Indexing and retrieval of human motion data by a hierarchical tree
Proceedings of the 16th ACM Symposium on Virtual Reality Software and Technology
3D human motion retrieval based on ISOMAP dimension reduction
AICI'11 Proceedings of the Third international conference on Artificial intelligence and computational intelligence - Volume Part III
Human motion retrieval using topic model
Computer Animation and Virtual Worlds
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Widely used in data-driven computer animation, motion capture data exhibits its complexity both spatially and temporally. The indexing and retrieval of motion data is a hard task that is not totally solved. In this paper, we present an efficient motion data indexing and retrieval method based on self-organizing map and Smith–Waterman string similarity metric. Existing motion clips are first used to train a self-organizing map and then indexed by the nodes of the map to get the motion strings. The Smith–Waterman algorithm, a local similarity measure method for string comparison, is used in clustering the motion strings. Then the motion motif of each cluster is extracted for the retrieval of example-based query. As an unsupervised learning approach, our method can cluster motion clips automatically without needing to know their motion types. Experiment results on a dataset of various kinds of motion show that the proposed method not only clusters the motion data accurately but also retrieves appropriate motion data efficiently.