  Video summarization methods are usually classified into shot-level or
frame-level methods, which are individually used in a general way. This paper
investigates the underlying complementarity between the frame-level and
shot-level methods, and a stacking ensemble approach is proposed for supervised
video summarization. Firstly, we build up a stacking model to predict both the
key frame probabilities and the temporal interest segments simultaneously. The
two components are then combined via soft decision fusion to obtain the final
scores of each frame in the video. A joint loss function is proposed for the
model training. The ablation experimental results show that the proposed method
outperforms both the two corresponding individual method. Furthermore,
extensive experimental results on two benchmark datasets shows its superior
performance in comparison with the state-of-the-art methods.
