Efficient Query Processing in 3D Motion Capture Gesture Databases
Efficient Query Processing in 3D Motion Capture Gesture Databases
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DOI：
10.1142/s1793351x16400018
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发表时间：
2016-03-01
影响因子：
0.8
通讯作者：
Seidl, Thomas
中科院分区：
文献类型：
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作者：
Beecks, Christian;Hassani, Marwan;Seidl, Thomas
在3D动作捕捉数据库中访问手势模式时，最基本的挑战之一是时空相似性的定义。虽然基于距离的相似模型（如手势签名的手势匹配距离）能够利用手势模式的空间和时间特征，但由于其高计算复杂性，它们对大型3D动作捕捉数据库的适用性受到限制。为此，我们提出了手势匹配距离的下界近似值，该近似值可用于最优的多步查询处理架构，以支持高效的查询处理。我们研究了基于3D运动捕捉数据库的精度和效率方面的性能，并表明我们的方法能够在精度损失可以忽略不计的情况下实现超过一个数量级的效率提高。此外，我们还讨论了相似搜索方法在数字人文学科中的不同应用，以突出相似搜索方法在手势模式分析研究领域中的重要意义。
One of the most fundamental challenges when accessing gestural patterns in 3D motion capture databases is the definition of spatiotemporal similarity. While distance-based similarity models such as the Gesture Matching Distance on gesture signatures are able to leverage the spatial and temporal characteristics of gestural patterns, their applicability to large 3D motion capture databases is limited due to their high computational complexity. To this end, we present a lower bound approximation of the Gesture Matching Distance that can be utilized in an optimal multi-step query processing architecture in order to support efficient query processing. We investigate the performance in terms of accuracy and efficiency based on 3D motion capture databases and show that our approach is able to achieve an increase in efficiency of more than one order of magnitude with a negligible loss in accuracy. In addition, we discuss different applications in the digital humanities in order to highlight the significance of similarity search approaches in the research field of gestural pattern analysis.