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Original 3D motion sequences lie in high dimensional subspace and on a high-dimensional manifold which is highly contorted, so it is difficult to cluster the similar poses together to form distinct movements. Here we use a non-linear learning dimensionality reduction technique (ISOMAP) based on radius bias function (RBF) generalized to map original motion sequences into low dimensional subspace. Experimental...
Along with the development of motion capture technique, more and more large-scale 3D motion databases become available. In this paper, a novel approach is presented for motion retrieval based on a novel index method. Due to high dimensionality of Motion's features, the dimension reduction is used. Then an index system is built based on s the low-dimensional subspace tree. So we can reduce the number...
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