To reduce reconstruction errors during keyframe extraction and to control the optimal compression ratio, this study proposes a method for keyframe extraction from human motion capture data based on a multiple population genetic algorithm. The fitness function is defined to meet the goals of minimal reconstruction errors and the optimal compression rate, where multiple initial populations are su...

This paper proposes a keyframe extraction method based on a novel layered curve simplification algorithm for motion capture data. Bone angles are employed as motion features and keyframe candidates can be selected based on them. After that, the layered curve simplification algorithm will be used to refine those candidates and the keyframe collection can be gained. To meet different requirements...

Keyframe extraction is a widely applied remedy for issues faced with 3D motion capture -based computer animation. In this paper, we propose novel keyframe method, where the represented in linear rotation invariant coordinates and dimensions covering 95% of data are automatically selected using principal component analysis. Then, by K-means classification, summarized clustered extracted from eac...

The insufficiency of labeled training data for representing the distribution of the entire dataset is a major obstacle in automatic semantic annotation of large-scale video database the objective is to represent the most “important” or “meaningful” scenes of the large amount of visual information by only a few images:the key frames. First, the image sequences are temporally segmented into conti...

This research extracts keyframes from Motion capture data. Accurate selection of keyframes is a key point in motion synthesizing, editing, retargeting and motion data compression. Our method firstly represents every joint curve with a simple numeric sequence based on wavelet transformation. Then the correlation between curves is calculated based on these numeric sequences. Several curves which ...