Adaptive reference frame selection for near-duplicate video shot detection
Near-duplicate video shots provide critical visual link between videos and detecting such video shots efficiently and effectively is of paramount importance in many applications such as detecting copyright infringement. In this paper, we propose an improved near-duplicate video shot detection approach by adaptively selecting reference frames for more effective shot representation. The correlation between adjacent frames is measured with Pearson's Correlation Coefficient (PCC) so that a set of compact yet representative reference frames can be selected adaptively in terms of content variation within video shots. Interest points are further extracted from the selected frames to effectively represent shot contents for similarity matching. Comprehensive experimental results on TRECVID-2008 corpus demonstrate that our proposed approach outperforms the state-of-the-art method effectively.