Motion Retrieval Based on Semantic Code and Dynamic Bayesian Network Inference

Abstract

A novel motion retrieval scheme is proposed. Based on semantic analysis and graph model, this scheme involves system learning in the first stage. In system learning, a Motion Semantic Dictionary (MSD) is derived by clustering. A Dynamic Bayesian Network (DBN) graph model is constructed based on the MSD and learning parameters. MSD and DBN are combined to derive motion information as features. Motion categories are recognized based on motion feature queries and matching. Experimental results are presented, showing the proposed method is more effective in execution time as compare to some existing representative algorithms.