Efficiently supporting ad hoc queries in large datasets of time sequences
SIGMOD '97 Proceedings of the 1997 ACM SIGMOD international conference on Management of data
Similarity Searching for Multi-Attribute Sequences
SSDBM '02 Proceedings of the 14th International Conference on Scientific and Statistical Database Management
Similarity Search for Multidimensional Data Sequences
ICDE '00 Proceedings of the 16th International Conference on Data Engineering
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
Indexing of variable length multi-attribute motion data
Proceedings of the 2nd ACM international workshop on Multimedia databases
A similarity measure for motion stream segmentation and recognition
MDM '05 Proceedings of the 6th international workshop on Multimedia data mining: mining integrated media and complex data
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Indexing of motion data is important for quickly searching similar motions for sign language recognition and gait analysis and rehabilitation. This paper proposes a simple and efficient tree structure for indexing motion data with dozens of attributes. Feature vectors are extracted for indexing by using singular value decomposition (SVD) properties of motion data matrices. By having similar motions with large variations indexed together, searching for similar motions of a query needs only one node traversal at each tree level, and only one feature needs to be considered at one tree level. Experiments show that the majority of irrelevant motions can be pruned while retrieving all similar motions, and one traversal of the indexing tree takes only several microseconds with the existence of motion variations.