Fast subsequence matching in time-series databases
SIGMOD '94 Proceedings of the 1994 ACM SIGMOD international conference on Management of data
Locally adaptive dimensionality reduction for indexing large time series databases
ACM Transactions on Database Systems (TODS)
R-trees: a dynamic index structure for spatial searching
SIGMOD '84 Proceedings of the 1984 ACM SIGMOD international conference on Management of data
Efficient Similarity Search In Sequence Databases
FODO '93 Proceedings of the 4th International Conference on Foundations of Data Organization and Algorithms
Efficient Time Series Matching by Wavelets
ICDE '99 Proceedings of the 15th International Conference on Data Engineering
Motion synthesis from annotations
ACM SIGGRAPH 2003 Papers
Indexing multi-dimensional time-series with support for multiple distance measures
Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
Segmenting motion capture data into distinct behaviors
GI '04 Proceedings of the 2004 Graphics Interface Conference
Automated extraction and parameterization of motions in large data sets
ACM SIGGRAPH 2004 Papers
Efficient content-based retrieval of motion capture data
ACM SIGGRAPH 2005 Papers
Information Retrieval for Music and Motion
Information Retrieval for Music and Motion
Scaling and time warping in time series querying
The VLDB Journal — The International Journal on Very Large Data Bases
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Motion indexing concerns efficient ways to identify and retrieve motions similar to a query motion from a large set of motions stored in a human motion database. In this paper, we perform the first quantitative evaluation and comparison of motion indexing techniques. We extend PCA-based algorithms for motion segmentation to address the motion indexing problem and perform a survey of the most significant motion indexing techniques in the literature. We implement five different techniques for motion indexing: two principal component analysis (PCA) based methods, a feature-based method, and two dynamic time warping (DTW) based methods. The indexing accuracy is evaluated for all techniques and a quantitative comparison among them is achieved. The two PCA-based techniques have the lowest number of false negatives but, at the same time, they have a large number of false positives (close to 90%). The feature-based and DTW quaternion-based techniques perform better than the PCA-based techniques. While the DTW-3D technique has a small number of false positives, the false negatives are also very few. The Dynamic Time Warping 3D-based technique performed best among all techniques when compared by false positives and false negatives metrics.