Creating video summarization from emotion perspective
This paper proposes a novel approach of summarization to non-professional edited videos containing a lot of redundant information, from the viewpoint of emotion. Ground-truth emotion scores of each frame are firstly obtained from our dataset annotated by humans. Then, we extract emotional features of each frame from training set videos. After that, we train our predictive model from the feature vectors and emotion scores by using linear regression. Meanwhile, videos are partitioned into several segments. We select a subset of segments in the video whose length is below a specific value by optimizing the sum of their emotion scores. This subset of segments can be treated as the emotional video summarization desired. The experimental results show that the proposed scheme can achieve an effective emotional video summarization.