R-trees: a dynamic index structure for spatial searching
SIGMOD '84 Proceedings of the 1984 ACM SIGMOD international conference on Management of data
M-tree: An Efficient Access Method for Similarity Search in Metric Spaces
VLDB '97 Proceedings of the 23rd International Conference on Very Large Data Bases
Similarity Search in High Dimensions via Hashing
VLDB '99 Proceedings of the 25th International Conference on Very Large Data Bases
Warping indexes with envelope transforms for query by humming
Proceedings of the 2003 ACM SIGMOD international conference on Management of data
Locality-sensitive hashing scheme based on p-stable distributions
SCG '04 Proceedings of the twentieth annual symposium on Computational geometry
Recognizing Human Actions: A Local SVM Approach
ICPR '04 Proceedings of the Pattern Recognition, 17th International Conference on (ICPR'04) Volume 3 - Volume 03
Exact indexing of dynamic time warping
Knowledge and Information Systems
Scaling and time warping in time series querying
VLDB '05 Proceedings of the 31st international conference on Very large data bases
Indexing large human-motion databases
VLDB '04 Proceedings of the Thirtieth international conference on Very large data bases - Volume 30
Trajectory-based visual analysis of large financial time series data
ACM SIGKDD Explorations Newsletter - Special issue on visual analytics
The TS-tree: efficient time series search and retrieval
EDBT '08 Proceedings of the 11th international conference on Extending database technology: Advances in database technology
iSAX: indexing and mining terabyte sized time series
Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining
On the Discrimination of Speech/Music Using a Time Series Regularity
ISM '08 Proceedings of the 2008 Tenth IEEE International Symposium on Multimedia
Discovery of time series in video data through distribution of spatiotemporal gradients
Proceedings of the 2009 ACM symposium on Applied Computing
Scalable similarity search of timeseries with variable dimensionality
Proceedings of the 20th ACM international conference on Information and knowledge management
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This paper introduces a novel hashing algorithm for large timeseries databases, which can improve the querying of human motion. Timeseries that represent human motion come from many sources, in particular, videos and motion capture systems. Motion-related timeseries have features which are not commonly present in traditional types of vector data and that create additional indexing challenges: high and variable dimensionality, no Euclidean distance without normalization, and a metric space not fully defined. New techniques are needed to index motion-related timeseries. The algorithm that we present in this paper generalizes the dot product operator to hash timeseries of variable dimensionality without assuming constant dimensionality or requiring dimensionality normalization, unlike other approaches. By avoiding normalization, our hashing algorithm preserves more timeseries information and improves retrieval accuracy, and by hashing achieves sublinear computation time for most searches. Additionally, we show how to further improve the hashing by partitioning the search space using timeseries within the index. This paper also reports the results of experiments that show that the algorithm performs well in the querying of real human motion datasets.