Searching of motion database based on hierarchical SOM

A novel approach for 3D motion capture data retrieval based on the Hierarchical Self Organizing Map (HSOM) is proposed. Given a query motion sequence, our goal is to search for all the similar motions from a database. Specifically, a feature vector based on the distribution of the human motion data is first extracted from each motion sequence in the database. Then, Singular Value Decomposition (SVD) is applied to reduce the dimensionality of the feature vector. To improve the retrieval efficiency, a two-level indexing scheme based on the HSOM is constructed, in which the motion sequences are first partitioned with the reduced feature vectors at the top level and then at the lower level, the original feature vectors are adopted to classify the cluster associated with a parent node at the first level into sub-clusters. Finally, fuzzy search is implemented to traverse the index structure to search for similar motions. Experimental results show that our method can achieve good performance in terms of retrieval accuracy and efficiency.