Motion retrieval with temporal-spatial features based on ensemble learning

Along with the development of Motion Capture technique, more and more 3D motion database become available. In this paper, a novel method is presented for motion retrieval based on Ensemble HMM leaming. First 3D temporal-spatial features and their keyspaces of each human joint are extracted for training data of Ensemble HMM learning. Then each action class is learned with one HMM. Since ensemble leaming can effectively enhance supervised learners, ensembles of weak HMM learners are built. Experimental results show that our approaches are effective for motion data retrieval.