Keyframe compression and decompression for time series data based on the continuous hidden Markov model

Memory of motion patterns as data, comparison of a new motion pattern with data, and playback of one from the data are inevitably involved in the information processing of intelligent robot systems. Such computation forms the computational foundation of learning, acquisition, recognition, and generation process of intelligent robotic systems. In this paper, we propose to apply the continuous hidden Markov model to establish the computational foundation, using which one obtains the specified number of keyframes and their probability distributions. The keyframes are optimally selected to maximize the likelihood. The probability distributions are to be used to compute comparison and playback. The proposed method is applied to the motion data of a humanoid robot as well as the time series image data, and its validity is to be discussed.