Fixed-size video summarization over streaming data via non-monotone submodular maximization

Video summarization which potentially fast browses a large amount of emerging video data as well as saves storage cost has attracted tremendous attentions in machine learning and information retrieval. Among existing efforts, determinantal point processes (DPPs) designed for selecting a subset of video frames to represent the whole video have shown great success in video summarization. However, existing methods have shown poor performance to generate fixed-size output summaries for video data, especially when video frames arrive in streaming manner. In this paper, we provide an efficient approach k-seqLS which summarizes streaming video data with a fixed-size k in vein of DPPs. Our k-seqLS approach can fully exploit the sequential nature of video frames by setting a time window and the frames outside the window have no influence on current video frame. Since the log-style of the DPP probability for each subset of frames is a non-monotone submodular function, local search as well as greedy techniques with cardinality constraints are adopted to make k-seqLS fixed-sized, efficient and with theoretical guarantee. Our experiments show that our proposed k-seqLS exhibits higher performance while maintaining practical running time.