Summarizing Video using Non-Negative Similarity Matrix Factorization

Abstract

We present a novel approach to automatically ex-tracting
summary excerpts from audio and video. Our approach is to maximize the average similarity between the excerpt and the
source. We first calculate a similarity matrix by comparing each pair of time samples using a quantitative similarity measure. To determine the segment with highest average similarity, we maximize the summation of the self-similarity matrix over the support of the segment. To select multiple excerpts while avoiding redundancy, we compute the non-negative matrix factorization (NMF) of the similarity matrix into its essential structural components. We then build a summary comprised of excerpts from the main components, selecting the excerpts for maximum average similarity within each component. Variations integrating segmentation and other information are also discussed, and experimental results are presented.