3D motion sequence retrieval based on data distribution

In this paper, we propose a novel 3D human motion sequence retrieval method based on the similarity of the motion data distribution. First, for each motion sequence in the database, the self-organizing maps (SOM) clustering algorithm is adopted to partition the frames into different classes to get the associated class reference vectors. Then given a query motion, probabilistic principal component analysis (PPCA) is applied to estimate the distribution of its data. We adopt two different approaches to model the query data. In the first one, we directly estimate the distribution of the original data. For the other one, we estimate the class reference vectorpsilas distribution after training by SOM, instead of that of the original data. Both of these approaches model the data using a Gaussian distribution. Finally the similarity between the query example and the motion sequence in a database is measured using the Mahalanobis distance. Experimental results on the CMU database demonstrate that the proposed method achieves good performance.