# Diversity Promoting Online Sampling for Streaming Video Summarization

arXiv: https://arxiv.org/abs/1610.09582 · HTML: https://mlsanity.com/paper/1610.09582/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 29 Oct 2016.

## Built on by (most cited first, 2 of 2)

- [AudioVisual Video Summarization](https://mlsanity.com/paper/2105.07667/) (2021)
- [Fast Graph Sampling for Short Video Summarization using Gershgorin Disc Alignment](https://mlsanity.com/paper/2110.11420/) (2021)

## Abstract (arXiv)

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.