An Efficient Hash-Based Algorithm for Sequence Data Searching

Chu, Kam-Wing

doi:10.1093/comjnl/41.6.402

“…For example, in music retrieval has been reported [9]: "To achieve tempo invariance, the targets are stretched by 19 different scaling factors from 0.5 to 2.0." Similar remarks can be found in the literature of gait analysis [14], handwritten archive indexing [28], bioinformatics [1] and data mining [8].…”

Section: Motivating the Need For Uniform Scalingsupporting

confidence: 78%

Indexing Large Human-Motion Databases

Keogh

¹

,

Palpanas

²

,

Zordan

³

et al. 2004

Proceedings 2004 VLDB Conference

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Data-driven animation has become the industry standard for computer games and many animated movies and special effects. In particular, motion capture data recorded from live actors, is the most promising approach offered thus far for animating realistic human characters. However, the manipulation of such data for general use and re-use is not yet a solved problem. Many of the existing techniques dealing with editing motion rely on indexing for annotation, segmentation, and re-ordering of the data. Euclidean distance is inappropriate for solving these indexing problems because of the inherent variability found in human motion. The limitations of Euclidean distance stems from the fact that it is very sensitive to distortions in the time axis. A partial solution to this problem, Dynamic Time Warping (DTW), aligns the time axis before calculating the Euclidean distance. However, DTW can only address the problem of local scaling. As we demonstrate in this paper, global or uniform scaling is just as important in the indexing of human motion. We propose a novel technique to speed up similarity search under uniform scaling, based on bounding envelopes. Our technique is intuitive and simple to implement. We describe algorithms that make use of this technique, we perform an experimental analysis with real datasets, and we evaluate it in the context of a motion capture processing system. The results demonstrate the utility of our approach, and show that we can achieve orders of magnitude of speedup over the brute force approach, the only alternative solution currently available.

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“…Although we are particularly interested in motion capture data, as we noted above, our algorithm may have utility in domains as diverse as music retrieval [7] and space telemetry [8]. We will therefore perform all experiments on the following two datasets.…”

Section: Resultsmentioning

confidence: 99%

“…However, all previous work has focused on speeding up similarity search, when the scaling factor is known [8][17] [26]. The feature that differentiates our work from all the rest is that we allow a user to issue a single query, and find the best match at any scaling.…”

Section: Related Workmentioning

confidence: 99%

Indexing Large Human-Motion Databases

Keogh

¹

,

Palpanas

²

,

Zordan

³

et al. 2004

Proceedings 2004 VLDB Conference

View full text Add to dashboard Cite

Data-driven animation has become the industry standard for computer games and many animated movies and special effects. In particular, motion capture data recorded from live actors, is the most promising approach offered thus far for animating realistic human characters. However, the manipulation of such data for general use and re-use is not yet a solved problem. Many of the existing techniques dealing with editing motion rely on indexing for annotation, segmentation, and re-ordering of the data. Euclidean distance is inappropriate for solving these indexing problems because of the inherent variability found in human motion. The limitations of Euclidean distance stems from the fact that it is very sensitive to distortions in the time axis. A partial solution to this problem, Dynamic Time Warping (DTW), aligns the time axis before calculating the Euclidean distance. However, DTW can only address the problem of local scaling. As we demonstrate in this paper, global or uniform scaling is just as important in the indexing of human motion. We propose a novel technique to speed up similarity search under uniform scaling, based on bounding envelopes. Our technique is intuitive and simple to implement. We describe algorithms that make use of this technique, we perform an experimental analysis with real datasets, and we evaluate it in the context of a motion capture processing system. The results demonstrate the utility of our approach, and show that we can achieve orders of magnitude of speedup over the brute force approach, the only alternative solution currently available. KeywordsMotion Capture, Animation, Time Series, Indexing INTRODUCTIONData-driven animation has now become the industry standard for the production of computer games and many animated movies and special effects. The most promising and widely applied approach so far is the use of motion capture data. These are motion data recorded from live actors, which can subsequently be used for animating realistic human characters. Motion capture data, in its rawest form, is recorded with a few technologies, the most popular of which appears to be optical (see Vicon [38] and Motion Analysis [39] products) in which digital cameras record small reflective markers fixed to the human actor as he/she moves. Through multiple cameras and triangulation, three dimensional position traces for the markers are resolved faithfully. The markers can then be identified (as outer left knee, for example) and filtered. Motion capture allows the animation of a 3D model, where the data is mapped to the skeleton of the desired character and body orientations are determined ( Figure 1). In practical applications, most motion capture data is stored in segmented sequences in a motion library, for example a modern sports game may contain thousands of motion data "clips". The system, i.e. game engine in this case, selects and plays motions from the database [37]. Our approach aids in the creation and manipulation of such libraries by quickly finding instances of a given motion se...

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“…There exists a handful of techniques that can support similarity search under uniform scaling if the scaling factor is known in advance [3,9]; however, in most domains it is unlikely that we know the scaling factor. In such instances we must resort to multiple queries, one for each possible scaling factor.…”

Section: Introductionmentioning

confidence: 99%

Efficiently Finding Arbitrarily Scaled Patterns in Massive Time Series Databases

Keogh

¹

2003

Knowledge Discovery in Databases: PKDD 2003

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Abstract. The problem of efficiently finding patterns in massive time series databases has attracted great interest, and, at least for the Euclidean distance measure, may now be regarded as a solved problem. However in recent years there has been an increasing awareness that Euclidean distance is inappropriate for many real world applications. The limitations of Euclidean distance stems from the fact that it is very sensitive to distortions in the time axis. A partial solution to this problem, Dynamic Time Warping (DTW), aligns the time axis before calculating the Euclidean distance. However, DTW can only address the problem of local scaling. As we demonstrate in this work, uniform scaling may be just as important in many domains, including applications as diverse as bioinformatics, space telemetry monitoring and motion editing for computer animation. In this work, we demonstrate a novel technique to speed up similarity search under uniform scaling. As we will demonstrate, our technique is simple and intuitive, and can achieve a speedup of 2 to 3 orders of magnitude under realistic settings.

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An Efficient Hash-Based Algorithm for Sequence Data Searching

Cited by 14 publications

References 26 publications

Indexing Large Human-Motion Databases

Indexing Large Human-Motion Databases

Indexing Large Human-Motion Databases

Efficiently Finding Arbitrarily Scaled Patterns in Massive Time Series Databases

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