Query-Focused Extractive Video Summarization
This addresses the need for intelligent algorithms to handle big video data, particularly for search engines to display video snippets, but it appears incremental as it builds on existing summarization methods.
The authors tackled query-focused extractive video summarization by developing a probabilistic model (SH-DPP) that selects key shots based on query relevance and video importance, achieving verification on two densely annotated datasets.
Video data is explosively growing. As a result of the "big video data", intelligent algorithms for automatic video summarization have re-emerged as a pressing need. We develop a probabilistic model, Sequential and Hierarchical Determinantal Point Process (SH-DPP), for query-focused extractive video summarization. Given a user query and a long video sequence, our algorithm returns a summary by selecting key shots from the video. The decision to include a shot in the summary depends on the shot's relevance to the user query and importance in the context of the video, jointly. We verify our approach on two densely annotated video datasets. The query-focused video summarization is particularly useful for search engines, e.g., to display snippets of videos.