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.

Order-Preserving sparse coding for sequence classification

Abstract: In this paper, we investigate order-preserving sparse coding for classifying multi-dimensional sequence data. Such a problem is often tackled by first decomposing the input sequence into individual frames and extracting features, then performing sparse coding or other processing for each frame based feature vector independently, and finally aggregating individual responses to classify the input sequence. However, this heuristic approach ignores the underlying temporal order of the input sequence frames, which in turn results in suboptimal discriminative capability. In this work, we introduce a temporal-order-preserving regularizer which aims to preserve the temporal order of the reconstruction coefficients. An efficient Nesterov-type smooth approximation method is developed for optimization of the new regularization criterion, with guaranteed error bounds. Extensive experiments for time series classification on a synthetic dataset, several machine learning benchmarks, and a challenging real-world RGB-D human activity dataset, show that the proposed coding scheme is discriminative and robust, and it outperforms previous art for sequence classification.

Kernels for Periodic Time Series Arising in Astronomy

Abstract: We present a method for applying machine learning algorithms to the automatic classification of astronomy star surveys using time series of star brightness. Currently such classification requires a large amount of domain expert time. We show that a combination of phase invariant similarity and explicit features extracted from the time series provide domain expert level classification. To facilitate this application, we investigate the cross-correlation as a general phase invariant similarity function for time series. We establish several theoretical properties of cross-correlation showing that it is intuitively appealing and algorithmically tractable, but not positive semidefinite, and therefore not generally applicable with kernel methods. As a solution we introduce a positive semidefinite similarity function with the same intuitive appeal as cross-correlation. An experimental evaluation in the astronomy domain as well as several other data sets demonstrates the performance of the kernel and related similarity functions.

Synchronization for dynamic blending of motions

Abstract: In this paper we present a new real-time synchronization algorithm. In dynamic environments, motions need to be continuously adapted to obtain realistic animations. We propose an advanced time warping algorithm to synchronize such motions. This algorithm uses the sequence of support phases of the motions. It also takes into account the priority associated to each motion. It is based on an algebraic relation to detect incompatible motions and to select elements of the sequence to be enlarged. The resulting time warping function can be non-derivable so it is corrected by using a cardinal spline interpolation. In this paper, we demonstrate that our algorithm always finds at least one solution. This synchronization module is part of a complete animation engine called MKM already used in production.

Vision based Hand Gesture Recognition using Dynamic Time Warping for Indian Sign Language

Abstract: This paper presents an algorithm of Hand Gesture Recognition by using Dynamic Time Warping methodology. The system consists of three modules: real time detection of face region and two hand regions, tracking the hands trajectory both in terms of direction among consecutive frames as well as distance from the centre of the frame and gesture recognition based on analyzing variations in the hand locations along with the centre of the face. The proposed technique of ours overcomes not only the limitations of a glove based approach but also most of the vision based approach concerning different illumination conditions, background complexity and distance from camera which is up to 2 meters. Also by using Dynamic Time Warping Algorithm which finds the optimal alignment between the stored database features and query features, improvement in recognition accuracy is observed compared to conventional methods. Experimental results show that the accuracy is 90% in recognizing 24 gestures based on Indian Sign Language.

On the time series support vector machine using dynamic time warping kernel for brain activity classification

Abstract: A new data mining technique used to classify normal and pre-seizure electroencephalograms is proposed. The technique is based on a dynamic time warping kernel combined with support vector machines (SVMs). The experimental results show that the technique is superior to the standard SVM and improves the brain activity classification.