  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.
