a method of deferred group subsequence retrieval. We then derive another lower bound, the window-group distance, that can be used to effectively prune unnecessary subsequence accesses during deferred group-subsequence retrieval. Through extensive experiments with many data sets, we showcase the superiority of the proposed methods.
80 citations
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TL;DR: This paper focuses on the long-term intra-speaker variability of feature parameters as on the most crucial problems in speaker recognition, and presents an investigation into methods for reducing the effects of long- term spectral variability on recognition accuracy.
79 citations
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TL;DR: In this paper, an approach to on-line handwritten alphanumeric character recognition based on sequential handwriting signals is presented and the issue of reference (or template) set evolution is also addressed.
78 citations
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01 Dec 2013TL;DR: A novel discriminative learning-based temporal alignment method, called maximum margin temporal warping (MMTW), to align two action sequences and measure their matching score, based on the latent structure SVM formulation.
Abstract: Temporal misalignment and duration variation in video actions largely influence the performance of action recognition, but it is very difficult to specify effective temporal alignment on action sequences. To address this challenge, this paper proposes a novel discriminative learning-based temporal alignment method, called maximum margin temporal warping (MMTW), to align two action sequences and measure their matching score. Based on the latent structure SVM formulation, the proposed MMTW method is able to learn a phantom action template to represent an action class for maximum discrimination against other classes. The recognition of this action class is based on the associated learned alignment of the input action. Extensive experiments on five benchmark datasets have demonstrated that this MMTW model is able to significantly promote the accuracy and robustness of action recognition under temporal misalignment and variations.
78 citations