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As commercial motion capture systems are widely used , more and more 3D motion database become available. In this paper, we presented a motion retrieval system based on ensemble HMM learning. First, 3D features are extracted. Due to high dimensionality of motion's features, then non-linear PCA and radial basis function (RBF) neural network for dimensionality reduction are used. At last each action...
In this paper, a novel approach is presented for motion retrieval based on double-reference inde(DRI) in motion capture(mocap) database. Due to high dimensionality of motion's feature, first 3D temporal-spatial features and their keyspaces of each human joint are extracted. Then DRI is build based on selecting a small set of representative motion clips in the database by these features. So we can...
In this paper, a motion retrieval system is investigated from a multiple-instance learning view. In order to retrieve similar motion data, each human joint's motion clip is regarded as a bag, while each of its segments is regarded as an instance. First 3D temporal-spatial features and their keyspaces of each human joint are extracted. Then data driven decision trees based on ensemble multiple-instance...
Along with the development of motion capture technique, more and more 3D motion libraries become available. In this paper, a novel approach is presented for motion retrieval based on data-driven decision tree with 3D temporal-spatial features. First 3D temporal-spatial features of each human joint are extracted with the help of keyspace. Since the gotten features of each joint are independent, data-driven...
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