Abstract
Along with the development of Motion Capture technique, more and more 3D motion database become available. In this paper, a novel method is presented for motion retrieval based on Ensemble HMM learning. First 3D temporal-spatial features and their keyspaces of each human joint are extracted for training data of Ensemble HMM learning. Then each action class is learned with one HMM. Since ensemble learning can effectively enhance supervised learners, ensembles of weak HMM learners are built. Experimental results show that our approaches are effective for motion data retrieval.
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Xiang, J. (2007). Motion Retrieval with Temporal-Spatial Features Based on Ensemble Learning. In: Huang, DS., Heutte, L., Loog, M. (eds) Advanced Intelligent Computing Theories and Applications. With Aspects of Artificial Intelligence. ICIC 2007. Lecture Notes in Computer Science(), vol 4682. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-74205-0_33
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DOI: https://doi.org/10.1007/978-3-540-74205-0_33
Publisher Name: Springer, Berlin, Heidelberg
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