In this paper

a motion retrieval and recognition system is investigated from a ensemble learning model. In order to recognize and retrieve 3D motion data

first motion features are extracted from motion data. Due to the high dimensionality of motion’s features

a generalized isomap nonlinear dimension reduction based on the estimation of underlying eigenfunction is used for training data of ensemble HMM learning. Then each action class is learned with one HMM. Since ensemble learning can effectively enhance supervised learning

ensembles of weak HMM learners are built. Experimental results show that our approaches are effective for information retrieval from large scale motion database.