Nonparametric Motion Feature for Key Frame Extraction in Sports Video

Key frames extraction play an important role in video abstraction. Traditional key frame extraction methods only use color, texture, or shape features to represent a frame, while the motion feature is ignored or inappropriately modeled. Since the motion feature contains a lot of semantic information in video analysis, we propose a compact representation of the dominant motion information for each frame, based on a mean shift analysis procedure. Then, an EMD (Earth mover's distance) is employed as a similarity metric for the represented motion feature. Moreover, we propose a novel temporal k-means clustering algorithm for the key frame extraction, which naturally incorporates the sequential constraint into extracted key frames. Experimental results demonstrate the effectiveness of our approach.