Abstract:

Existing key frame extraction methods often fail to reveal the local topological structure of motion capture data.To this effect,we present a Laplacian Score (LS ) based feature selection approach to extract the key frames from the motion capture data.The proposed approach first extracts two kinds of representative and normalized feature vectors from the original motion capture data,and then employs LS algorithm to learn the scores and weights of the combined feature vectors.Accordingly,the discriminative feature subspace holding the promise of identifying the local motion information can be constructed.Subsequently,the initial key frame sequences can be obtained by the utilization of the comprehensive characteristic function and the extreme discrimination principle.With the constraints of the time threshold and discrimination strategies of similar poses,we further utilize the improved kmeans algorithm to cluster the candidate frames such that the final key frames can be obtained to remove the redundant ones. The experimental results show that the typical key frames extracted by the proposed approach have better visual summary of the whole motion capture data.

Key words: key frame extraction;motion capture data;local topological structure;Laplacian Score;feature selection