Memorable and rich video summarization

Video summarization can facilitate rapid browsing and efficient video indexing in many applications. A good summary should maintain the semantic interestingness and diversity of the original video. While many previous methods extracted key frames based on low-level features, this study proposes Memorability-Entropy-based video summarization. The proposed method focuses on creating semantically interesting summaries based on image memorability. Further, image entropy is introduced to maintain the diversity of the summary. In the proposed framework, perceptual hashing-based mutual information (MI) is used for shot segmentation. Then, we use a large annotated image memorability dataset to fine-tune Hybrid-AlexNet. We predict the memorability score by using the fine-tuned deep network and calculate the entropy value of the images. The frame with the maximum memorability score and entropy value in each shot is selected to constitute the video summary. Finally, our method is evaluated on a benchmark dataset, which comes with five human-created summaries. When evaluating our method, we find it generates high-quality results, comparable to human-created summaries and conventional methods.