Diversity Promoting Online Sampling for Streaming Video Summarization
This work addresses the need for memory-efficient and fast summarization in streaming video applications, offering an incremental improvement over existing methods.
The paper tackles the problem of streaming video summarization by proposing an online algorithm that uses competitive learning for diverse sampling, which performs better than batch mode summarization on 50 videos in the VSUMM dataset while requiring significantly lower memory and computational resources.
Many applications benefit from sampling algorithms where a small number of well chosen samples are used to generalize different properties of a large dataset. In this paper, we use diverse sampling for streaming video summarization. Several emerging applications support streaming video, but existing summarization algorithms need access to the entire video which requires a lot of memory and computational power. We propose a memory efficient and computationally fast, online algorithm that uses competitive learning for diverse sampling. Our algorithm is a generalization of online K-means such that the cost function reduces clustering error, while also ensuring a diverse set of samples. The diversity is measured as the volume of a convex hull around the samples. Finally, the performance of the proposed algorithm is measured against human users for 50 videos in the VSUMM dataset. The algorithm performs better than batch mode summarization, while requiring significantly lower memory and computational requirements.