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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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Proceedings ArticleDOI
01 Sep 1999
TL;DR: A new method for comparing planar curves and for performing matching at sub-sampling resolution is presented and the performance of the well-known Dynamic Time Warping algorithm is compared.
Abstract: The problem of establishing correspondence and measuring the similarity of a pair of planar curves arises in many applications in computer vision and pattern recognition. This paper presents a new method for comparing planar curves and for performing matching at sub-sampling resolution. The analysis of the algorithm as well as its structural properties are described. The performance of the new technique applied to the problem of signature verification is shown and compared with the performance of the well-known Dynamic Time Warping algorithm.

179 citations

Journal ArticleDOI
01 Jan 2006
TL;DR: The experimental results demonstrate that the index can help speed up the computation of expensive similarity measures such as the LCSS and the DTW and can also be tailored to provide much faster response time at the expense of slightly reduced precision/recall.
Abstract: While most time series data mining research has concentrated on providing solutions for a single distance function, in this work we motivate the need for an index structure that can support multiple distance measures. Our specific area of interest is the efficient retrieval and analysis of similar trajectories. Trajectory datasets are very common in environmental applications, mobility experiments, and video surveillance and are especially important for the discovery of certain biological patterns. Our primary similarity measure is based on the longest common subsequence (LCSS) model that offers enhanced robustness, particularly for noisy data, which are encountered very often in real-world applications. However, our index is able to accommodate other distance measures as well, including the ubiquitous Euclidean distance and the increasingly popular dynamic time warping (DTW). While other researchers have advocated one or other of these similarity measures, a major contribution of our work is the ability to support all these measures without the need to restructure the index. Our framework guarantees no false dismissals and can also be tailored to provide much faster response time at the expense of slightly reduced precision/recall. The experimental results demonstrate that our index can help speed up the computation of expensive similarity measures such as the LCSS and the DTW.

178 citations

Journal ArticleDOI
TL;DR: It is found that combining likelihoods of multiple models in a second classification stage degrades performance of the proposed classifiers, while improving performance with HMM and SD TW, and combining DFFM mappings of multiple SDTW models with SDTW likelihoods can provide significant improvement over SDTW.
Abstract: To recognize speech, handwriting, or sign language, many hybrid approaches have been proposed that combine dynamic time warping (DTW) or hidden Markov models (HMMs) with discriminative classifiers. However, all methods rely directly on the likelihood models of DTW/HMM. We hypothesize that time warping and classification should be separated because of conflicting likelihood modeling demands. To overcome these restrictions, we propose using statistical DTW (SDTW) only for time warping, while classifying the warped features with a different method. Two novel statistical classifiers are proposed - combined discriminative feature detectors (CDFDs) and quadratic classification on DF Fisher mapping (Q-DFFM) - both using a selection of discriminative features (DFs), and are shown to outperform HMM and SDTW. However, we have found that combining likelihoods of multiple models in a second classification stage degrades performance of the proposed classifiers, while improving performance with HMM and SDTW. A proof-of-concept experiment, combining DFFM mappings of multiple SDTW models with SDTW likelihoods, shows that, also for model-combining, hybrid classification can provide significant improvement over SDTW. Although recognition is mainly based on 3D hand motion features, these results can be expected to generalize to recognition with more detailed measurements such as hand/body pose and facial expression.

178 citations

Journal ArticleDOI
TL;DR: This paper proposes similarity measures that attempt to capture the “spirit” of dynamic time warping while being defined over continuous domains, and presents efficient algorithms for computing them.
Abstract: The problem of curve matching appears in many application domains, like time series analysis, shape matching, speech recognition, and signature verification, among others. Curve matching has been studied extensively by computational geometers, and many measures of similarity have been examined, among them being the Frechet distance (sometimes referred in folklore as the "dog-man" distance). A measure that is very closely related to the Frechet distance but has never been studied in a geometric context is the Dynamic Time Warping measure (DTW), first used in the context of speech recognition. This measure is ubiquitous across different domains, a surprising fact because notions of similarity usually vary significantly depending on the application. However, this measure suffers from some drawbacks, most importantly the fact that it is defined between sequences of points rather than curves. Thus, the way in which a curve is sampled to yield such a sequence can dramatically affect the quality of the result. Some attempts have been made to generalize the DTW to continuous domains, but the resulting algorithms have exponential complexity. In this paper we propose similarity measures that attempt to capture the "spirit" of dynamic time warping while being defined over continuous domains, and present efficient algorithms for computing them. Our formulation leads to a very interesting connection with finding short paths in a combinatorial manifold defined on the input chains, and in a deeper sense relates to the way light travels in a medium of variable refractivity.

178 citations

Proceedings ArticleDOI
01 Dec 2011
TL;DR: This paper investigates the use of randomized algorithms that operate directly on the raw acoustic features to produce sparse approximate similarity matrices in O( n) space and O(n log n) time and demonstrates these techniques facilitate spoken term discovery performance capable of outperforming a model-based strategy in the zero resource setting.
Abstract: Spoken term discovery is the task of automatically identifying words and phrases in speech data by searching for long repeated acoustic patterns. Initial solutions relied on exhaustive dynamic time warping-based searches across the entire similarity matrix, a method whose scalability is ultimately limited by the O(n2) nature of the search space. Recent strategies have attempted to improve search efficiency by using either unsupervised or mismatched-language acoustic models to reduce the complexity of the feature representation. Taking a completely different approach, this paper investigates the use of randomized algorithms that operate directly on the raw acoustic features to produce sparse approximate similarity matrices in O(n) space and O(n log n) time. We demonstrate these techniques facilitate spoken term discovery performance capable of outperforming a model-based strategy in the zero resource setting.

174 citations


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Performance
Metrics
No. of papers in the topic in previous years
YearPapers
2023236
2022471
2021341
2020416
2019420
2018377